Capability
AI / ML for evidence
Generative AI only matters in our world when it is grounded, auditable, and wired into how clinical and access teams already work. We design agentic workflows—retrieval, review loops, and guardrails—that accelerate evidence tasks without trading away rigor.
Most life-sciences organizations have run pilots; fewer have production workflows that compliance, IT, and medical leaders trust for decisions that matter. We focus on narrow, document-grounded use cases—literature synthesis, diligence support, protocol consistency, internal Q&A—where accuracy, provenance, and audit trails are non-negotiable.
The capability gap
Clinical R&D and market access teams produce and consume enormous document volume: protocols, SAPs, CSRs, labels, HTA modules, and diligence data rooms. Generic large-language-model chat interfaces create enthusiasm but rarely survive questions about hallucination, data leakage, version control, and 21 CFR Part 11–adjacent expectations. Meanwhile, enterprise IT rightly insists on identity, logging, and data residency before any workflow touches confidential development data.
The opportunity is not “AI for everything.” It is targeted automation where retrieval-augmented generation, structured extraction, and human review loops compound: faster turnaround on repetitive synthesis, tighter consistency across related documents, and scalable first drafts that experts edit rather than author from zero. We help you choose those targets and implement them with the same seriousness as a clinical system implementation.
Where we focus
Document-grounded retrieval and Q&A
We design internal copilots over curated corpora—IBs, submission sections, trial master files, diligence indexes—with citation back to page and version. Patterns include controlled vocabulary for queries, chunking strategies tuned to regulatory prose, and escalation to human SMEs when confidence scores or contradiction checks fail.
Literature and competitive intelligence workflows
Structured screening, extraction templates, and dual-review paths reduce the labor of systematic reviews and landscape analyses without eliminating accountability. We emphasize provenance: which model version, which prompt template, which human sign-off—so medical and legal can stand behind outputs used in submissions or BD materials.
Authoring assistance and consistency checks
We scope automation for cross-referencing inclusion criteria across synopsis, SoA, and statistical sections; flagging undefined abbreviations; and generating first-pass tables from structured trial data where APIs exist. The goal is to shrink cycle time on mechanical tasks, not to remove medical writers or biostatisticians from judgment calls.
Governance, validation, and operating model
We help you articulate what “good enough” means for internal versus external-facing use, how to log and audit model interactions, and when workflows belong on vendor platforms versus on-prem or VPC deployments. We align with your compliance and IT stakeholders early so pilots do not collapse at scale-up.
What we deliver
- Workflow specifications: user roles, data boundaries, retrieval architecture, and QC checkpoints.
- Prompt and RAG design iterated on your real documents—not generic pharma samples.
- Pilot plans with success metrics (time, error rates, SME burden) and kill criteria.
- Integration guidance for document management, SSO, and logging aligned to enterprise standards.
- Training and playbooks for medical writers, clinicians, and analysts adopting new tools.
- Executive-ready summaries of risk, cost, and maintenance for scaled deployment.
Outcomes you can expect
- Fewer abandoned pilots and clearer criteria for what graduates to production.
- Measurable reduction in analyst and writer time on well-defined repetitive tasks.
- Stronger confidence from compliance and IT because architecture and logging were designed in, not retrofitted.
- A portfolio view of AI investments tied to evidence and access outcomes—not novelty for its own sake.
How we work
Our engineering background and clinical R&D experience mean we build for maintainability and trust, not demo polish. We prioritize narrow, high-friction workflows over generic chatbots, and we stay skeptical of outputs until tested on your edge cases—unusual visit windows, redacted CSRs, multi-arm combination trials.
lotor lab treats AI like any other evidence asset: useful only when traceable, governed, and tied to decisions. We work alongside your digital, data science, and medical teams; we do not replace internal ownership of validation and vendor management.
When teams bring us in
- Portfolio or TA teams overwhelmed by literature, congress, and competitive intelligence throughput.
- BD and diligence with compressed timelines and large, heterogeneous data rooms.
- After a stalled pilot, when leadership needs a credible path to production or a disciplined stop decision.
- When building an internal “evidence copilot” roadmap across medical, regulatory, and access use cases.