TetraScience Consulting & Integration for Pharma R&D
Empowering pharmaceutical and life science organizations with cutting-edge AI solutions.
Services Across the TetraScience Platform
Tetra Data Platform Setup
Stand up the Tetra Data Platform on AWS or vendor-managed tenancy, configure tenant security, RBAC, and audit, and integrate with your enterprise SSO and key management. Validated configuration baseline ready for production.
Tetra Integrations Rollout
Deploy and qualify pre-built integrations for chromatography CDS, mass spec, plate readers, ELN, LIMS, and bioprocess instruments. Multi-site rollout playbooks tailored to instrument fleet inventory.
Custom Tetra Pipelines
Python-based Tetra Pipelines for domain-specific data transformations: peak review, plate normalization, ELN reconciliation, and AI-ready feature extraction. CI/CD with unit and integration tests.
IDS Schema Engineering
Design, version, and govern Intermediate Data Schemas across instrument and assay families. Schema contracts treated like production APIs with deprecation policy and migration tooling.
Downstream Activation
Land Tetra Data into Benchling, Veeva Vault, Databricks, Snowflake, and ELN/LIMS systems with governed pipelines. Make harmonized scientific data first-class in your enterprise stack.
GxP Validation
Full GAMP 5 Second Edition validation packages for the Tetra Data Platform deployment, custom pipelines, and AI consumers. Inspection-ready artifacts aligned to Part 11 and Annex 11.
Why Tetra Data Matters for Pharma R&D
The single biggest blocker to scientific AI in pharma is messy, siloed instrument and application data trapped in proprietary formats. TetraScience solves this with a managed, cloud-native platform — 300+ pre-built integrations, open IDS schemas, and a Python pipeline runtime. The result is AI-ready data flowing across Benchling, Veeva Vault, and lakehouses without years of bespoke ETL.
Built for Open Science
TetraScience publishes IDS schemas openly and aligns with the FAIR data principles championed by industry bodies including the Pistoia Alliance. This open posture is what makes the platform safe to bet a multi-year R&D data strategy on — schemas are transparent and migratable rather than vendor-locked black boxes.
Fits in Regulated Environments
The Tetra Data Platform supports the technical controls required by 21 CFR Part 11 and EU GMP Annex 11. We layer a validated configuration on top using ISPE GAMP 5 methodology so the platform is inspection-ready from day one — see our TetraScience compliance and validation service for the full approach.
Core Capabilities We Deliver on TetraScience
Platform Deployment
Tenant setup on AWS, networking, RBAC, SSO integration, audit configuration, and validated baseline. Production-ready Tetra Data Platform deployment with documented control mapping for inspection.
Learn moreInstrument Integration
Empower, OpenLab, Chromeleon, plate reader, mass spec, and bioprocess instrument onboarding using pre-built Tetra Integrations or custom connectors built with the Tetra SDK and Connector framework.
Learn morePipeline Engineering
Python-based Tetra Pipelines for harmonization, enrichment, and AI-ready feature extraction. CI/CD with unit tests, integration tests, schema contract checks, and validated release process.
Learn moreIDS Schema Governance
IDS schema design, versioning, deprecation, and migration tooling. Schemas treated like production APIs so downstream consumers — ELN, LIMS, lakehouse, AI — see deterministic contracts.
Learn moreDownstream Activation
Tetra Data delivered into Benchling, Veeva Vault, Databricks, Snowflake, and ELN/LIMS via governed integration pipelines. Includes data contract definition, monitoring, and alerting.
Learn moreAI & Scientific Intelligence
LLM-powered assistants connected to Tetra Data via MCP and the Tetra API. Natural-language scientific queries with audited, scoped access aligned to your role-based security model.
Learn moreUse Cases We Build on TetraScience
Empower QC Automation
Empower CDS results harmonized into IDS, peak review automated with rule and AI-driven flagging, validated handoff to Vault QualityDocs.
Plate Reader Throughput
High-content plate reader fleets delivered into Benchling assay results in near real time with normalization and QC enrichment built into the pipeline.
Bioprocess Data Lake
Sartorius BIOSTAT and Ambr runs harmonized into a Databricks lake with FAIR-aligned metadata for cross-program tech transfer and CMC analytics.
Cross-CRO Aggregation
Tetra-harmonized data from multiple CROs aggregated into a single sponsor data lake so analytics teams stop building bespoke ETL per partner.
Mass Spec for Biologics
Intact and peptide mass spec data normalized and indexed for biologics characterization, with link-back to Geneious Biologics records and Vault submissions.
AI-Ready Training Sets
Curated training datasets for property prediction and assay QC models drawn from harmonized IDS records — replacing bespoke per-project ETL pipelines.
