Excelra’s Innovation Engine
40+ AI solution accelerators.
Governed architecture with HITL.
Working prototypes in 2–4 weeks.
Build your AI-PilotExplore the portfolio
40
AI solution accelerators across five capability areas
8
live in production with clients
2–4
from conversation to a working prototype
Pilots impress in a demo.
Systems have to survive an inspection.
That is the gap most R&D AI falls into. Every output from the lab passes the same five gates before anyone relies on it.
1
Rules decide first
Where a rule can decide, it does. AI handles only what rules cannot.
2
AI proposes
Grounded in your data and curated sources, and it flags when the evidence runs out.
3
Eval gate
The evaluation set exists before the build starts. Every release passes it, and it keeps running in production.
4
A scientist approves
Human review in four risk classes. The domain expert owns the final output, always.
5
Provenance logged
Source, page, sentence and model version for every value in an audit trail.
“We had sat through plenty of AI demos. The AI Lab gave us a working
prototype on our own data in three weeks, with every answer linked to its
source and our scientists signing off. That is what got it past QA.”
P
Prashant
Manager, Company XYZ
Governance is what makes AI usable in R&D.
Here is where teams put it to work first.
Where teams start
Two solutions where the time saved shows up in the first month.
Regulatory response intelligence
Health-authority questions arrive on a fixed clock, and the evidence sits across years of past submissions.
Finds the evidence behind each question in your own dossier and precedent, then drafts a sourced response for your regulatory writer to review and approve.
~50%
less drafting effort per response
10 → 4–5
hrs per question
hrs per question
~2,000
active users
Study design intelligence
The hardest design decisions are made by hand across thousands of prior protocols, then argued over again later.
Recommends eligibility criteria, endpoints, the assessment schedule and the statistics, drawn from your
own trial history.
Every recommendation comes with a reason, a source and a match score.
From rapid prototype to production
A selection of the 40+ accelerators. Pick the part of R&D you work in, then choose a solution to see what it does.
★ Has its own page.
Why it works at Excelra
The lab is how Excelra delivers AI, not a unit beside it.
The data
20+ years of curated scientific data
Grounding only works if the source is clean. GOSTAR, biomarker, multi-omics and clinical outcomes data are the substrate every solution runs on.
The architecture
One reference architecture, reused
Grounding, provenance, risk-based human review, an evaluation harness and an audit log. Assembled per use case, never rebuilt. Designed to FDA–EMA good AI practice, EU AI Act Art. 14, the GAMP AI Guide and ALCOA+.
The lab
Scientists and engineers, one team
A client conversation becomes a working prototype in 2–4 weeks, benchmarked against your current baseline before anyone is asked to fund it.
80
accuracy on AI-extracted metadata, every record scientist-reviewed
60–70
less manual curation time
~50
less drafting effort per health-authority question
87
recall on biomarker extraction from literature
Measured on client work and in Excelra’s own delivery, not modelled.
Every answer has a source.
Every decision owned.
Tell us the problem. A scientist and an engineer will look at it with you, and tell you plainly whether AI is the right answer for it.
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