Telmai’s new Data Reliability Agents deliver AI-ready data at the lake, validated in real-time, with autonomous detection, resolution, and natural language interfaces for agentic workflows.
SAN FRANCISCO, Oct. 7, 2025 /PRNewswire/ — Telmai, the AI-powered data observability platform, today announced its Agentic offerings to make enterprise data truly Autonomous-Ready. These new capabilities ensure agentic AI workflows can communicate, decide, and execute actions on real-time trusted data with minimal human oversight.
Agentic AI significantly changes the requirements for how organizations manage their data and thus their data quality (DQ). Because Agentic AI requires low-latency and real-time access to validated data, it’s imperative that data quality happens right at the source, not downstream, where most companies focus their DQ efforts today.
But validation alone isn’t enough. AI agents also need to understand whether data is truly fit for purpose in the context of their actions. This involves delivering contextual information about data health as metadata into catalogs and semantic layers that AI agents can access.
Only when trust and context are combined can AI agents operate responsibly and enterprises deploy them with real confidence.
Telmai has the unique ability to continuously validate, monitor, and enrich data with quality signals at the lake and can push that data quality metadata for consumption by agents. This creates the trusted foundation that autonomous AI products need to operate reliably and at scale.
With Telmai’s latest product launch, AI agents can continuously access reliable data and the critical data quality context needed to automate downstream workflows.
Real-Time, Continuous, Agentic AI-Ready Data
At the core of this update is the introduction of Telmai’s MCP-compliant server, which enables LLM-powered agents like Claude, Bedrock, or Vertex to query Telmai directly. Telmai continuously validates data, whether structured, semi-structured, or unstructured. Additionally, it generates comprehensive data quality metadata alongside the validated data, providing essential context on data health to ensure the data is reliable and AI-ready. Through the MCP layer, AI agents can access and retrieve validated data and metadata into their agentic workflows, eliminating the need for third-party transformations or complex workarounds.
“In the era of model commoditization, true competitive advantage will emerge from trustworthy, dynamic, and contextually aware data,” said Sanjeev Mohan, industry analyst and principal at SanjMo. “Telmai’s latest release is a big step in this process. It offers continuous validation and contextual metadata that enable AI agents to act responsibly, while reducing the operational debt that has long hindered enterprise adoption.”
Natural Language AI Assistants & Decentralized Data Trust
Building on this foundation, Telmai is introducing a suite of AI assistants called Data Reliability Agents accessible through natural language interfaces, enabling both technical and non-technical users to interact directly with the platform. This decentralization means that ownership of data reliability no longer sits solely with engineering, accelerating time to value by making platform management and critical data quality insights accessible and actionable to all relevant stakeholders.
Autonomous Detection and Remediation
Telmai’s Data Reliability Agents enable autonomous detection and resolution of data anomalies. These intelligent agents continuously monitor data pipelines for irregularities and provide clear, plain-language explanations of root causes. Identifying and resolving complex data quality issues that once required deep technical expertise are now easily understood and addressed by both technical and business teams. Beyond detection, the Data Reliability Agents provide actionable recommendations and assist in generating data quality rules tailored to newly identified anomalies.
Furthermore, these Data Reliability Agents augment existing automated workflows, such as ticket creation and alert triggers, to help data teams proactively adapt and drive continuous improvement in their data quality processes.
This comprehensive approach closes the loop from detection through triage and remediation, ensuring that data being fed into the downstream processes is not only trustworthy but consistently ready for autonomous consumption and decision-making.
“As AI agents take the reins of decision-making, we believe autonomy should never come at the cost of reliability,” said Mona Rakibe, Co-founder & CEO of Telmai. “With these updates, Telmai is laying the groundwork for true intelligent automation and allowing enterprise data teams to shift their focus to driving measurable business value via Agentic AI.”
About Telmai
Telmai is a data observability platform company that enables enterprise data owners to monitor and detect real-time data issues. The platform leverages AI to monitor all data passing through the data pipeline before entering the data warehouse, protecting downstream systems and analytics used for decision-making. Telmai’s open architecture supports anomaly detection closest to data sources and works over complex data types with native support for nested and multi-valued attributes.
AI pilots fail not because models don’t work, but because data systems lack reliability and context. To scale AI responsibly, enterprises need validated data at the source and metadata enriched with health and governance signals. This article shows how Telmai and Atlan close this gap. Telmai validates data as it lands, while Atlan’s Metadata Lakehouse adds lineage and governance to create the trusted foundation for scaling AI.
With a surge in AI investment, enterprise leaders are under mounting pressure to deliver reliable, scalable AI solutions that create measurable business impact. A recent MIT study found that 95 percent of generative AI projects failed to produce measurable outcomes, as many organizations struggle to move beyond experimentation and into reliable execution. AI pilots are failing to deliver, not because the models don’t work, but because the underlying foundational data systems upon which they are built lack reliability and context.
Autonomous systems and AI agents act on data in microseconds, so there’s no time for late-stage downstream fixes where most companies focus their data quality efforts today. To power AI-native ecosystems at scale, organizations must build trust at the source as data is ingested and ensure that data quality metadata is pushed to data catalogs and metadata systems, allowing agents to evaluate fitness before consumption. This creates the trusted foundation that Autonomous AI products need to operate reliably and at scale.
