Biomedical Discovery

BIOMEDICAL DISCOVERY · RESEARCH PARTNERSHIPS

The bottleneck is rarely the science.

It is the week spent reconciling assay outputs across three file formats. The prior program whose design rationale lives in a departed scientist's notebook. The candidate that advanced for reasons nobody documented.

We build governed data infrastructure for research teams — the layer that makes screening results reproducible, design decisions traceable, and prior institutional knowledge retrievable. We are selective about these engagements and we work as a partner rather than a vendor.

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What we are, and what we are not.

Scientific audiences are appropriately skeptical of software companies making discovery claims. So we will be direct about the boundaries of what we do.

WHAT WE ARE NOT

We do not build molecular property prediction models. We do not do de novo generative design. We do not have a proprietary chemistry or biology platform, and we are not going to tell you our AI will find your next candidate.

Companies with hundreds of computational scientists are doing that work. If that is what you need, they are the right call — and we will say so.

WHAT WE ARE

We build the governed data and knowledge infrastructure underneath the science. The pipeline that makes assay results reproducible six months later. The traceability layer that documents why a candidate advanced. The retrieval system that makes prior program knowledge findable by someone who was not there.

That work is unglamorous. It is also the work most often deferred until it becomes urgent — usually when a partner, a regulator, or an investor asks a question the current systems cannot answer.

If your bottleneck is model architecture, we are not your partner. If your bottleneck is that the data feeding your models is scattered, inconsistent, or undocumented — that is precisely what we build.

Four problems that repeat across every research program.

Screening output that outpaces interpretation

High-throughput screening produces results faster than scientists can review them consistently. Hit triage becomes a queue, dose-response review becomes a bottleneck, and the criteria for advancing a candidate vary depending on who is doing the reviewing that week.

Institutional knowledge held in people

The rationale behind a prior program's design decisions, the assay quirk that explained an anomalous result, the reason a promising candidate was dropped — this knowledge exists in notebooks, archived presentations, and the memory of specific scientists. It is not retrievable by anyone who was not in the room.

Design and assay data that never reconcile

In silico predictions live in one system. Wet-lab results live in another. Comparing predicted versus observed performance requires manual reconciliation every time — which means the feedback loop that should be improving your design process is running at a fraction of its potential frequency.

Advancement decisions with no documented lineage

A candidate reaches development. A partner, an investor, or eventually a regulator asks how it was selected and what ruled out the alternatives. The answer exists across a lab notebook, a Slack thread, and a presentation from eight months ago. Reconstructing it costs a week of senior scientist time.

Each disease target is biologically distinct. The operational workflow around it rarely is. These are the failure patterns we see across RNA therapeutics, small molecule discovery, and translational research programs alike.

Four bounded engagements, each scoped to one workflow.

These are the patterns we can deliver in a four-week cycle. Each is narrow by design — narrow enough to finish completely and validate against your real data.

  
        Engagement
        What we build
        When it fits
      
        EngagementScreening result triage
        What we buildA governed pipeline structuring screening output with automated triage logic — separating clear hits, ambiguous results, and exceptions into a reviewable queue with consistent criteria applied.
        When it fitsAssay output volume exceeds what your team can review consistently, and advancement criteria vary by reviewer.
      
        EngagementDesign-to-assay data loop
        What we buildA structured schema connecting in silico design outputs to experimental readouts, so predicted versus observed comparison is a query rather than a manual reconciliation.
        When it fitsComputational and wet-lab data live in disconnected systems and the feedback loop runs slower than it should.
      
        EngagementProgram knowledge retrieval
        What we buildA bounded retrieval layer over prior program documentation, assay summaries, design rationale, and internal reports — every answer citing the specific source document.
        When it fitsInstitutional knowledge is fragmented across notebooks, decks, and people, and onboarding or handoff is painful.
      
        EngagementDecision lineage and traceability
        What we buildA traceability layer connecting inputs, evidence, review notes, and outcomes for candidate progression — so advancement rationale is documented as it happens rather than reconstructed later.
        When it fitsA program is approaching clinical development, partnership diligence, or regulatory submission.

