Governed AI for life sciences.
We help pharma and biotech teams adopt AI that produces results a scientist can validate, an auditor can trace, and a diligence team can defend.
Lucid Loop Technologies is an analytical partner for R&D, manufacturing, clinical, and quality teams in life sciences. We build AI-assisted workflows around the scientific knowledge your team already has, with IP protection engineered into the engagement and scientist validation built in from day one.
Four blockers come up in almost every conversation
Proprietary data cannot go into public AI.
Process parameters, restricted clinical data, and manufacturing IP are off limits to tools built around open data access.
Black-box outputs have no validation path.
A result a scientist cannot trace and test has no scientific value, and in a GxP environment it is a compliance problem.
Most teams are early in AI adoption.
They need a credible starting point, not a platform pitch that assumes infrastructure they have not built yet.
Critical knowledge is not ML-ready.
It lives in spreadsheets, informal SOPs, and people's heads. Generic AI tools cannot reach it.
Three segments, one governance standard
Drug development
Biotech and pharma teams in discovery, translational research, clinical development, and CMC.
Medical devices
Device developers in design iteration, bench testing, and market access.
Digital health and diagnostics
Companies deploying AI-enabled products into health systems.
Five pillars, each a prerequisite for the next
Tiered Governance
Assurance rigor is matched to what is at stake, scoped to a clear tier before work begins.
Regulatory Alignment
Each engagement is mapped to the standards that apply to your use case, and the mapping is documented.
Abstraction by Design
Sensitive data is classified and transformed before any external AI sees it. Restricted data never leaves your environment.
Controlled Traceability
Every AI-assisted output carries its inputs, the model used, who ran it, and the human disposition.
Evidence-Graded Outputs
Every inference is tagged by the strength of evidence behind it, so a tentative idea is never mistaken for a proven conclusion.
Rigor matched to what is at stake
A team exploring a new literature synthesis approach does not need the same controls as a batch disposition workflow. We scope every engagement to a governance tier up front, so you never pay for GMP-grade controls on exploratory work and never get under-engineered controls on something that reaches a regulatory submission.
Foundational decision hygiene
Early scoping, exploratory work, internal notes. No controlled-document referencing.
Controlled-document traceability
Development-stage work cited in controlled reports or QbD outlines. Our default tier.
GxP-ready assurance
Outputs reused in regulatory-facing narratives, or where QA requires stronger assurance evidence.
GMP operational use
AI outputs influence GMP outcomes such as batch disposition, real-time control, or release support.
Governed AI solutions
From the data foundation everything else depends on, through to an independent read on whether your AI story survives diligence.
Research data foundation
One governed place to ask questions of your research data, with lineage from source system to individual field.
Clinical and real-world evidence
Structured evidence from published research through to the outcomes that get a product bought and reimbursed.
Process development and design of experiments
Experimental discipline on a live manufacturing campaign, without sending process IP to a public model.
AI governance readiness review
An independent read on whether your AI story survives diligence, before someone else performs that read for you.
Engagements start small and earn the next step
Diagnose
Pick the use case with the clearest value, set the data boundary, agree the governance tier, and define the test of success in writing.
Pilot
One scoped build, provenance on every output, and a pass or fail against the test agreed in stage one.
Scale
Extend to adjacent programs and functions. The governance carries forward. The team is sized to the roadmap rather than to a contract.
Governed AI on a live manufacturing campaign
A clinical-stage pharmaceutical company moved a proven batch process to inline homogenization with new equipment arriving in days. In six weeks we delivered an evidence-graded constraint map and an adaptive design of experiments that ran live alongside their campaign. Their scientists made every scientific call. No proprietary data reached an external model.
The governance that makes an AI result safe for a scientist to act on is the same governance that makes a company's AI story hold up under outside diligence.
When a company heads toward a financing, a partnership, or an exit, an ungoverned AI story reads as a risk. A governed one reads as an asset under control.
Put AI to work without putting compliance at risk.
Tell us the use case you care about most and we will scope a governed engagement around it.