By combining Telmai’s AI-first data quality platform with Atlan’s AI-native metadata and governance platform through Atlan’s App Framework, enterprises gain a seamless way to detect, resolve, and govern data issues directly within the tools their teams already use.
In this article, let’s dive deeper into how they together create a single fabric of trust + context that allows enterprises to move beyond pilots and scale AI responsibly.
Why is real-time validation at ingestion critical for reliable AI?
The failure point for most AI initiatives isn’t in the model, but rather it’s in the underlying data pipeline feeding it. Business Intelligence (BI) is inherently deterministic and descriptive, working with structured historical data to explain what happened through predefined reports and dashboards. AI, in contrast, is non-deterministic and predictive. It consumes both structured and unstructured data to learn patterns, forecast outcomes, and make autonomous decisions.
The old adage “garbage in, garbage out” takes on far higher stakes here. AI and LLMs always identify patterns from the inputs they receive and derive insights without context or judgment. If those inputs are incomplete, drifting, or biased, the model confidently reproduces those flaws at scale.
Traditional data quality approaches were designed for a reporting world, where errors could be corrected after a dashboard broke or a KPI looked suspicious. AI-native workloads break this model entirely. AI and autonomous systems operate at machine speed, where thousands of micro-decisions are made every second. Waiting until the BI or monitoring layer to enforce quality is simply too late, as the damage has already propagated through to downstream business-critical applications.
That’s why ingestion-layer validation has become non-negotiable. Data quality must be ensured before Agentic workflows can access or read the data, not after the data lands in the access layer, at the data lake, or before it enters the lake through event streams. Reliability must be enforced as data is ingested into the lake, especially in open formats like Apache Iceberg and Delta Lake, where it is profiled and validated before being published to production tables.
This is exactly where Telmai comes in. Purpose-built for AI-first architectures, Telmai continuously monitors and validates data through your data pipeline, irrespective of volume or velocity. Telmai’s ML-driven and rule-based checks automatically detect anomalies, schema changes, and data drift before they impact production. Further, Telmai can publish data health KPIs into data catalogs and metadata systems like Atlan, enriching lineage and governance with real-time data quality context.
Agentic AI systems don’t have the luxury of waiting for late-stage fixes. They act in microseconds. That means trust must be built into data at ingestion, and that trust must travel with context across the enterprise, “ said Mona Rakibe, Co-Founder and CEO of Telmai. “With the App Framework, Telmai and Atlan will give teams a trusted data layer ready to power applications and integrations that let AI move beyond pilots and deliver at scale.”
How Atlan extends this trust with context
Trust in data is only half the story. For enterprises to scale AI responsibly, trust must travel with context so every consumer, whether human, system, or AI agent, knows what the data means, where it came from, and how it can be used.
As part of Atlan’s new App Framework, Telmai is now integrated directly into Atlan’s Metadata Lakehouse. For the first time, enterprises can unify monitoring within the same foundation that powers column-level lineage, business-ready data products, AI governance, and policy & compliance monitoring.
With this integration, customers using Telmai and Atlan can:
Unify trust and context in the Metadata Lakehouse – Telmai’s data quality signals, such as freshness, anomaly detection, schema changes, and volume drift, are automatically surfaced inside Atlan, enhancing lineage and metadata with actionable insights that empower data consumers and AI agents alike.
Enable true interoperability for agentic AI – For agentic systems to truly scale, interoperability is critical. The tools and services that agents depend on, whether for validation, access, enrichment, or downstream action, must be accessible through a common layer. Atlan delivers this through its open Metadata Lakehouse by providing consistent, versioned context across raw ingestion data and curated data products, ensuring data fitness can be evaluated at every step.
Enforce policy and compliance at scale – With Telmai’s data quality metadata embedded in the context layer, data trust signals can flow downstream via column-level lineage and bidirectional tag management to other platforms like Databricks, Snowflake, or data access systems. When Data Quality issues are encountered, they can trigger automated governance workflows, ensuring policy compliance and reducing risk across autonomous AI pipelines.
Enterprises can’t scale AI responsibly without a foundation of trust and context. Telmai brings real-time, ingestion-level validation, and Atlan serves as the context layer, ensuring that trust travels with context across every system, workflow, and AI agent.” said Marc Seifer, Head of Global Alliances at Atlan. “Together, Telmai and Atlan are enabling organizations to move beyond pilots and build AI systems that operate reliably, responsibly, and at scale.”
Get AI-Ready—Now
For enterprises to successfully transition AI pilots into production, they need real-time, low-latency access to validated data, along with metadata that carries context about its health, lineage, and governance. Without this foundation, AI agents operate blindly, lacking visibility into whether the data they consume is fit for use, where it originated, or whether they are authorized to access it.
Telmai and Atlan close this gap. Telmai continuously monitors and validates data in open table formats, such as Apache Iceberg and more, as it lands in the lake layer, detecting anomalies and data quality issues before they propagate downstream. It then generates rich observability metadata, which flows into Atlan’s Metadata Lakehouse. There, these signals are combined with lineage, policies, and business glossaries, providing a complete picture of data health and context for both humans and AI agents.
