devano.ai

Devano 2026: The Only Way to Work

Devano AI — Mon, 12 Jan 2026 00:00:00 +0000

A Year of Acceleration</h2>
2025 was a monumental year for AI. The list of major model releases, benchmarks, and product launches is long and well-documented elsewhere. Rather than recounting those developments, we wanted to share how the broader movement in AI shaped us as a startup—and a few things we consistently observed while working with customers across scientific and biomedical organizations.</p>

Our Starting Hypothesis</h2>
Our hypothesis going into 2025 was straightforward: AI for science needs to be scalable, repeatable, and consistent. One of the core bottlenecks is the ability to use AI agents to reliably produce high-quality scientific assets. Everyday conversations with powerful chatbots have transformed our world, but this now-common modality often isn’t sufficient for scientific work. These one-off conversations don’t naturally compound to turn into durable datasets, reproducible analyses, or shared organizational knowledge.</p>

From Conversations to Systems</h2>
Agentic pipelines, by contrast, can be designed to systematically generate clean, verifiable data assets. They can encode assumptions, track provenance, apply quality control, and be re-run at scale. The challenge—and the opportunity—is that moving from low-level agent frameworks to workflows that are genuinely useful in real scientific contexts is non-trivial.</p>
Bridging this gap requires more than better prompts or more powerful models. It requires systems that make execution, verification, and reuse first-class concerns. This is the space we’ve focused on building into Devano, and where we consistently see the most durable value emerge for teams working with complex scientific data.</p>

An Unexpected Shift in How We Work</h2>
What we didn’t anticipate was how deeply this same ethos would affect the way we worked ourselves.</p>
As we built and deployed our agentic platform with customers, our internal bottleneck quietly moved. Writing code was no longer the limiting factor. Instead, the hard work became designing guidance systems, constraints, and quality-control procedures for the agents that now handle much of the coding and execution work we previously did by hand. In effect, as we helped customers build agentic automations for biomedical data, we were building and refining the same systems inside Devano—systems for guidance, verification, provenance, and scale. We were using the product the way it was meant to be used.</p>
The impact on our productivity was dramatic. A team of two began operating with the leverage of a much larger organization, while making the services component of our enterprise business far more scalable, consistent, and repeatable.</p>

Validation from Our Customers</h2>
Over the course of the year, this pattern was reinforced by our customers. Teams across scientific and biomedical organizations described the same limitations: chat-based AI interactions were useful and necessary, but often insufficient for building assets that could be trusted, reused, and scaled. What they needed were systems that could apply consistent logic, track provenance, and enforce quality standards across many datasets and use cases.</p>
These conversations validated our direction. As customers incorporated Devano into their workflows, several described meaningful reductions in time spent on previously manual or ad-hoc tasks—often compressing work that took days or weeks into hours. Just as importantly, this shift has pushed teams to think more deliberately about how multiple AI systems fit together: where Devano provides structured execution and verification, and where other models and tools support exploration, review, or domain-specific reasoning. In practice, Devano has become one component of a broader, hybrid AI setup shaped by each team’s needs and constraints.</p>

The Shape of Modern Work</h2>
In short, what we are seeing is not just AI adoption—and certainly not a world where a single AI platform replaces everything. Instead, we are seeing a fundamental and rapid shift in how work gets done: well-designed agentic systems plugging into an evolving, hybrid stack that augments human judgment. Increasingly, the human work is not in execution, but in the design of guidance systems and in figuring out how best to assemble the pieces.</p>

Looking Ahead</h2>
Looking back on 2025, the biggest lesson for us wasn’t just about AI capability. It wasn’t even about biomedical data. It was about what the future of modern knowledge work looks like.</p>
We thought we were building agentic pipelines for biomedical data. We didn’t realize we were on the bleeding edge of building the only way to work in the modern era.</p>
In 2026, we’re continuing to build Devano around this belief: that the future of scientific work depends on systems that scale judgment, not just execution. If this way of working resonates with you, we’d love to compare notes—or help you build it inside your own organization.</p>

Announcing Our First Strategic Partnership

Devano AI — Tue, 06 May 2025 00:00:00 +0000

How Devano and Verge Genomics Are Reimagining Data Curation</h2>
At Devano, our mission is to use modern AI to eliminate the manual bottlenecks that slow down scientific discovery. We believe AI agents can finally make tedious biomedical data wrangling a thing of the past.</p>
Today, we’re excited to announce our first strategic partnership with a team that shares that vision: Verge Genomics</a>.</p>
Over the past few months, Verge and Devano have partnered to tackle the problem of GEO data curation. With Devano’s GEO and PubMed agents, the Verge team can now compress days of manual work into just minutes.</p>

Introducing Devano AI

Devano AI — Tue, 22 Apr 2025 00:00:00 +0000

We started Devano with a simple but ambitious mission: use modern AI to solve the everyday challenges that impact the pace of scientific discovery.</p>

In the life sciences, data exists in a fascinating ecosystem of interconnected knowledge. Biomedical information naturally spans diverse formats, sources, and systems—reflecting the rich complexity of biology itself. This creates a fundamental challenge: questions that should be straightforward to answer often require hours of tedious manual work.</p>

A researcher looking to compile "all human kidney studies using single-cell RNA-seq" faces a deceptively complex task. This information might exist across Gene Expression Omnibus (GEO), PubMed, supplementary materials, and other sources, but connecting the dots means manually:</p>

Sifting through free-text fields that might describe "kidney," "renal tissue," or "nephric samples"</li>
Cross-referencing papers to find crucial sample metadata not included in repository entries</li>
Determining experimental methods from inconsistently formatted protocol descriptions</li>
Understanding the access patterns and limitations of each data source</li> </ul>
This manual connection-making steals time from the creative, high-value work that advances science.</p>