Engagement	What we build	When it fits
EngagementScreening result triage	What we buildA governed pipeline structuring screening output with automated triage logic — separating clear hits, ambiguous results, and exceptions into a reviewable queue with consistent criteria applied.	When it fitsAssay output volume exceeds what your team can review consistently, and advancement criteria vary by reviewer.
EngagementDesign-to-assay data loop	What we buildA structured schema connecting in silico design outputs to experimental readouts, so predicted versus observed comparison is a query rather than a manual reconciliation.	When it fitsComputational and wet-lab data live in disconnected systems and the feedback loop runs slower than it should.
EngagementProgram knowledge retrieval	What we buildA bounded retrieval layer over prior program documentation, assay summaries, design rationale, and internal reports — every answer citing the specific source document.	When it fitsInstitutional knowledge is fragmented across notebooks, decks, and people, and onboarding or handoff is painful.
EngagementDecision lineage and traceability	What we buildA traceability layer connecting inputs, evidence, review notes, and outcomes for candidate progression — so advancement rationale is documented as it happens rather than reconstructed later.	When it fitsA program is approaching clinical development, partnership diligence, or regulatory submission.

Each of these runs as a four-week cycle scoped to one program or one assay family. We start narrow deliberately — in research environments, a system validated against real data on a small scope earns more trust than a broad system nobody has stress-tested.

The moment infrastructure debt comes due.

There is a specific point in a program's life where deferred data infrastructure becomes urgent: the transition from research to clinical development.

Until that point, a lean team with good notebooks and strong institutional memory functions well — arguably better than a team burdened with process. After that point, the questions change. Clinical partners want development history. Regulatory reviewers want candidate selection rationale. Safety monitoring generates observations that need to link back to design hypotheses. Investors conducting diligence want a documented chain.

The knowledge to answer those questions exists. It is just not assembled, and assembling it retroactively is far more expensive than capturing it as it happens.

THE PRACTICAL POINT

If your program is approaching first-in-human, an IND filing, or a co-development partnership, the traceability and monitoring infrastructure is worth building before the questions arrive rather than after. That is a four-week problem addressed early and a six-month problem addressed late.

SPECIFIC TO PERSONALIZED THERAPEUTICS

For programs treating ultra-rare or single-patient indications, this is more acute. Standard clinical monitoring infrastructure assumes population-scale data, comparison cohorts, and established reference ranges. When the patient population is one, every observation is the entire dataset — and the documentation requirements are less defined precisely because the situation is newer than the frameworks built to govern it.

Partnership terms, not vendor terms.

We start with a scoping conversation, not a proposal

The first conversation is us understanding your workflow, not us presenting capabilities. If there is not a bounded problem we can genuinely solve in four weeks, we will say so rather than manufacture one.

You own everything

Code, infrastructure definitions, documentation, and any data model we build. Deployed into your environment on open source and platform tooling your team can maintain. No proprietary layer you would need us to keep running.

Narrow scope, validated against real data

One assay family. One program's documentation. One decision workflow. Scope narrow enough that the output can be validated by your scientists against data they know well — which is the only validation that means anything in a research setting.

Selective engagement

We take a small number of research partnerships because they require domain immersion we cannot do at volume. If the fit is not right, we would rather tell you early and stay in touch than take an engagement neither of us is well served by.

Research engagements are structured differently from our commercial work, for reasons that matter to both sides.

For organizations where the data is the constraint, not the science.

Clinical-stage biotech with lean teams

Companies moving a program from research into development, where the documentation and traceability requirements are about to increase sharply and the team size is not going to.

High-throughput screening operations

Groups generating assay volume that exceeds consistent manual review, where reproducibility and reviewer-to-reviewer consistency have become operational problems rather than theoretical ones.

Research organizations with fragmented institutional knowledge

Teams where the answer to “have we seen this before” requires asking a specific person, and where that person's eventual departure represents an unquantified risk.

Reasonable questions.

Your billing company submits claims. This evaluates whether a claim will survive submission before it goes out, and whether a patient's record supports continued eligibility before their renewal date. Most billing partners welcome it — fewer denials to rework is good for both of you.
Documentation quality and documentation compliance are different problems. A thorough note can still fail a plan-specific modifier requirement. The question is not whether your clinicians document well — it is whether anything systematically checks that documentation against the requirement that applies to that specific claim, payer, and program.
Yes, and it is built for that. We execute a BAA before any data access, work in isolated environments, and tokenize PHI at the ingestion boundary so protected data never reaches the AI layer directly.
That is exactly why the cycle is four weeks. A cycle started this quarter is in production well before the first six-month renewal wave. A cycle started in November is not.

Find out in 30 minutes whether this fits.

We will ask you three questions: which workflow is leaking, what data sits behind it, and what "fixed" looks like to you. If we can scope it into a four-week cycle, we will tell you exactly what that includes and what it costs. If we cannot, we will tell you that too.

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