By bringing Telmai’s data quality signals into Atlan’s Metadata Lakehouse, enterprises can now drive measurable impact on their AI implementation with reliability and context fabric that enables AI to scale responsibly.
Want to learn how Telmai and Atlan can work together to scale your existing data infrastructure to be AI-ready? Click here to connect with our team for a personalized demo.
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GreenShift, an Amsterdam-based AI powered technology company revolutionizing sustainable cloud computing, has raised €2.35 million in a Convertible Loan Agreement (CLA) round. The investment was led by 4impact capital and The Footprint Firm with continued support from Rockstart.
The fresh capital will enable team growth and support GreenShift’s efforts to deepen enterprise partnerships, advancing its mission to become the operating system for sustainable cloud computing.
Tackling the urgent challenge of cloud emissions
The global cloud market is valued at over €900 billion and growing at a 21% CAGR. With cloud computing already generating more CO₂ emissions than the aviation industry, data centers are projected to account for up to 2.5 billion tons of CO₂ by 2030, nearly 40% of U.S. annual emissions. Today, cloud computing is estimated to account for roughly 2.5–3.7% of global greenhouse gas emissions. A significant share of this footprint is amplified by inefficiencies, such as idle provisioning, complex application deployment architectures that prevent easy cost and emissions attribution, and under-optimised application logic that reduce overall energy use effectiveness. This represents both a massive sustainability challenge and a strategic opportunity: without intervention, cloud and AI workloads will continue to drive unsustainable energy demand.
GreenShift’s solution: Full-stack visibility, real-time optimisation and carbon aware automation
Unlike existing solutions, many of which focus on infrastructure-level optimisation, GreenShift operates at the application layer, where most inefficiencies originate. GreenShift’s AI-powered platform reduces cloud application energy usage by up to 40%. By unifying cost, performance, and carbon into a single optimization engine, it enables organizations to lower emissions and significantly cut cloud costs while driving performance. Unlike traditional optimization initiatives that force a trade-off between cost and performance, GreenShift removes that compromise as well. The result is a step-change in cloud sustainability: lower costs, higher performance, and significantly reduced environmental impact.
Omar Regoort and Leonid Borodaev, Co-CEOs of GreenShift, stated:
“Our vision is to lead the global shift to sustainable cloud computing. By building the operating system for sustainable cloud, we ensure digital growth is efficient, high-performing, and green by default. With 4impact, The Footprint Firm, and Rockstart as partners, we’re in a strong position to accelerate adoption and scale our impact globally.”
Ali Najafbagy, Founding Partner at 4impact capital, said:
“The rapid increase in cloud computing leads to vast inefficiencies, high energy consumption and high emissions. GreenShift tackles this head-on with a scalable, software-first solution that reduces energy use while enhancing performance. Omar, Leonid, and their team are uniquely positioned to build the global category leader in sustainable cloud computing, and we are proud to lead this round.”
Sofie Käll, CIO at The Footprint Firm, commented:
“We are excited to back GreenShift in addressing the critical and growing issue of costly, and emissions-heavy computing. Their solution delivers a powerful alignment of operational, economic and environmental value for Enterprises – exactly what’s needed to make sustainable technology truly scale. We’re betting on a future where AI serves, rather than strains, the planet, and we truly believe Omar and Leonid of Greenshift are the ones to help realize that vision”
Gem Kua, Investment Manager at Rockstart, added:
“Backing Omar and Leonid in GreenShift’s stealth phase was about more than investing in technology – it was about backing founders who could tackle the AI era’s compute and emissions challenge. Their early deployments have already demonstrated strong results across sustainability, cost, and performance metrics, and we’re proud to back them again as they are on track to lead the sustainable cloud computing category.”
The GreenShift investment benefits from support from the European Union under the InvestEU Fund.
Today, we’re excited to announce that our SOSV portfolio company and HAX startup development program graduate Hyperlume, a pioneer in MicroLED-based optical interconnect technology, has been acquired by Credo (NASDAQ: CRDO).
This marks a major step forward in delivering secure, high-speed connectivity solutions that are more reliable, energy efficient, and ready for the AI era.
Impact on the AI World
The explosion of AI and hyperscale data center workloads is pushing existing connectivity infrastructure to its limits. Traditional electronic interconnects face serious energy and bandwidth bottlenecks that slow scaling and increase costs.
GPU performance has improved ~40,000x since 2000. But the networks that connect those chips have only improved ~80x, creating a significant bottleneck. Today, most GPUs still transfer data via copper cables, an antiquated method that’s cheap but highly inefficient (it generates excessive heat and wastes energy). Laser-based optical cables exist, but they’re 5x more expensive than copper and don’t scale well within tightly packed racks where 40% of the interconnects live.
To keep pace with the exponential growth of data, the world needs a new class of connectivity.
That’s where Hyperlume’s breakthrough MicroLED technology comes in. By using high bandwidth, low latency, low power interconnects for AI data centers and high-performance computing systems, Hyperlume has created a new way to overcome the energy and bandwidth bottlenecks inherent in traditional electronic interconnects.
