IntuitionLabs
AI Technology Vision

TetraScience AI Integration & Scientific AI Agents

Empowering pharmaceutical and life science organizations with cutting-edge AI solutions.

AI on Harmonized Scientific Data

Most AI projects in pharma R&D get stuck on data preparation. The Tetra Data Platform removes that blocker by harmonizing instrument and assay data into open IDS schemas before agents touch it. Our role is to build the agents that consume IDS records and deliver scientist-facing capabilities — peak review, anomaly detection, run summarization — grounded in real harmonized data rather than scraped exports.
AI agents reasoning over harmonized Tetra Data Platform IDS records

Governed by Default, Not by Afterthought

Every prompt, retrieval, and tool call is logged to a dedicated audit trail. Permissions inherit from the Tetra role model, so scientists only see records they are already authorized to access. This aligns with FDA Data Integrity guidance and the MHRA GxP data integrity guidance applied at the AI access layer.
Audit trail and governance for AI agents on Tetra Data

Model-Agnostic Architecture

We route all text generation through the IntuitionLabs AI proxy which supports Claude, Gemini, and OpenAI endpoints. Model choice is a configuration decision per workflow, not a rewrite — and we maintain evaluation harnesses so model upgrades are reviewed scientifically before rolling out.
Model-agnostic AI architecture for TetraScience

AI Capabilities We Build on TetraScience

Chromatography QC Review

Agents read Empower or OpenLab IDS records, perform first-pass peak review, flag system suitability deviations, and draft narratives for analyst review. Cuts routine review time substantially while preserving e-signature integrity.

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Cross-Instrument Anomaly Detection

AI reviews incoming plate reader, mass spec, and bioprocess data for outliers, drift, and edge effects before results reach scientists. Findings surface to lab leads with suggested actions and links back to source IDS records.

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Bioprocess Run Summarization

Sartorius BIOSTAT and Ambr runs summarized in natural language with KPI trajectories, deviation flags, and comparator runs. Useful for daily standups and tech-transfer to CMOs without manual data crunching.

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Method Transfer Narratives

AI drafts analytical method transfer reports from harmonized historical injections — same column, same gradient, different site — flagging differences in retention time, peak shape, and resolution.

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ELN Pre-Population

Agents pre-populate Benchling or LabArchives ELN entries from Tetra IDS records — instrument metadata, raw results, and standard table layouts already filled in for scientist review and signature.

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Stability & Trend Analysis

Long-running stability studies and forced degradation series harmonized through Tetra and analyzed by AI for trend deviations. Especially valuable for CMC teams managing dozens of concurrent stability programs.

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Reference Architecture for TetraScience AI

Our reference architecture has three layers: a governed data plane where Tetra Data is accessed through the Tetra Data Platform API, an MCP layer that exposes scoped tools to agents with authentication and audit, and a scientist-facing surface inside Benchling, ELN, Slack, or a custom UI. This separation is what keeps the system validatable, upgradable, and vendor-agnostic across model providers.

Data Plane

Wraps the Tetra Data Platform API with caching, retry, and audit logging. Single controlled entry point for AI agents to retrieve IDS records and raw files.

MCP Layer

Exposes scoped tools (search, retrieve, summarize, draft) to LLMs with strong authentication, per-tool permissions, and full prompt/response logging for regulated review.

Scientist Surface

Agents reach scientists inside Benchling, Vault, Slack, Teams, or custom UIs. Outputs always cite source IDS records and link back into the native Tetra views.

Why Customers Choose IntuitionLabs for TetraScience AI

Scientific Data Fluency

We speak chromatography, mass spec, and bioprocess. Your scientists do not need to teach us instrumentation before the work can start.

Validation Built In

Every AI capability ships with a GAMP 5 validation package — URS, risk assessment, test scripts, and VSR — not a retrofitted approval at the end.

Veeva Ecosystem Experience

Proven patterns for moving Tetra-harmonized data into Veeva Vault Quality and Vault RIM, where most enterprise scientific AI workflows eventually need to land.

AI Proxy & Multi-Model

One proxy, three providers, clear spend controls. No lock-in, no rewrites when a better model ships six months from now.

Evaluation-First Engineering

We build evaluation harnesses before rolling out features. You see correctness and drift metrics, not vibes.

Pharma-Native Delivery

We work the way regulated IT works. Change control, release notes, periodic review, and inspection-ready artifacts from day one.

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Guardrails We Apply to Every AI Agent

Authenticated Identity
Every prompt is tied to a named human user. Service accounts are not used for scientist-facing workflows. Aligns with Part 11 access control expectations and with NIST authentication guidance.
Scoped Tool Access
Each agent tool has explicit read or write scope. Write tools require human-in-the-loop confirmation. Prevents the common failure mode of an agent silently editing GxP records.
Full Audit Trail
Every prompt, retrieval, and tool call logged immutably with user, timestamp, and result. Meets Part 11 audit trail expectations and supports inspection readiness end-to-end.
Evaluation Harness
Gold-set tests run on every model and prompt change. Correctness and drift metrics published per release so regressions are caught before they reach scientists.
Citation Required
Answers must cite the source IDS records they are grounded in. Hallucination risk is reduced and scientist trust increases because every claim is traceable back into Tetra.
Periodic Review
Annual review of AI components per the GAMP 5 Second Edition lifecycle — prompt templates, model versions, evaluation results, and incident logs.

