Industries · Pharma, Biotech, CROs & Research Labs
AI for Life Sciences
Aaga builds AI and software for pharmaceutical, biotech, medical device and research teams, CROs and testing labs. We organize lab data, help draft regulatory documents for expert review and make the scientific literature searchable with cited answers, using a validation approach your quality team can work with.
- Track samples, tests, instruments and results in one auditable lab system
- Draft sections of regulatory and study documents from your source data, for expert review
- Search literature and internal reports with answers that cite every source
- Build with GxP expectations in mind: requirements, risk assessment, traceability and test evidence
- One lab, document type or research question first
- Pilot
- Experts approve every regulated output
- Human review
- Clients across the US, Canada, UK, Netherlands, UAE and India
- 100+
Life Sciences Challenges We Help Solve
Science moves fast. Lab records, documents and literature reviews often don't, and every shortcut has to stand up to an audit.
Lab data in silos
Results sit in instrument software, spreadsheets, paper notebooks and email, which makes trending, review and audits slow.
Heavy documentation
Protocols, study reports, CMC sections and regulatory responses take weeks of drafting, cross-checking and formatting.
Literature overload
Scientists and medical affairs teams can't read everything published in their field, and keyword search misses relevant papers.
Validation and data integrity
Computerized systems in regulated work must be validated and keep reliable records, which slows down new tools.
Sensitive data limits
Patient and trial data can't be shared freely with developers or external AI services, so projects stall at the data stage.
Where AI Works in Life Sciences Today
AI drafts, organizes and retrieves. Scientists, regulatory specialists and QA stay responsible for the science and the submission.
Lab data and LIMS
Sample tracking by unique ID, test workflows, instrument data capture, role-based review and audit trails in one system, with AI flags for out-of-trend results.
Regulatory drafting assistance
Draft sections of study reports, CTD summaries, responses to agency questions and SOP updates from approved source data, with citations for expert review.
Literature search with RAG
Ask questions across PubMed abstracts, licensed full texts and internal reports, and get answers that cite the passages they came from.
Document QC and consistency checks
Compare numbers, terms and references across tables, listings and narrative text, and list inconsistencies for a reviewer.
Synthetic data for development
Statistically realistic synthetic datasets to build and test software and models without direct access to real patient or trial data.
Research and assay analytics
Machine learning on assay, stability or process data to spot trends and anomalies, presented for scientists to interpret.
Life Sciences AI and Software We Build
Each solution links to the service behind it. Most teams start with lab data or one document type.
LIMS and lab software
Laboratory information management, instrument integration and lab dashboards built around your workflow.
Generative AI and RAG
Literature assistants and drafting tools grounded in your approved sources, with citations.
Document and workflow automation
Document assembly, QC checks, review routing and approvals with full audit trails.
Synthetic data creation
Privacy-preserving synthetic datasets for testing, training and demos.
Machine learning for lab and process data
Trend, anomaly and prediction models with documented training data and performance.
Secure cloud and data platforms
Access-controlled hosting, backups and audit logging in your chosen region or environment.
From Lab Review to a Validated Pilot
Process and data review
We map the lab or document process, the systems involved and which records are GxP-relevant.
Requirements and risk assessment
We write user requirements with your team and agree, with QA, how critical each function is and how it will be tested.
Scoped pilot
We build one workflow, such as sample tracking or a literature assistant, on test or synthetic data first.
Verification and evidence
We run documented tests, keep traceability from requirement to test, and hand QA the evidence they need for their validation decision.
Release and monitor
After your approval we release, control changes, monitor AI output quality and extend to more labs or document types.
Aaga vs a Typical Large IT Services Model for Life Sciences AI
Large IT services firms suit global, multi-year programs such as enterprise-wide platform rollouts. Focused lab and document AI projects usually need a smaller team.
| Aaga | Typical large IT services model | |
|---|---|---|
| Starting point | One lab, workflow or document type as a pilot | Multi-phase program with a long discovery stage |
| Team | Senior engineers you work with directly | Larger blended teams with account management layers |
| Validation | Risk-based evidence scoped to the pilot, owned by your QA | Often part of a wider validation program |
| Best fit | Targeted AI for lab data, documents and literature | Global, multi-thousand-person outsourcing programs |
Comparison describes typical delivery models, not any specific company.
What Is AI for Life Sciences?
AI for life sciences is the use of machine learning, language models and workflow software to manage lab data, support scientific and regulatory writing, and search research literature in pharma, biotech, medical device and research organizations. In practice, the most useful applications today are operational: getting lab data into one place, drafting and checking documents, and finding the right evidence quickly. Scientific and regulatory judgment stays with your experts.
Aaga is an AI-native engineering company with clients across the USA, Canada, the UK, the Netherlands, Dubai (UAE) and India. We build lab software and AI together, so the AI works on clean, well-governed data from your own systems.