With this acquisition, Credo now offers one of the most comprehensive connectivity platforms in the industry and strengthens their ability to help customers scale massive AI networks more efficiently and sustainably.
Huge congratulations to Hyperlume co-founders Mohsen Asad (CEO) and Hossein Fariborzi (CTO). When they first joined HAX in 2023, as part of our ever-expanding thesis into next-gen compute and data centres power, their mix of deep technical expertise and conviction is why we invested early.
Read more in Credo’s press release announcing the acquisition here.
Scale is excited to announce our investment in Inspiren’s $100M Series B!
By 2030, 1 in 5 Americans will be aged 65 or older, and without technology senior living communities won’t be able to safely keep up with demand. Inspiren is creating a safer future for older adults by helping senior living communities deliver safer, higher quality care through unifying resident safety, care planning, staffing, and emergency response into a single, AI-powered platform.
Today, senior living care teams are flying blind on critical details of patient care and rely on inaccurate care plans, midnight rounding, and some of the worst software you’ve ever seen to deliver care to incredibly vulnerable patient populations. Inspiren replaces guesswork with data by combining hardware and software to give care teams a real-time view of resident behavior they can use for both tactical care delivery and long-term planning. The end result? Safe residents, happy staff members, and financially prosperous senior living communities.
Inspiren is a case study in category creation and how impeccable execution can shift industry sentiment in a matter of months. We were first introduced to the business last fall and were immediately impressed with Alex and the value Inspiren was delivering to its early adopters. However, industry sentiment at large was mixed. Theoretical ROI was easy to grok, but operators burned by past generations of technology over-promising and under-delivering had concerns about product quality and resident privacy. Fast forward eight months and industry sentiment had totally flipped: Inspiren is the obviously better way to run a senior living community.
So what changed and convinced operators that Inspiren should be the new standard of care? At the core of Inspiren’s success is privacy-first design driven by tight coupling between hardware and software. When a community adopts Inspiren, sensors are installed in resident’s rooms that monitor activity and alert nurses when residents might be in trouble. To be clear, these aren’t security cameras: they’re beautiful, unobtrusive devices that leverage audio, visual, and radar inputs to create a privacy-preserving digital twin of every resident. Care teams get exactly the right amount of information to help residents when they need it and residents don’t feel like they’re living under a surveillance state.
Informed care teams are effective care teams and Inspiren’s data helps create safer communities. This is particularly important when resident needs change rapidly. When residents join a community, there’s an initial medical evaluation that determines a care plan based on their needs. However, resident health can change meaningfully in a matter of weeks. In the current state, care teams will recognize that and start ad-hoc delivering more care, but without a formal re-evaluation the care plan won’t be rigorous, and the community won’t be compensated for the increased care levels. Inspiren changes that. Data from Inspiren’s sensors is fed back into their software platform and will proactively flag residents who might need increased care. Inspiren’s clinical team reviews that data with communities and together they make sure that all residents are getting the care they need with financial arrangements that make sense for the community. The results are stunning: Inspiren customers are seeing 80%+ reductions in bedroom-related falls with an injury.
Inspiren is the latest of many bundled sensors and software deals in our portfolio and joins the likes of Motive, Locus Robotics, Spot AI, VergeSense, and others injecting intelligence into real-world operations. A hard-earned learning of ours is that it’s incredibly difficult to build a sensor + software platform that delivers hard ROI to customers at a price point that makes the unit economics work for the business. Alex, Michael, and the rest of the Inspiren team have done exactly that and are helping some of the largest senior living communities better serve thousands of residents and their families. We’re honored to be a part of their mission.
Scindo develops enzymes—nature’s tiny biological catalysts—that can transform a wide range of feedstocks into ingredients that have historically been sourced from petrochemicals.
Founded in 2020 by Dr. Gustaf Hemberg, Dr. Ben Davis, and Juliet Sword, Scindo combines AI models with proprietary data to accelerate enzyme discovery and optimization.
The firm, which has established partnerships with leading specialty chemical manufacturers, develops enzymes for several industry verticals including food and flavorings, cosmetics, and specialty chemicals.
With the new funding, it will expand its platform, scale wet-lab capabilities and strengthen its team.
“The specialty chemicals industry has long sought to move away from petrochemical-derived ingredients, but existing approaches have struggled with complex natural feedstocks,” said Ali Morrow, partner at lead investor Clay Capital.
“Scindo’s approach creates molecular craftsmen: enzymes designed for specific industrial jobs that offer cost-competitive natural alternatives and unlock previously inaccessible feedstocks, creating significant opportunities globally to end the industry’s reliance on crude oil.”
Designer enzymes
Scindo CEO Gustaf Hemberg told AgFunderNews: “When we started, we focused on mapping enzymes with functionalities that are difficult to achieve selectively with traditional chemistry and relatively uncommon in nature—particularly C–H activations and C–C bond cleavages [thereby opening up route for degrading stubborn molecules such as plastics].”
“By discovering and characterizing these underexplored enzymes, we built datasets that could be fed into our machine learning models—not only to identify new enzymes with previously unknown functionalities, but also to enable generative design of novel enzymes with entirely new capabilities.
