Enterprise AI for Life Sciences
Most enterprise AI searches the collaboration layer well. Sinequa reaches the validated systems beneath it – Veeva Vault, Documentum, your LIMS and ELN – so your teams get answers grounded in the regulated record, not the conversation about it.
Backed by independent recognition from Gartner, Forrester, and SPARK Matrix™ and by results in the world’s most regulated, documentation-heavy enterprises.
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Faster discovery cycle time
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Improvement in R&D capital efficiency
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Saved with Phase 1/2 pass/fail ratio improvements
NEW STUDY
The Forrester Total Economic Impact™ Of Sinequa AI-Powered Search
An independent Forrester Consulting study, built on a composite $20 billion enterprise, quantifies what a governed enterprise search and AI layer returns over three years: 299% ROI, $22.4M net present value, and payback in under six months. Based on interviews with five decision-makers across chemicals, manufacturing, life sciences, transport, and aerospace and defense.
Sinequa is simply great technology. We immediately saw its benefit watching it perform something we didn’t know was possible. It makes an exponential difference for our organization. We were also impressed with the number of smart connectors available out-of-the-box and Sinequa’s unique ability to develop new ones.
Oliver Thoennessen, Senior Manager Global IT Drug Development, UCB
In life sciences, the answers your teams need don’t sit in chat threads and shared docs. They live in the effective SOP, the approved specification, the as-submitted version, the study report, the ELN entry, the batch record.
That knowledge lives in Veeva Vault, Documentum, LIMS, ELN, eTMF, and RIM — systems most enterprise AI tools were never built to fully index. And your AI is only as useful as the systems it can actually reach inside.
Sinequa reaches the validated systems of record where your scientific and regulated knowledge actually lives with lifecycle state, version, and permissions intact, not just the collaboration and office tools your teams talk in.
Demos and connector checklists rarely surface the differences that decide a deployment in a regulated, PLM-heavy environment. These are the questions that do.
Veeva Vault, Documentum, LIMS, ELN, eTMF, and RIM are the systems of record for regulated and scientific work. A single native connector scoped to steady-state quality documents can’t reach the in-flight documents, submissions, and lab data your teams actually work in.
A results table inside a CSR or protocol should be retrievable without knowing the filename. Multimodal indexing extracts tables, structure, and metadata from study reports, protocols, and scanned legacy documents so they’re directly searchable.
A compound carries a code name, a generic name, and a brand name. Entity and ontology-aware retrieval resolves all three to the same evidence, and admins can load company dictionaries to keep it accurate.
On-premise, VPC, or fully air-gapped. Model-agnostic. For teams with GDPR and data-residency obligations, trial-subject and HCP personal data in eTMF and safety content should never route through a cloud subprocessor it shouldn’t.
When access is revoked in the system of record, retrieval should stop returning that content at the user’s next query, not on the next periodic crawl. ALCOA+ applies to how content is retrieved, not just how it’s stored.
Every answer cited and linked to the effective, permission-checked document in its system of record: one click from claim to source. In a GxP context, an answer you can’t audit doesn’t exist.
These requirements are drawn from the patterns we see across the industry’s most regulated deployments. Read the State of Enterprise Agentic AI 2026 →
See how Sinequa indexes the validated systems where your scientific knowledge actually lives — Veeva Vault, Documentum, LIMS, ELN, eTMF, and RIM — and returns answers, cited to the effective document, that a scientist or QA lead can act on. Ready to see it in your environment?
Book a custom demoIn regulated science, a wrong answer isn’t a minor inconvenience. It’s a deviation, a compliance gap, or an inspection finding.
That knowledge lives in Veeva Vault, Documentum, LIMS, ELN, eTMF, and RIM — systems that reach far past steady-state quality documents, and that general-purpose AI tools were never built to fully understand.
Sinequa has native depth into the systems where regulated science lives:
Not ready for a demo yet?
Beyond the hype, what does agentic AI actually look like inside a $5B+ revenue organization? This research explores the gap between “Agent-Washing” and the realities of deploying AI agents in complex, regulated, and legacy-heavy environments — exactly the ones Sinequa is built for.