Which Life Sciences Workflows Should You Start With?
Choose a workflow where the data exists, the output is easy to check and the regulatory impact is understood.
| Workflow | What AI or software does | Who stays in control |
|---|---|---|
| Sample and test tracking | Tracks samples, tests and results with an audit trail | Lab supervisor reviews and releases |
| Out-of-trend flags | Highlights unusual results for review | Scientist investigates |
| Regulatory drafting | Drafts sections from approved data, with citations | Regulatory writers edit and approve |
| Literature search | Finds and cites relevant passages | Scientist judges the evidence |
| Development data | Generates synthetic datasets | Data owner approves use |
Lab data and LIMS
A laboratory information management system (LIMS) tracks every sample from receipt to report. Aaga built a Laboratory Information Management System for the Tamil Nadu Food Safety Department's testing lab. It covers sample tracking with unique IDs, workflows for chemical and instrumental tests, instrument integration for data capture, report generation, role-based access for admins, technicians and supervisors, and audit trails. Research, QC and contract labs need the same foundations. On top of them we add AI for out-of-trend flags, result summaries and lab dashboards.
Regulatory submissions: drafting assistance with human review
Regulatory documents such as clinical study reports, CTD summaries, CMC sections and responses to agency questions draw on large volumes of source data. A drafting assistant pulls the relevant tables and approved text, drafts a section in your template, cites where each statement came from and checks numbers for consistency. It is a first draft for a regulatory or medical writer, never a final document. Every edit and approval is recorded.
Literature search with RAG
Retrieval-augmented generation (RAG) lets scientists ask a question in plain language and get an answer drawn only from retrieved sources, with citations. We connect it to PubMed, licensed journals, conference abstracts and your internal reports, respecting each source's license terms. Read our guide to RAG for business or see generative AI development.
Synthetic data
Patient and trial data is tightly controlled. Synthetic data creation produces datasets that mirror the structure and statistics of real data, so developers can build and test software and models without access to the originals.
How Does Aaga Approach GxP Validation?
We build with GxP expectations in mind and support your validation, rather than claiming compliance for you. Our approach follows widely used principles, including a risk-based approach in the spirit of GAMP 5 and data integrity principles often summarized as ALCOA+:
- User requirements and risk assessment agreed with your QA team
- Audit trails, access control and electronic records designed to support 21 CFR Part 11 and EU Annex 11 where they apply
- Traceability from each requirement to its test and result
- Documented testing and change control for every release
- AI-specific controls: fixed model versions, recorded prompts and sources, and human review of every regulated output
Your quality team makes the validation decision. We provide the evidence and fix what they find. This is a general description of our approach, not regulatory or legal advice.
Why Life Sciences Teams Choose Aaga
- Senior engineers, directly. You work with the people building your system.
- Pilot first. One lab, workflow or document type before a wider rollout.
- Own platform. Workflows, permissions, audit logs and integrations come from Aaga's platform, so they aren't rebuilt from scratch.
- Affordable. Lower cost than large IT services firms for comparable scope.
See also AI in healthcare or browse all industries we serve.
Frequently Asked Questions
Life sciences teams use AI to organize lab data, flag unusual results, draft sections of regulatory and study documents, search scientific literature and generate synthetic data for development. Aaga builds these tools around your lab systems and documents, with experts reviewing every regulated output.
AI can draft sections, summaries and responses from your approved source data, and check consistency across documents. It does not replace regulatory or medical writers. Every draft cites its sources and is reviewed, edited and approved by qualified people before it goes anywhere near an agency.
Compliance belongs to the regulated company and its quality system, so we don't claim it on your behalf. We build with GxP expectations in mind, including a risk-based approach in the spirit of GAMP 5, audit trails, access control and electronic records designed to support 21 CFR Part 11 and EU Annex 11, and we provide the requirements, traceability and test evidence your QA team needs to validate the system.
Yes. Aaga built a Laboratory Information Management System for the Tamil Nadu Food Safety Department's testing lab, covering sample tracking with unique IDs, test workflows, instrument integration, reporting, role-based access and audit trails. It is a food-safety lab, and the same building blocks apply to research, QC and contract labs.
It uses retrieval-augmented generation: it first finds relevant passages in PubMed abstracts, licensed papers or your internal reports, then answers only from those passages and cites them. If the sources don't support an answer, it says so. Scientists can open every citation to check it.
Yes. We can generate synthetic data for development and testing, de-identify data before it reaches a model, host models in your own environment, or use providers with appropriate data-processing terms. We agree the approach with your privacy and QA teams first.
It depends on the workflow, integrations and the level of validation evidence needed. After a free consultation we give you a scoped pilot plan and budget range for one lab, workflow or document type.
Less Paperwork, More Science
Tell us the lab or document process that slows your team down. We'll show you how AI can help, with the controls your QA team expects.
Discuss My Project