“That’s really at the core of what we do: by gathering new examples and generating proprietary datasets of enzymes with novel and defined functionalities and selectivities, we enable our machine learning models to learn which parts of the sequence or structure drive the performance or selectivity that we’re targeting.
“And that’s the real challenge with public datasets—they’re concentrated on a few well-studied enzyme families with narrow, specific functionalities. They’re often incomplete and sometimes even contain mischaracterized examples, which makes them limiting when training models for prediction or generative design of enzymes for novel transformations and the specific applications that we are targeting.”
Once Scindo has identified suitable candidates, it engineers these enzymes further—first to optimize selectivity and transformation efficiency, and then to improve physical traits such as thermostability and expressibility, said Hemberg. Scindo also collects and enriches its predictive models with rich metadata to identify candidates most likely to scale in industrial settings and express at high yields in microbial systems. This ensures maximum viability for rapid scale-up and collaboration with manufacturing partners, he explained.
“We have quite a big chemistry screening platform, so we are able to test the enzymes in the lab, characterize them and then feed that data back into the machine learning. Closing the loop between real life results and machine learning has been really critical for us.”
Once it has tested some candidates, it can do further work to rank them based on viability for scaling up in a microbial expression system and then work with an enzyme manufacturer to scale up production, said Hemberg.
Cell-free biomanufacturing
Scindo’s first two products are enzymes that can create key building blocks of flavors & fragrances, and enzymes that can enable cost-effective petrochemical-free production of a high-value cosmetic ingredient via cell-free biomanufacturing.
In the case of flavor and fragrance ingredients, he said, “We can use a wide range of agricultural fatty acid feedstocks and have designed enzyme systems that selectively convert them into flavor molecules. We’re now advancing into pilot-scale production through a partnership we haven’t yet announced.
“Some of those flavor ingredients could be produced with precision fermentation [by engineering microbes to express them in costly steel fermentation tanks], but that is much more expensive [than using a cell-free approach just using enzymes], the titers are quite low, and you generate a lot of waste metabolites.”
By using a cell-free approach that utilizes the internal machinery of microbial cells (such as enzymes) to convert feedstocks into the target flavor molecules, Scindo can significantly reduce production costs, he claimed.
Operating outside the constraints of a cell—and without relying on costly cellular cofactors—allows Scindo to run faster reactions across a wider range of conditions, such as pH and temperature, and in ways that significantly reduce energy consumption as there is less heating and cooling required, he explained. It also generates cleaner products that require less costly downstream processing.
“We’re hoping to target a market launch in the next 12 months or so for our first two products.”
Proprietary data sets
Stepping back, he said, the world’s largest enzyme companies tend to concentrate on a few specific enzyme families for traditional applications, particularly in food, laundry detergents, and some pharma applications.
“We are instead focusing on novel applications that have historically been much more difficult to target with traditional chemistry and enzymes alike.
“Our key differentiator is the proprietary data we’ve built around novel enzymes—their functionalities, specificities, and characteristics—with broad applicability to carbon-chain transformations. That’s really what separates us: we’re working with data that isn’t publicly available.”
A few months ago, a close family member of mine needed a routine medical procedure. The care team was ready. The facility had an opening. But the entire process was put on hold while the office staff chased down a prior authorization from the insurance company. Days of back-and-forth. Hours of staff time. And in the meantime, unnecessary anxiety for my family.
Unfortunately, this is not an unusual story. It’s the reality of a system where a third of the healthcare workforce is dedicated to administrative tasks, costing over $450 billion annually. Endless phone calls, long hold times, and redundant processes eat up resources that should be directed toward patients.
That’s why we’re so excited about Prosper AI, which today announced a $5 million Seed round to bring voice AI agents purpose-built for healthcare to market. Emergence is proud to lead the round, alongside Y Combinator, CRV, and Company Ventures.
Prosper is working with industry leaders like a 30,000-employee billing company to transform their core operations. In speaking to customers, we consistently heard that voice AI is the biggest innovation in the healthcare revenue cycle management area since the EHR, and that Prosper’s product was the best in market. This customer love is driving rapid growth, as Prosper has more than quadrupled revenue since last quarter.
Prosper co-founders Xavier de Gracia and Josep Mingot met in Boston while studying at MIT and Harvard, and their backgrounds in call centers and regulated industries uniquely positioned them to build AI agents for healthcare’s most complex workflows.
When I think back to that delayed procedure, I can’t help but wonder how different the experience could have been if Prosper’s agents were already deployed. Faster authorizations. Less stress. More care delivered on time.
Healthcare should be about patients, not paperwork. That’s the future Prosper AI is building—and why we’re so proud to partner with them on the journey.
We’re entering a new era of life sciences, one marked by unprecedented speed of innovation and, paradoxically, slowing scientific progress. Drug discovery is becoming harder and more costly, not easier. Despite incredible technological advances, it now takes more time and more money to develop life-saving medicines than ever before.
This phenomenon has been wryly dubbed Eroom’s Law (Moore’s Law backwards). A nod to the fact that drug R&D productivity is moving in the opposite direction of the exponential advances we’ve seen in semiconductors and software. At Madrona, we believe this slowdown is unacceptable and not inevitable.