Our Engagement Model for TetraScience Programs
TetraScience programs fail for the same reasons most enterprise data programs fail: scope is too broad, validation is bolted on at the end, and instruments are integrated faster than the downstream consumers can absorb. Our engagement model fixes all three. Work ships in quarterly slices, validation is embedded from discovery, and every instrument family rollout is paired with a downstream consumer commitment so harmonized data does not sit unused. We follow GAMP 5 Second Edition from kickoff.
Discovery & Roadmap
Two to four week sprint to inventory instruments, software, validation scope, and AI use cases. Output is a prioritized rollout plan with cost ranges and quarterly milestones.
Iterative Delivery
Tetra Integrations rollout, custom pipelines, and downstream activation run in parallel quarterly slices. Each slice ships validated data flow before the next starts.
Operate & Evolve
Hypercare transitioning into managed services: pipeline upgrades, IDS migrations, AI agent expansion, and continuous integration with Veeva, Benchling, and lakehouse platforms.
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Standards, Regulations, and Guidance We Align To
21 CFR Part 11
FDA electronic records and signatures rule. Foundational for any TetraScience GxP deployment in scope for FDA-regulated activity.
EU GMP Annex 11
European equivalent governing computerized systems in GMP. Required for any TetraScience deployment supporting EU manufacturing or QC programs.
GAMP 5 Second Edition
ISPE risk-based framework for computerized system validation. We deliver Tetra Data Platform validation packages aligned to GAMP 5 with category-appropriate rigor per component.
FAIR Data Principles
Findable, Accessible, Interoperable, Reusable. The international baseline for scientific data strategy in pharma R&D, directly served by Tetra IDS schemas and APIs.
ICH Q9(R1)
Quality risk management updated 2023. We use ICH Q9 to scope validation effort, prioritize integrations, and justify testing depth for each Tetra pipeline.
ALCOA+ Data Integrity
MHRA and WHO data integrity principles. We design Tetra pipelines to preserve attribution, contemporaneity, and integrity from instrument to consumer.
Frequently Asked Questions
TetraScience is the Tetra Data Platform (TDP), a cloud-native scientific data and AI platform purpose-built for life sciences R&D. It ingests data from hundreds of lab instruments and scientific software systems, harmonizes it into open Intermediate Data Schemas (IDS), and delivers AI-ready, FAIR-aligned data to downstream consumers. For pharma and biotech IT leaders, TetraScience matters because it solves the single biggest blocker to scientific AI: messy, siloed instrument and application data trapped in proprietary formats.
We deliver end-to-end services across discovery, platform setup, Tetra Integrations rollout, custom pipeline development, AI use case enablement, and GxP validation. Our team helps customers stand up the Tetra Data Platform on their AWS or vendor-managed tenancy, deploy Tetra Integrations for instruments and applications, build custom pipelines using the Tetra Developer SDK, and integrate Tetra Data with Benchling, Veeva Vault, Databricks, and Snowflake. We also build AI agents on top using the Model Context Protocol so scientists can query harmonized scientific data in natural language.
TetraScience publishes 300+ pre-built Tetra Integrations spanning chromatography (Waters Empower, Agilent OpenLab CDS, Thermo Chromeleon), mass spectrometry, plate readers, ELN/LIMS (Benchling, LabWare LIMS, IDBS E-WorkBook), bioprocess (Sartorius BIOSTAT), and analytics platforms. The current catalog is at tetrascience.com/integrations. We extend the catalog with custom connectors when needed using the Tetra Connector framework.
Tetra Data Pipelines are the workflows that transform raw instrument and application data into harmonized IDS records. They run on the Tetra Data Platform and are written in Python using the Tetra SDK. We build pipelines for tasks like Empower SDMS file parsing, plate reader result enrichment, ELN-to-LIMS reconciliation, and AI-ready feature extraction. We follow CI/CD discipline — every pipeline has unit tests, integration tests, and a documented IDS contract — so updates ship safely into validated environments.
Building scientific data harmonization in-house is technically possible but rarely succeeds at scale. The challenge is not one parser — it is hundreds of instrument vendors, proprietary file formats, firmware versions, regulatory metadata requirements, and continuous schema drift. TetraScience invests in maintaining 300+ connectors and IDS schemas as a managed product, which is far cheaper than a 10-person platform team. Our role is to help customers focus their internal engineering on the differentiating layer — domain-specific pipelines and AI workflows — while TetraScience absorbs the commodity work.
