DeepTech.
Research-grade AI for the labs writing tomorrow.
Compress months of literature review and lab cycles into days. We build the AI scaffolding around your scientists, not on top of them.
Knowledge graphs, simulation models and automated discovery for R&D-heavy teams.
What gets in the way
Fragmented research
Decades of papers, internal notes and lab data scattered across drives, wikis and people's heads.
Long R&D cycles
Every experiment is expensive — both in compute and in scientist-hours. Picking the wrong direction stings.
Hard-to-justify spend
Boards want ROI math on AI bets. Research teams want freedom. Both are right.
Where AI moves the needle
Knowledge graph
Connect disparate data sources to surface non-obvious correlations across years of work.
Simulation modeling
Predict complex outcomes and shrink the wet-lab cycle by ranking experiments before they run.
Automated discovery
LLM pipelines that read literature, extract claims, and propose hypotheses with citations.
Collaboration tools
Co-analyze datasets in real time, with provenance baked into every chart and query.
Patent intelligence
Map your IP against the global landscape; spot whitespace and infringement risk early.
How we delivered
Knowledge assistant that turned 22 years of notes into a queryable graph
We unified 14 data sources — papers, lab books, ELN exports — behind a single retrieval layer with citation-grade answers. Senior scientists got back 6 hours a week. Onboarding for new hires dropped from 4 months to 6 weeks.
How we work
Discovery
4–6 hour workshop. Goals, customer segments, JTBD, user flows, AI proposal, 6-month roadmap.
Architecture
Data audit, model selection, integration plan, evaluation harness, governance.
Build
2-week iterations. Demo Fridays. Built-in observability from day one.
Scale
Production rollout, change management, continuous fine-tuning and cost monitoring.
Ready to put this on the roadmap?
We run a focused discovery in 2 weeks. You leave with a working prototype and a defensible ROI case.