That’s why we’re thrilled to announce our investment in Synthesize Bio, founded by Rob Bradley, McIlwain Family Endowed Chair and Director of the Translational Data Science Integrated Research Center and Jeff Leek, J Orin Edson Foundation Endowed Chair and Chief Data Officer at Fred Hutchinson Cancer Center.
Rob and Jeff are world-leading researchers who have built their careers understanding biology from massive RNA datasets. They founded Synthesize Bio to build foundation models to solve the problems they face in their own research – understanding complex biology to deliver new insights that enable novel medicines in a highly competitive and resource-constrained environment.
Generative Genomics – A Foundation for the Future of Science
While biology hasn’t had its “ChatGPT moment” yet, we’re getting close. Rob and Jeff saw the massive potential while hacking diffusion models on their nights and weekends, inspiring them to found Synthesize Bio.
They came to us with the idea that in the future, most genomic data would be generated by models instead of in a lab. They called this idea “generative genomics” and showed us the first prototype of their idea. Over the past year, they have been quietly building, training a generative genomics foundation model (GEM-1) on the largest, most deeply curated RNA-seq dataset ever assembled. Their recent preprint demonstrates best-in-class performance: generating in silico data that matches wet-lab experiments – simply from experimental designs.
RNA-seq is the gold standard for linking genotype to phenotype at scale, capturing the transcriptional dynamics that translate static DNA into the active processes driving health and disease. RNA provides a uniquely rich substrate for generative modeling, leveraging high-throughput sequencing and massive datasets. For the first time, we’re starting to see scaling laws emerge in biology and with them, the possibility of generative tools becoming foundational infrastructure for scientific research.
While this is just the first release of the GEM models from Synthesize, they are already seeing super-experimental (analogous to super-human) performance – AI models that outperform novel lab experiments at reproducing biological signals.
It’s time to revisit the old adage, “An hour in the library can save a month in the lab.” Synthesize Bio reinvents that hour in the library; no longer are scientists limited to what others have published. Generative genomics moves reasoning agents from just searching published literature to enabling dynamic AI experiments that accelerate research by years, not months.
From Bottlenecks to Breakthroughs: Scientists Need to Do More with Less
Biology is hard. Scientists spend years and pharmaceutical companies spend millions narrowing down hypotheses, only to find themselves limited by what can be physically tested in patients or animals or what can be gleaned from early efficacy signals in Phase I clinical trial data. Can you get the patient samples needed? Do you have the statistical power to predict which patients are likely to respond to treatment? These constraints no longer need to define the limits of discovery or drug development.
Synthesize Bio is building a future where early-stage clinical data can be augmented with AI-generated datasets, providing better predictive power to de-risk costly trials and bring needed medicines to patients sooner. On top of the generative genomics foundation model, scientists can build applications solving the critical research challenges limiting scientific progress.
Hypotheses that were once infeasible due to cost or time can now be evaluated in silico
Human-relevant models bridge the gap between cells in a dish and real biology in people
Clinical study designs can be modeled computationally offering a preview of outcomes before the first patient is even enrolled
Generating biomarker data creates insight from impossible to collect biopsies
Biomarker hypotheses can be tested with robust statistical power before Phase III trials
This is the power of generative genomics: unlocking scale, speed, and scope that wet lab experiments or early clinical trial data alone simply can’t match.
The Scientist of the Future Will Spend 90% of Their Time at the Keyboard
The tech shift in biology is already underway. The success of generative protein design, AI-driven structure prediction, and automated screening tools is showing what’s possible. These tools, while powerful, all act on known biology and targets. To drive the future of life sciences, we need to discover new biology faster and more efficiently. We need tools to better de-risk clinical development before years are wasted on the wrong assets. Into this need steps Synthesize Bio and generative genomics.
The next generation of biotech breakthroughs will be built by scientists and drug development companies who are fully tech enabled, running experiments in code, iterating rapidly, and validating only the best ideas in the lab. But this future requires infrastructure. Unlike large language foundation models, life science research demands deep domain expertise, vertical-specific and highly curated training data, and integration with life science workflows. This isn’t a space for one-size-fits-all solutions. Synthesize Bio has built the foundation to drive the future of in silico-first, life science research.
Democratizing Discovery: From Insights to Impact
While no model will ever perfectly recapitulate all of biology, Synthesize’s represents a profound shift, empowering scientists to do more, faster, with greater confidence. And this is beyond just efficiency, it’s about enabling new and better science.
The Synthesize Bio team is developing partnerships with biopharma teams to accelerate drug development using their foundation models. Access to the GEM-1 foundation models is now available at Synthesize.bio and through R and Python API clients.
We are proud to back Rob, Jeff, and the Synthesize Bio team as they build this foundational layer for modern biotech. At Madrona, we’re investing from day one in the companies creating the future of life science R&D where scaling laws apply to biology, discovery accelerates, and new medicines reach patients faster.
If you’re building at this intersection—or want to—let’s talk.