The Tetra Data Platform supports the technical controls required by 21 CFR Part 11 — audit trails, access controls, and record retention — but compliance is always a shared responsibility. TetraScience delivers the platform; you must validate your specific configuration, pipelines, and integrations. We perform a full ISPE GAMP 5 Second Edition validation including URS, functional and configuration specs, IQ/OQ/PQ, and a validation summary report. See our TetraScience compliance and validation page for the full approach.
TetraScience is not an ELN, LIMS, or registration system — it is the data layer that connects them. Benchling, Dotmatics, and LIMS platforms like LabWare are systems of record where scientists work. TetraScience moves data between them, harmonizes it into a single open schema, and makes it AI-ready. Most enterprise R&D programs end up running TetraScience alongside one or more ELN/LIMS platforms — they are complementary, not competitive.
The Intermediate Data Schema (IDS) is TetraScience's open, vendor-neutral data model for scientific results. Each IDS captures a class of data — a chromatography injection, a plate reader run, a bioreactor batch — in a normalized JSON structure with explicit metadata. IDS schemas are open and version-controlled, which is critical for regulated use because a downstream consumer sees a deterministic data contract regardless of which instrument vendor or firmware produced the source data. We treat IDS contract changes with the same rigor as API versioning in production software.
TetraScience customers include AstraZeneca, Pfizer, Merck, Bayer, GSK, Bristol Myers Squibb, Janssen, Genentech, and many mid-market biotechs — see the customers page and case studies for current published references. The platform is positioned as the "scientific data foundation" for AI/ML programs in big pharma. The market signal is strong: when AstraZeneca and Pfizer both publicly endorse a platform as their AI data backbone, it has crossed the chasm from emerging to standard.
The most common patterns are: (1) Empower or other chromatography CDS into Tetra IDS, then into Veeva Vault QualityDocs and Vault QMS for QC review; (2) plate reader data through TetraScience into Benchling assay results; (3) ELN entries enriched with harmonized Tetra Data for AI/ML training datasets in Databricks or Snowflake; (4) bioprocess data from Sartorius BIOSTAT into IDBS Polar via Tetra; (5) cross-CRO data lakes where a CMO ingests Tetra-harmonized data without bespoke ETL per partner. Each pattern uses Tetra Integrations as the inbound layer and the Tetra Data Platform API for outbound consumption.
TetraScience markets the platform as "Scientific AI" — AI-ready scientific data — and we build the AI workflows that consume it. Common use cases include LLM-powered queries over harmonized assay data, automated chromatographic peak review, anomaly detection on bioprocess runs, and ELN summarization grounded in instrument data. We connect AI agents to Tetra via the Tetra API and the Model Context Protocol with full audit, scoped access, and validation. See our TetraScience AI Integration page.
A focused initial deployment — Tetra Data Platform setup, a small set of priority Tetra Integrations (for example, Empower CDS for one site, plus a plate reader fleet), and one downstream consumer such as Benchling or a Databricks lake — runs 12 to 18 weeks from kickoff to first validated data flow. A broader program covering 8-15 instrument types, multi-site rollouts, and several pipelines typically spans 6 to 12 months. We always slice work so the first scientists see harmonized data within the first quarter, rather than waiting for a single big-bang go-live.
Multi-site rollouts are the norm in big pharma TetraScience programs. We follow a hub-and-spoke pattern: a central Tetra Data Platform tenancy, a standard library of validated pipelines and IDS contracts, and per-site rollout playbooks for instrument fleets. Each site rolls out one instrument family at a time so validation effort is bounded and operational risk stays low. Site-specific deviations are documented as configuration variants rather than custom code, which keeps the platform inspection-ready under GAMP 5 change control.
TetraScience explicitly aligns with the FAIR data principles — Findable, Accessible, Interoperable, Reusable — that are now standard expectations across pharma R&D and increasingly referenced in regulatory submissions. The IDS provides interoperability and reusability; the Tetra Data Platform indexing and APIs provide findability and accessibility. We help customers map their TetraScience deployment to internal FAIR maturity programs and to the broader data strategy work driven by groups like Pistoia Alliance.
Most engagements start with a two to four week discovery sprint. We interview key scientists, IT owners, QA, and validation leads, inventory current instrument fleet and software footprint, and produce a prioritized roadmap including Tetra Integrations to deploy first, custom pipelines required, downstream consumers to enable, and validation scope. From there we move into platform setup, integration rollouts, pipeline development, validation, and hypercare. Book a working session via our book a meeting page or explore the integrations index for adjacent platforms we connect.
Ready to Build Your Scientific Data Foundation?
From Tetra Data Platform setup to instrument integration, custom pipelines, and AI on harmonized data — with full GxP validation. Let us show you a roadmap tailored to your R&D stack.
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