Don’t just keep up. Get a research platform and run faster. Leaner. Better.
Bench gives your research team enterprise AI infrastructure — local-first, built on biological data, and configurable to your specific biology. Move faster. Spend less. Protect your data.
The ROI Is Measurable. We’ll Show You the Math.
Replace five budget lines with one.
Research teams use outdated, expensive software while outsourcing slow discovery and balloons costs. Heureka offsets both.
| Cost Center | Typical Annual Spend |
|---|---|
| Commercial ELN | $300 – $1,200 per researcher |
| Reference management tools | $150 – $300 per researcher |
| LLM subscriptions | $240 – $2,400+ per researcher |
| Bioinformatics outsourcing | $5,000 – $50,000+ per project |
| Bench | Starting at $250 per researcher per year |
Beyond direct cost replacement:
- Analyses that previously required CRO outsourcing or a dedicated bioinformatician run in-house, on demand
- Scheduled research tasks run overnight — no consultant billing hours
- Hypotheses generated and triaged faster means fewer dead ends funded to completion
- Reduce time-to-decision on target prioritization, lead optimization, and go/no-go calls
We’ll model the offset for your team size in a 30-minute scoping call.
Your Data And Insights Stay with You.
Local-first. Cloud support if you need it.
In biotech, your experimental data is your competitive advantage. The idea of that data leaking — or worse, training someone else’s model — is a non-starter.
Bench is built local-first to maximize security while seamlessly integrating into your workspace.
- ELN installs on your infrastructure — experimental data, protocols, target hypotheses, and assay results never leave your environment without your explicit action
- We do not train on user data. Your experiments are never used to improve our models or anyone else’s
- Custom data models are trained in isolated environments — your proprietary datasets are never shared, pooled, or exposed
- Full data provenance and audit logging for every analysis — know exactly what was run, on what data, at what time
- Supports IP protection documentation and due diligence preparation for fundraising, partnerships, and licensing conversations
Speed That Compounds
Get ahead of the competition with results that build on themselves.
In biotech, the research calendar is also the financial calendar. Pipeline velocity isn’t an operational metric — it’s a valuation driver.
- Hypothesis generation grounded in 165M+ biological relationships surfaces non-obvious connections your team would take weeks to find manually
- Bioinformatics analyses that previously took days of setup and execution run in hours — from your desktop
- AI-assisted protocol design reduces iteration cycles on experimental setup
- Scheduled agents run literature surveillance, competitive intelligence scans, and data analyses overnight — results waiting at standup
- From data to interpreted findings to slide-ready outputs in a single workflow — no handoffs between tools, no reformatting
The compounding effect matters: a team that makes decisions one week faster across a 12-month program doesn’t finish one week early. They take a different set of decisions entirely.
A Model Trained on Your Biology
General-purpose AI doesn’t know your target. Yours will.
Archimedes is already trained on 165M+ biological relationships across trusted multi-omic data sources and 30+ tissue types. That’s the starting point. A custom model goes further — trained on your proprietary data to reflect your specific biology, your assay history, and your compound landscape.
- Fine-tune Archimedes on your internal datasets — cell line data, in vivo results, proteomics, historical assays — to build a model that reflects what your lab has actually learned
- Query your custom model the same way you’d query ours: structured, reproducible, auditable outputs
- The model improves as your data grows — a compounding scientific advantage that expands your competitive edge over time
- Purpose-built models available for specific disease areas, target classes, and assay platforms — off-the-shelf for common contexts, fully customized for your needs
- Your model is yours — isolated, not shared, not used to train anything else
This is the difference between a tool that helps your team work faster and an asset that gives your pipeline a durable edge.
Reproducible Research Is Regulatory-Ready Research
What’s good science today is due diligence tomorrow.
Reproducibility in biotech isn’t just a scientific standard — it’s a business requirement. Every analysis that eventually touches an IND, a partnership data room, or an acquisition review needs to be traceable, documented, and defensible.
Heureka makes reproducible outputs the default, not the exception.
- Every analysis automatically generates a full report package — code, input data, output figures, methods, and interpretation bundled together
- Any result can be reproduced from any point in a project’s history without reconstructing what was done
- Step-by-step ARC workflows are fully visible and auditable — no black-box inference chains
- Data artifacts are inspectable and downloadable at every stage
- Structured outputs are formatted for regulatory documentation, partnership data rooms, and board reporting from the start
When a partner, acquirer, or regulator asks to see the underlying analysis, your team has it ready — organized, documented, and reproducible.
Built for Lean Teams. Scales With Your Pipeline.
The output of a larger team. Without the headcount.
Most biotechs face staffing tradeoffs constantly. Bench doesn’t replace scientists — but it extends what a small, focused team can do without scaling headcount at the same rate as the pipeline.
- Researchers without deep computational backgrounds run analyses previously requiring a dedicated bioinformatician
- Step-by-step guided workflows mean new team members are productive faster — less ramp time, lower training overhead
- Agentic tasks handle routine research operations — literature monitoring, competitive scans, scheduled analyses — without consuming senior scientist time
- As your team grows, the platform scales with it — no retooling, no new contracts, no migration
- Onboarding support, office hours, and dedicated support calls ensure your team gets value from day one, not month three
Hiring a bioinformatician costs $120,000–$180,000 per year. Bench costs $250 per researcher. The advantage is clear.
Transparent AI Your Team Can Stand Behind
If your scientists can’t explain the output, it doesn’t belong in your pipeline.
The problem with black-box AI in drug discovery isn’t just scientific — it’s organizational. When a hypothesis comes from an opaque model, your team can’t interrogate it, your CSO can’t defend it, and your board can’t evaluate it.
ARC is auditable by design.
- Every analysis displays step-by-step reasoning — what was run, in what order, on what data
- Hypothesis outputs include confidence levels, supporting biological evidence, and explicit alternative explanations
- Data artifacts are visible and downloadable at each stage — nothing is hidden in an inference layer
- Researchers interact with the reasoning, not just the conclusion — building scientific judgment rather than creating dependency on a tool they don’t understand
- Full audit trail for every query, every output, every decision point
Your team should be able to walk anyone — an investor, a collaborator, a regulator — through exactly how a finding was generated. Bench makes that possible.
Infrastructure for Where Biotech Research Is Going
Start with a productivity tool. Build toward an autonomous research operation.
The research organizations that lead the next decade won’t just use AI — they’ll run on it. Heureka is designed as the infrastructure layer for that transition, starting with tools your team can use today and scaling toward capabilities that change how research divisions operate.
- Core facility integration: Connect Bench projects to genomics, imaging, proteomics, and other platform outputs for end-to-end automated research pipelines
- Autonomous research workflows: Define research questions, set parameters, and let agentic pipelines run literature surveillance, hypothesis generation, and data analysis continuously
- Purpose-built disease area models: Foundation models scoped to your specific therapeutic area, target class, or patient population — in development and available through partnership
- AI research policy and governance: For biotechs navigating board-level questions about AI use, we work with leadership to develop frameworks that satisfy investors, partners, and regulators
- Bespoke development: Unique infrastructure requirements, existing LIMS integrations, or workflow-specific needs — we scope and build to spec
The companies building this infrastructure now aren’t just getting more efficient. They’re building a research capability that compounds.
Book a Briefing
30 minutes. We’ll model the ROI for your team size, walk through the data security architecture, and scope a custom deployment. We’ll come with numbers — bring your questions.