Simon AI acts as a marketer’s data and execution team, uncovering hidden signals, activating real-world context, and accelerating high-performing launches that elevate customer experiences
NEW YORK — Sept. 15, 2025 — Simon AI, formerly known as Simon Data, today announced the launch of the Simon AI™ Agentic Marketing Platform, to enable marketing teams at fast-growing and enterprise brands to break free from the limitations and trade-offs that hold back high-performing personalization. With Simon AI, marketers set business goals, then purpose-built agents turn live customer and contextual data into adaptive campaigns that deliver higher conversion, increase customer lifetime value, and drive measurable growth.
Personalization previously took weeks to months to execute. Now, Simon AI Agents identify signals and patterns, prepare data for execution, and automate high-volume micro-segmentation into engagement channels. As a result, marketing is now fast and nimble enough to activate customer moments, elevating both scale and performance.
“Agentic AI is changing how marketing gets done, representing the biggest shift since the move to SaaS and cloud computing,” said Jason Davis, co-founder and CEO of Simon AI. “Until now, marketers have faced a painful trade-off — launch more campaigns and watch performance drop, or push for deeper personalization and lose volume. With Simon AI, that trade-off ends.
“Agentic Marketing is a new model where embedded agents operate across the most complex workflows on an AI-first, composable CDP, accessing all customer and contextual data live in the data cloud. Simon AI Agents can reason over that data, enrich it, and execute at a scale that was previously impossible. Now, marketing teams can finally overcome the data and execution complexity that has held personalization back.”
Solving Data and Execution Complexity for Marketers
For most brands, personalization is still constrained by four challenges:
Data access: Marketers can’t get the right signals in time to act.
Execution bottlenecks: Campaigns take weeks to launch, making “real-time” and “continuous” impossible.
The missing context: First-party data leaves out signals like weather, inventory, and trends that drive customer decisions.
AI acceleration: Teams use surface-level AI tools for content generation and predictive analysis, yet struggle to apply AI to the most complex marketing problems that are blockers to insight and execution.
Together, these challenges prevent marketing teams from achieving true personalization — and they define why a new way of marketing and a new solution are needed.
The Simon AI Agentic Marketing Platform
Simon AI combines a goal-based workflow, agents that deliver insights and automate execution, and an AI-first composable CDP powered by best-in-class integrations. With unified customer and contextual data in a brand’s existing cloud data environment, real-world signals define audiences and trigger messaging, adaptive campaigns launch faster, and personalization executes with governance and control.
Simon AI Personalization Studio
The workspace where marketers turn strategy into performance. The Personalization Studio starts with goals, not static segments, and gives teams a guided environment to connect data to campaigns that adapt automatically to live signals. With it, marketers can:
Define business goals in plain language and turn them into data-driven campaigns.
Use Blueprints—reusable playbooks that translate goals into strategies and execution plans—to guide agents and launch thousands of micro-campaigns.
Continuously evolve campaigns with AI Fields and AI Moments. AI Fields create new attributes about customers or products, such as a “Cold-weather readiness score” or “Price sensitivity”. AI Moments detect and operationalize real-world triggers, such as a weather swing, social trend, or inventory change, that signal when to act.
Automate execution across every channel—engagement platforms, owned channels, and paid media—with campaigns that stay aligned to outcomes.
Simon AI Agents
The marketer’s data and execution team that builds personalization based on insights and customer moments. Agents handle the complexity of surfacing signals, preparing data, and activating campaigns so that marketers can focus on strategy, creative, and customers. With Simon AI Agents, marketers can:
Detect hidden signals such as churn risk, demand spikes, inventory changes, weather, and social trends.
Transform messy customer and contextual data into campaign-ready attributes.
Orchestrate workflows and activation across platforms like Braze, Attentive, Iterable, and more.
Simon AI Composable CDP
The data foundation and semantic layer that makes AI work. Running natively in your cloud, the composable CDP makes customer and contextual data actionable, enables high-volume personalization, and enriches the enterprise source of truth. With the AI-first CDP, marketing teams can:
Explore and activate all customer, business, and contextual signals.
Run personalization directly on live data with zero ETL pipelines.
Enrich data and write back new fields, segments, and results into the data cloud for enterprise use.
Maintain enterprise-grade governance and control inside the data warehouse.
What It Means for Customers
With Simon AI, brands accelerate differentiation and growth by launching campaigns frequently, acting on more signals, and scaling personalization without trade-offs. Early adopters have reported:
Rapid execution of contextually relevant campaigns.
Higher conversion rates driven by contextual signals and adaptive personalization.
Material revenue growth, powered by more campaigns in market at a greater speed.
The New Model: Agentic Marketing
AI is reshaping how brands engage customers. To compete, marketers must act on 100x more signals, make 100x more decisions, and run thousands of micro-campaigns. Simon AI introduces Agentic Marketing — a new model that removes bottlenecks, unlocks insights, and gives marketers direct control of fast, precise personalization:
AI-Powered Execution: Agents handle insights, data preparation, and orchestration as part of the marketing team. Campaigns adapt quickly to live customer and contextual data, scaling personalization without overhead.
Contextual Personalization with Real-World Signals: Marketers see customers in full context, connecting profiles and behavior to signals like inventory, weather, and trends. Marketing moves past assumptions and acts on what matters now.
Marketer-First, Goal-Based Workflows: Instead of starting with static segments, marketers define business goals. Agents turn those goals into personalized campaigns that launch faster and continuously optimize as new signals emerge.