Frequently Asked Questions

It means connecting LLM-powered agents to the Tetra Data Platform so scientists can query and reason over harmonized instrument and assay data in natural language. We build this using the Model Context Protocol (MCP) as the connective tissue between agents and the Tetra Data Platform API. The outcome is not a chatbot bolted onto a database — it is a governed capability that respects access control, records every query in an audit trail, and is validated for regulated use.
As of April 2026, TetraScience has not published an official MCP server. We build customer-specific MCP servers that wrap the Tetra Data Platform API with scoped tools — search records, retrieve IDS payloads, summarize bioprocess runs, draft QC narratives. The MCP spec itself is an open standard from Anthropic — see the MCP GitHub organization — so building a compliant, reusable MCP server for TetraScience is well-trodden engineering work. We expect official vendor MCP servers across the scientific data space during 2026-2027 and we design our wrappers so they can migrate cleanly when those ship.
TetraScience explicitly markets itself as a "Scientific AI" platform — see tetrascience.com — and the design choices reflect that positioning. Open IDS schemas mean LLMs reason over a documented, deterministic data model rather than vendor-specific binary formats. The Tetra API exposes structured queries, file content, and metadata uniformly across instruments. This is what AI-readiness actually means in practice: data that an agent can retrieve, ground in, and explain without bespoke parsers per instrument family.
We apply the same controls to AI agents that we apply to any other computerized system in scope for 21 CFR Part 11: authenticated access tied to a named user, role-based permissions, full audit trail of every prompt and tool call, no unreviewed writes into GxP records, and periodic review. We follow ISPE GAMP 5 risk-based validation for AI components and align with the FDA January 2025 AI draft guidance and the EMA AI reflection paper.
The six use cases we see most are: (1) natural-language QC review on chromatography results, (2) cross-instrument anomaly detection on assay data, (3) bioprocess run summarization and comparator search, (4) automated narrative drafting for analytical method transfer, (5) AI-assisted ELN entry generation grounded in instrument data, and (6) trend analysis on stability and forced degradation studies. We treat each as a separate validated capability rather than one monolithic AI system, which keeps validation scope tight and failure modes isolated.
All our TetraScience AI work routes through the IntuitionLabs AI proxy which supports Anthropic Claude, Google Gemini, and OpenAI models. Customers can pin model choice per workflow — for example, high-reasoning models for chromatographic peak interpretation and cheaper, faster models for bulk metadata enrichment. We never send GxP data to consumer LLM endpoints; production traffic runs on enterprise endpoints with documented data processing addenda.
By default our agents run read-only against the Tetra Data Platform — IDS records, raw files, and metadata are read-only resources. Writes target adjacent surfaces — draft Vault QualityDocs entries, scratch Benchling assay templates, ELN drafts — through governed APIs with explicit human approval. Direct writes to IDS records are not a pattern we recommend; the Tetra Data Platform is a system of record for harmonized scientific data, and unaudited mutation would compromise the integrity guarantees that make it valuable.
Waters Empower is a primary source of GxP-scope chromatography data in QC and bioanalytical labs, and Tetra Integrations expose Empower injections, methods, and chromatograms as IDS records. We build agents that perform first-pass peak review, flag system suitability deviations, and draft narrative explanations — all read-only and grounded in the IDS payload. Final review and e-signature stay with the analyst, but the prep work that consumes most of their time is automated. See the FDA Data Integrity guidance for the controls we preserve.
Yes. Bioprocess runs from Sartorius BIOSTAT or Ambr platforms generate dense time-series data harmonized into Tetra IDS. We build agents that retrieve historical comparator runs, summarize KPI trajectories, and draft tech-transfer narratives. Especially useful for CMO handoffs where institutional memory is locked in a small number of subject-matter experts. All outputs are cited and reviewed by a process scientist before use in regulated documentation.
We design every AI deployment around data minimization. Agents retrieve only the IDS fields needed for the task, model providers operate under enterprise data processing agreements with no training on customer data (see for example Anthropic commercial terms and Google Cloud DPA), and sensitive fields can be redacted before prompting. For EU programs we comply with GDPR and the incoming EU AI Act obligations.
TetraScience is building AI capabilities natively into the platform — see the platform page and recent product announcements. Our work is complementary: cross-system workflows spanning Tetra plus Benchling plus Veeva plus a lakehouse, custom agents tuned to your specific assay portfolio or therapeutic area, and bespoke validation packages. If vendor-native features cover your use case, we configure those. If they do not, we build the gap and keep it ready to retire when the vendor catches up.
A single validated use case — for example, Empower QC review on a single product line — takes roughly 10 to 14 weeks end-to-end: two weeks of design, six to eight weeks of build and evaluation, and two to four weeks of validation and rollout. Broader programs covering three to six use cases usually span six to nine months including managed hypercare and a continuous evaluation harness. We always start narrow because narrow projects ship.
Bring AI to Your Tetra Data
Bring AI to Your Tetra Data image

Bring AI to Your Tetra Data

From MCP servers for the Tetra Data Platform to QC review automation and bioprocess narratives, we build compliant AI workflows scientists actually use. Explore TetraScience services or GxP validation.

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