IntuitionLabs
AI Technology Vision

Dotmatics AI Integration & MCP Agents for R&D

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

AI Where Scientists Already Work

Adoption fails when AI lives in a separate tab. We embed AI capability inside the Dotmatics surfaces scientists already use — query results, ELN entries, assay dashboards — so the path from question to answer is one click, not a context switch. See the Model Context Protocol spec for the pattern we use to bridge agents and scientific systems.
AI-enabled scientific workflow inside Dotmatics

Governed by Default, Not by Afterthought

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

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 Dotmatics

AI Capabilities We Build on Dotmatics

Natural-Language SAR Triage

Scientists ask "show me recent compounds under 5 nM with no hERG flag from program X" and get a ranked, cited list drawn from Luma registration and assay data — with links back into the native Dotmatics views.

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ELN Search & Summarization

Retrieve and summarize LabArchives or Luma ELN experiments across projects, sites, and years. Useful for onboarding, tech transfer, and regulatory inquiry — every summary is grounded with a link to the source experiment.

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Assay QC & Anomaly Detection

AI reviews incoming plate-level assay data for outliers, drift, and edge-of-plate effects before results publish. Findings surface to lab leads with suggested actions, reducing downstream data integrity findings.

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Prism Figure Automation

Agents read ELN and assay outputs, map them into validated GraphPad Prism templates, and draft dose-response, survival, or comparative plots. Scientists review and finalize, cutting figure prep time from hours to minutes.

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Sequence Retrieval & QC

Agents retrieve constructs and sequences from Geneious and SnapGene, check against registered biologics in Dotmatics, and flag conflicts. Useful for rapid review cycles in antibody and cell/gene therapy pipelines.

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Bioprocess Recipe Mining

IDBS Polar recipes and historical bioprocess runs become searchable in natural language. Process scientists retrieve analogous runs, compare titer profiles, and accelerate tech-transfer packages to CMOs.

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Reference Architecture for Dotmatics AI

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

Data Plane

Wraps Luma, Elements, LabArchives, Geneious, and IDBS APIs with caching, retry, and audit logging — the single controlled entry point for AI agents.

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 Dotmatics, Slack, Teams, or custom UIs. Outputs always cite source records and link back into the native Dotmatics views.

Why Customers Choose IntuitionLabs for Dotmatics AI

R&D Informatics Fluency

We speak medicinal chemistry, biologics, and bioprocess. Your scientists do not need to teach us the domain 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 data between Dotmatics and Veeva Vault, which is where most enterprise AI R&D 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.
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 Dotmatics records they are grounded in. Hallucination risk is reduced and scientist trust increases because every claim is traceable.
Periodic Review
Annual review of AI-adjacent 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 Dotmatics stack — Luma, LabArchives ELN, GraphPad Prism, SnapGene, Geneious, IDBS E-WorkBook — so scientists can query, retrieve, and summarize R&D data in natural language. We build this using the Model Context Protocol (MCP) as the connective tissue between agents and Dotmatics APIs, which gives us a vendor-neutral, auditable bridge. 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, Dotmatics has not published an official MCP server for Luma. We build customer-specific MCP servers that wrap the Luma and Elements REST APIs documented at the Dotmatics developer portal. The MCP spec itself is an open standard from Anthropic — see the MCP GitHub organization — so building a compliant, reusable MCP server for Dotmatics is well-trodden engineering work. We expect official vendor MCP servers across the R&D informatics space during 2026-2027 and we design our wrappers to migrate cleanly if and when they ship.
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-adjacent components and keep up with the FDA January 2025 AI draft guidance and the FDA AI program.
The six use cases we see most are: (1) natural-language SAR triage for medicinal chemistry, (2) ELN search and summarization across LabArchives and Luma, (3) assay QC anomaly detection, (4) automated GraphPad Prism figure drafting from ELN data, (5) sequence and construct retrieval across Geneious and SnapGene, and (6) bioprocess recipe mining on IDBS Polar. 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 Dotmatics 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 SAR triage and cheaper, faster models for ELN summarization. We never send GxP data to any consumer LLM endpoint; production traffic runs on enterprise endpoints with documented data processing addenda.
Yes but carefully. By default our agents run read-only against GxP-scope records — ELN experiments, registered compounds, approved assay results. Writes are gated behind explicit human approval workflows and only target non-GxP draft areas such as scratch workbooks, curation queues, or draft Prism projects. Any write path carries its own validation package and is mapped to the relevant Part 11 signature and audit requirements. We never allow unaudited auto-writes into approved experiments.
Prism is desktop software but ships standardized XML project files. We build AI workflows that generate Prism-ready tables from ELN data via APIs, auto-populate validated Prism templates, and draft dose-response or survival analyses that scientists finalize. See the Prism user guide for template structure. The result is hours of Excel preprocessing replaced with minutes of review, and a traceable pipeline from ELN entry to submission-ready figure.
Yes. We connect agents to SnapGene file formats and the Geneious REST API to retrieve and explain constructs, flag restriction-site conflicts, suggest optimizations, and cross-check biologics candidates against registered records. For antibody work, Geneious Biologics exposes pipelines for humanization and liability analysis that pair well with LLM-based triage. All suggestions flow through scientist review before any registration or patent-relevant action.
We design every AI deployment around data minimization. Agents retrieve only the 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.
Dotmatics is building AI natively into Luma and we support those capabilities where they fit — see the Luma product page and the Dotmatics news archive for current features. Our work is complementary: cross-system workflows spanning Luma plus Benchling plus Veeva, custom agents tuned to your chemistry or biology ontology, 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, SAR triage on Luma — 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. We scope each use case independently so scientists see working value inside a quarter. Broader programs covering three to six use cases usually span six to nine months, including managed hypercare and a continuous evaluation harness.
We baseline time-to-insight metrics before rollout — hours scientists spend per SAR review cycle, per assay QC pass, per Prism figure — and track them through rollout. Typical outcomes are 40-60% reduction in routine triage time and a meaningful reduction in downstream rework caused by data quality issues. We report quarterly against the baseline along with quality metrics like correctness rate on evaluation sets and user satisfaction. See related FDA discussion paper on AI/ML in drug manufacturing for regulator expectations on AI metrics.
Bring AI to Your Dotmatics Stack
Bring AI to Your Dotmatics Stack image

Bring AI to Your Dotmatics Stack

From MCP servers for Luma to Prism figure automation and biologics triage agents, we build compliant AI workflows that scientists actually use. Explore Dotmatics services or GxP validation.

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