Alongside the launch of the Simon AI Agentic Marketing Platform, the company has rebranded from Simon Data to Simon AI, reflecting its evolution into an AI-first company. The new name underscores the central role of agentic AI in enabling personalization and highlights the value of connecting data to execution through AI.
Visit simon.ai to learn more and connect with our team to see how Simon AI works.
About Simon AI
Simon AI empowers marketing teams with the data, tools, and support needed to deliver personalized experiences for each customer across every touchpoint. The platform combines an AI-first, composable customer data platform with AI agents, enabling marketers to start with a goal while agents analyze signals, create attributes, identify triggers, and orchestrate campaigns that continuously adapt to meet that goal. By uncovering hidden signals, activating 100x more customer and contextual data, and automating execution across engagement channels, Simon AI allows even small teams to perform like much larger ones. Leading brands such as ASOS, SeatGeek, and others rely on Simon to turn complex data into faster launches, personalized experiences at scale, and revenue-driving performance. Visitsimon.ai to learn more.
Walgreens, O’Reilly Auto Parts, and TD Bank are among the Fortune 500 companies using GreenLite’s AI-driven Private Plan Review for permitting efficiency
NEW YORK, Sept. 15, 2025 /PRNewswire/ — GreenLite, the construction technology company accelerating permit timelines by 75% through AI-powered plan review and compliance solutions, today announced a Series B funding round of $49.5M, led by global software investor Insight Partners with participation from Energize Capital, as well as existing investors Craft Ventures, LiveOak Ventures, and Chicago Ventures. GreenLite will utilize the new capital to expand its go-to-market efforts and enter new verticals, including lodging, industrial and logistics, clean energy infrastructure, and residential development, while further advancing its AI-powered technology platform.
As demand for construction surges, jurisdictions and building departments face unprecedented challenges, including labor shortages, limited adoption of technology, and rising backlogs.
This strain is renewing focus on technologies and policies for permitting solutions, including Private Plan Review (PPR), where qualified third-party experts conduct official code compliance reviews instead of the city. Nearly a quarter of U.S. states have advanced legislation for PPR in the past three years, aiming to reduce delays and streamline development. Today, GreenLite is the only Private Provider combining regulatory expertise with AI to deliver PPR at a national scale.
“The permitting backlog is holding back America’s ability to build at the scale and speed we need,” said James Gallagher, Co-Founder and CEO of GreenLite. “By combining a growing database of proprietary compliance comments with advanced automation, we’re catching violations faster and providing builders, developers, and jurisdictions with the predictability and transparency they need to move projects forward, dramatically transforming the plan review and construction code compliance process.”
GreenLite’s AI-powered digital plan review tool, LiteTable, rapidly ingests plan sets, identifies compliance flags and code requirements, and surfaces relevant guidance from GreenLite’s extensive comment library based on compliance patterns within specific jurisdictions. Today, GreenLite is trusted by nearly a hundred Fortune 500 customers, including retailers, REITs, quick service restaurants, industrial owner-developers, and production home builders to advance permitting nationwide. The company is expanding into lodging, logistics, multifamily, and additional verticals this year.
“GreenLite’s full-stack Private Plan Review approach delivers building permits in days, not months, and is driving growth in America’s local communities and economies,” said Jeff Horing, Co-founder and Managing Director at Insight Partners. “We’re thrilled to back GreenLite as it continues to partner with the commercial sector and local governments to power the future of construction permitting.”
GreenLite was founded in 2022 by James Gallagher and Ben Allen, former Gopuff executives. The company has 50 full-time employees today, and is actively hiring across engineering, product, sales, marketing, operations, and executive roles.
To learn more about GreenLite’s AI-powered permitting and private plan review capabilities, please visit: https://greenlite.com/.
About GreenLite: GreenLite is transforming how America builds by streamlining the permitting process for developers, builders, and local governments. GreenLite pioneered AI-powered Private Plan Review (PPR), where third-party experts, supported by proprietary software, conduct official code compliance reviews instead of cities.
Its technology accelerates approvals by scanning plan sets, identifying code violations, and surfacing jurisdiction-specific guidance from a large and growing proprietary database of compliance comments. With a team of in-house architects, engineers, and plan examiners, GreenLite helps customers reduce revisions, avoid delays, and cut weeks or months off their permitting timelines.
Trusted by nearly 100 national brands, GreenLite is reshaping the future of permitting across industries from retail and banking to logistics, lodging, and multifamily development. Learn more at https://www.greenlite.com.
About Insight Partners: Insight Partners is a global software investor partnering with high-growth technology, software, and Internet startup and ScaleUp companies that are driving transformative change in their industries. As of June 30, 2025, the firm has over $90B in regulatory assets under management. Insight Partners has invested in more than 875 companies worldwide and has seen over 55 portfolio companies achieve an IPO. Headquartered in New York City, Insight has a global presence with leadership in London, Tel Aviv, and the Bay Area. Insight’s mission is to find, fund, and work successfully with visionary executives, providing them with tailored, hands-on software expertise along their growth journey, from their first investment to IPO. For more information on Insight and all its investments, visit insightpartners.com or follow us on X @insightpartners.
