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 work as a partner rather than a vendor.
Scientific audiences are rightly skeptical of software companies making discovery claims, so we will be direct about the boundaries of what we do.
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.
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.
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 reviewing that week.
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 lives in notebooks, archived presentations and the memory of specific scientists.
In silico predictions live in one system and wet-lab results in another. Comparing predicted with observed performance means manual reconciliation every time, so the feedback loop that should improve your design process runs at a fraction of its potential frequency.
A candidate reaches development. A partner, an investor or a regulator asks how it was selected and what ruled out the alternatives. The answer exists across a lab notebook, a Slack thread and an eight-month-old presentation. Reconstructing it costs a week of senior scientist time.
Each disease target is biologically distinct. The operational workflow around it rarely is. We see these failure patterns across RNA therapeutics, small-molecule discovery and translational research programs alike.
These are the patterns we can deliver in a four-week cycle, scoped to one program or one assay family. We start narrow deliberately: in research, a system validated against real data on a small scope earns more trust than a broad system nobody has stress-tested.
| Engagement | What we build | When it fits |
|---|---|---|
| Screening result triage | A governed pipeline structuring screening output with automated triage logic, separating clear hits, ambiguous results and exceptions into a reviewable queue with consistent criteria applied. | Assay output volume exceeds what your team can review consistently, and advancement criteria vary by reviewer. |
| Design-to-assay data loop | A structured schema connecting in silico design outputs to experimental readouts, so predicted-versus-observed comparison is a query rather than a manual reconciliation. | Computational and wet-lab data live in disconnected systems and the feedback loop runs slower than it should. |
| Program knowledge retrieval | A bounded retrieval layer over prior program documentation, assay summaries, design rationale and internal reports, every answer citing the specific source document. | Institutional knowledge is fragmented across notebooks, decks and people, and onboarding or handoff is painful. |
| Decision lineage and traceability | A traceability layer connecting inputs, evidence, review notes and outcomes for candidate progression, so advancement rationale is documented as it happens rather than reconstructed later. | A program is approaching clinical development, partnership diligence or regulatory submission. |
There is a specific point in a program’s life when deferred data infrastructure becomes urgent: the transition from research to clinical development. Until then, a lean team with good notebooks and strong institutional memory functions well. After it, clinical partners want development history, regulatory reviewers want selection rationale, safety observations need to link back to design hypotheses and investors 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.
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. That is a four-week problem addressed early and a six-month problem addressed late.
For ultra-rare or single-patient indications this is more acute. Standard 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.
Research engagements are structured differently from our commercial work, for reasons that matter to both sides.
The first conversation is us understanding your workflow, not presenting capabilities. If there is no bounded problem we can genuinely solve in four weeks, we will say so rather than manufacture one.
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.
One assay family. One program’s documentation. One decision workflow. Narrow enough that your scientists can validate the output against data they know well, which is the only validation that means anything in research.
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.
Companies moving a program from research into development, where documentation and traceability requirements are about to rise sharply and the team size is not.
Groups generating assay volume beyond consistent manual review, where reproducibility and reviewer-to-reviewer consistency have become operational problems rather than theoretical ones.
Teams where the answer to “have we seen this before” requires asking a specific person, and where that person’s eventual departure is an unquantified risk.
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.
Book a 30-minute scoping call →Hi. Ask me about our services, the four-week cycle, PHI and security, or the Luveo case study. Every answer links to where it comes from.