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Industry — 06

Pharma & Life Sciences

Traceability is worth more than speed.

of outputs cite a source
%100of outputs cite a source
literature screening time
-%70literature screening time
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In pharma every output must trace back to a source. So the systems we build here are a chain of evidence first and automation second: no text is usable unless you can say which document, which version and which date it came from.

In short

Where is AI used in pharma and life sciences?

AI in pharma is used in four places: scientific literature screening with evidence-backed summaries, classification of adverse event reports, consistency checking across regulatory dossiers, and visual quality inspection in manufacturing. The same condition applies to all four: every generated sentence must carry which document and which version it came from. Neuros does not let output that cannot be traced to a source pass through the system.

What's hard in this sector

Traceability is worth more than speed.

01

Chain of evidence

Nothing submitted to a regulator can be sourceless; a model that cannot cite has no place here.

02

Validated systems

In GxP scope every change requires validation; continuous delivery has to be designed around that constraint.

03

Literature volume

There is more literature than any team can read; unscreened knowledge is unused knowledge.

What we can build for you

  • 01Scientific literature screening with grounded summarisation
  • 02Pharmacovigilance: classification of adverse event reports
  • 03Regulatory dossier preparation and consistency checking
  • 04Clinical trial data standardisation and quality testing
  • 05A knowledge assistant for medical affairs teams
  • 06Visual quality control and batch traceability in manufacturing

Sources

  1. 01Regulation (EU) 2024/1689 — Artificial Intelligence ActAvrupa Birliği Resmî Gazetesi · 2024
  2. 02NIST AI 600-1 — Generative AI ProfileNIST · 2024
  3. 03ISO/IEC 42001:2023 — Yapay zekâ yönetim sistemiISO/IEC · 2023

Frequently asked

Questions we get asked

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Such output never leaves the system. In pipelines built by Neuros the model cannot write a citation freely; it may only reference retrieved document chunks, and every reference is verified against a document id and character range. A sentence that fails verification is not shown to the user — it is logged as a failure and added to the evaluation set as a new test case.

The model version is treated as a configuration item: it is pinned, changes go through change control, and for every version the evaluation set is re-run and the result filed. Neuros never auto-applies a model upgrade; a new version first runs in shadow mode against the current one, the difference is reported, and it goes live only after approval.

Coverage is bounded by the sources it is connected to — the model reads from the databases you define, not from its own memory. Neuros wires the screening pipeline to your subscribed publication sources and internal archive, tagging every result with date and version. A study that is not found is never silently skipped; the coverage report states which source was searched over which date range.

Other industries

Let's assess your sector specifically

This page is the general frame. Let's find the actual bottleneck in your operation together, in a 30-minute call.

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