Pharma & Life Sciences · Regulatory document automation
Your QA team should not spend Monday morning searching for the right SOP
Banao builds regulatory document intelligence for pharma and life-sciences operations — a validated, audit-ready system that makes SOPs, CAPAs, change controls, and submission dossiers instantly searchable and chat-queryable without discarding the version trail or access controls behind them.
The common failure mode is a shared drive where nothing is findable and every audit costs a week of manual triage. We replace that with a search and retrieval layer trained on your document corpus, connected to your QMS or document management system, with a full log of every query and result.
The first call is free · 45 minutes · no obligation
What we build
What a Banao document-intelligence deployment includes
A document system that passes a GxP audit is not the same as a general document chatbot. Versioning, access control, and traceability are part of the deliverable, not afterthoughts.
SOP and quality-document search
Semantic search over your SOP library — find the right clause, the current approved version, and the approval history in a single query instead of navigating a folder tree or calling the document controller.
CAPA and deviation document processing
NLP over deviation reports and CAPA records to extract root-cause categories, affected products, and closure status — so a QA manager sees patterns across hundreds of records, not just individual PDFs.
Submission dossier intelligence
Regulatory submission packages made queryable by module, section, and topic — so Medical Affairs and Regulatory Affairs stop re-reading the same CTD sections for every health-authority query.
Change control document routing
Automated extraction of impact assessments, affected SOPs, and validation requirements from change requests — with suggested routing and linked documents surfaced at the point of approval.
Audit-ready version and access tracing
Every query, result, and document version is logged with a timestamp and user ID. Auditors see a clear, inspectable record rather than a black-box tool that cannot explain what it retrieved or why.
QMS and DMS integration
The intelligence layer connects to Veeva Vault, MasterControl, SharePoint, or your existing document management system — adding search and extraction on top of what you already own rather than migrating your documents.
Receipts
Where this is already running
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Case and clinical documents made queryable for external review
For a digital-health operation, Banao built a document retrieval layer over case and clinical paperwork — making records queryable by type, date range, and outcome category, with every retrieval logged for the audit trail. The team stopped manually cross-referencing files to answer external review queries.
Dogfooding
We depend on our own AI before you do
Banao runs a ~300-person engineering company on its own AI products. InterviewGod screens every engineering hire; Vikaas runs demand generation. Both systems leave the kind of decision log we expect from ourselves before we ask a regulated client to trust it.
When we say a document intelligence system must log every query and result, we are not describing a compliance box-tick — we built that requirement into our own tools first. A system that has survived our own operation is the version we bring to your quality environment.
Screens Banao's own engineering hires, with a decision log per candidate.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When regulatory document AI is not the right investment
Not every document problem needs an AI layer. We will tell you when the simpler path is the right one:
- Small, stable document corpus: if your total SOP library is under a few hundred documents and rarely changes, a well-structured DMS with good metadata is the right answer. An AI layer adds cost without adding value at that scale.
- Digitization is the real blocker: if core documents live in paper binders or unstructured scans, the first project is OCR and structured digitization — not AI search. We can do both, but the sequencing matters and we will be clear about where the cost sits.
- Validation sign-off not yet in place: some Quality departments have not yet run their computer-system validation process for an AI retrieval tool. If your quality head is not ready to validate the system, the build will stall before it reaches users — and we will say so before you commit budget.
How we start
How we start — fixed price, low risk
We do not quote a document intelligence system off a brochure. We look at your actual document corpus and your specific QA pain point first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your document corpus, test retrieval accuracy on your hardest query types, and hand back a baseline accuracy estimate, an ROI model, and a computer-system validation read — yours to keep. If you proceed, the Sprint is credited against the build.
- 02
Build
Document pipeline, embedding, and retrieval layer built to your QMS integration and access-control requirements. Audit logging and version tracing are part of the deliverable from the first commit, not bolted on at the end.
- 03
Production & continuous improvement
Deployment with QA change management, a dashboard showing query coverage and retrieval accuracy, and a feedback loop so the system stays current as your document corpus evolves.
FAQ
Frequently asked questions
Can an AI document system be validated under 21 CFR Part 11 or Annex 11?
Yes. We build for computer-system validation from the start — documented requirements, versioned models, audit trails, and electronic signature support where required. The system becomes a validated, inspectable component rather than an unexplained tool bolted onto the QMS.
We use Veeva Vault. Do documents have to move?
No. Banao builds the intelligence layer on top of your existing DMS — Veeva Vault, MasterControl, SharePoint, or a custom store. Documents stay where they are; search and extraction connect to the existing repository via API.
How accurate is retrieval on regulatory documents?
That is what the Discovery Sprint measures on your actual corpus. Regulatory documents are domain-specific — a model tuned on general text will miss the clause structure and term conventions that matter to a QA reviewer. We test accuracy on your hardest query types before committing to a build.
What happens when a document is superseded or withdrawn?
Version control is a core requirement. The system tracks document status and version history, surfaces the current approved version first, and logs any retrieval of a superseded document — so the audit trail reflects what a user was shown, not just what they searched for.
How long does deployment take?
A typical path is a 2-week Discovery Sprint, a 6–8 week build, and a 4-week validation and rollout phase — with computer-system validation work running alongside the build rather than after it. The timeline depends on corpus size and the complexity of your DMS integration.
Get started
Find out how long your QA team spends on documents it should not have to search for
Bring your hardest retrieval scenario — the audit finding you had to reconstruct manually, the submission query that cost a week. In 45 minutes we will tell you whether AI retrieval is worth building and what it would take.
Book a Discovery Sprint