Section_01 // Decision_Support_Infrastructure
JÄSHA Intelligence Systems
AI decision-support for biotech and nanotech. We turn fragmented lab, clinical, manufacturing, and regulatory data into structured intelligence — without replacing scientific judgment.
The models aren't too weak. The data is too scattered.
Across manufacturing, clinical trials, lab data, literature, biomarkers, and nanotech safety, the same root problem repeats. Data lives in incompatible systems — electronic lab notebooks, LIMS, genomics platforms, imaging archives, manufacturing records — and never gets structured enough for AI to use well.
JÄSHA is the data infrastructure and intelligence layer that closes that gap — sitting as a decision-support layer over fragmented biotech data, never replacing scientific or regulatory judgment.
Download
The case for structured intelligence infrastructure in biotech.
Our whitepaper outlines why fragmented data — not model capacity — is the binding constraint on AI in biotech, and how a governance-first intelligence layer turns scattered records into a decision-ready substrate across manufacturing, clinical, lab, and regulatory surfaces.
Request the whitepaper ↗Whitepaper
The Case for Structured Intelligence Infrastructure in Biotech
Why fragmented data — not model capacity — is the binding constraint on AI in biotech, and how a governance-first intelligence layer turns scattered records into a decision-ready substrate across manufacturing, clinical, lab, and regulatory surfaces.
Executive Summary
Biotech and nanotech companies are not short on artificial intelligence. Off-the-shelf models can already read a protocol, summarize a study, or draft a regulatory section. What they cannot do — not because the models are weak, but because the data was never built for them — is reason reliably across a company's own manufacturing records, lab notebooks, clinical trial systems, and regulatory history.
JÄSHA Intelligence Systems was founded on a simple premise: the durable value in applying AI to biotech is not the model, it is the data infrastructure underneath it. We build governance-first intelligence systems — starting with tala.RI, our regulatory pathway intelligence platform — that structure fragmented data into a decision-ready substrate, so the people making scientific and regulatory calls can make them with a fuller, faster, more current picture. The AI never makes the call. It makes the picture legible.
Flagship_Product
tala.RI
Regulatory intelligence, grounded in your own documents
tala.RI answers regulatory questions from one company's own document library. It cites the passage behind every statement, declines what the documents do not support, and holds every answer for a named person's sign-off.
Access the demo ↗The same pathway. Far less friction.
A regulatory pathway does not shorten because a model got smarter, and we do not claim to shorten one. What changes is the work around it: the weeks spent finding the evidence, reconciling it across systems, and proving where each statement came from.
JÄSHA removes that friction between stages, while scientific and regulatory judgment stays exactly where it belongs: with the humans.
Fig. 02 — JÄSHA structures the data, not the decision
Six systems on the roadmap. Each extends the same structured-data substrate into a distinct decision-support surface across the biotech and nanotech lifecycle.
Manufacturing & CMC Readiness Intelligence
Tracks CMC readiness against clinical timelines so manufacturing scale-up doesn't quietly fall behind and cause late-stage FDA delays.
Why now — CMC gaps are a leading cause of late-stage FDA setbacks.
Real-Time Clinical Trial Oversight
Unified, near-real-time visibility into trial safety signals and endpoint data instead of only at scheduled data-lock reviews.
Why now — signal latency turns manageable risks into reported events.
Lab & Omics Data Harmonization
Pulls ELN, LIMS, genomics, proteomics, and imaging data into one structured, queryable environment — the foundation layer for the portfolio.
Why now — every downstream system depends on this structured substrate.
Evidence & Literature Intelligence
Continuous synthesis of scientific literature and competitive landscape, feeding both regulatory evidence-gathering and R&D teams.
Why now — evidence windows close faster than manual review allows.
Biomarker & Precision Medicine Intelligence
Subpopulation and biomarker-correlation support for trial design and orphan / precision-medicine strategy.
Why now — precision indications reward early stratification.
Nanotech Safety & Characterization Intelligence
Harmonizes particle characterization, toxicology, and safety-margin data across a nanomaterial's delivery routes — a white space no general-purpose biotech AI vendor currently addresses.
Why now — nanotech safety is an open category with no incumbent.
01
Governance-first
AI as decision support, never a replacement for scientific or regulatory judgment.
02
Built by operators
Founded by a biotech and regenerative-medicine operator and an engineer — not a generic dev shop.
03
Data infrastructure first
The value is in structuring the data, not just the model on top of it.
Sharon Do
Co-founder + CEO
[ Bio placeholder — Sharon's background and what she built. ]
Jaye Camposanto Andaya
PA, Co-founder
[ Bio placeholder — Jaye's background spanning clinical practice and biotech / regenerative-medicine operations. ]
Interested in a briefing?
We are working with a small number of biotech and nanotech teams ahead of general availability. Tell us what you are working on.