Hire a RAG Developer · Boston, MA

Hire a RAG developer in Boston.

Boston's biotech and life sciences companies (Moderna, Vertex Pharmaceuticals, Biogen, Sarepta Therapeutics) generate clinical document volumes that no other document type matches: clinical study reports with 500-page statistical appendices, IND submissions with cross-referenced safety narratives, protocol amendments that supersede prior versions mid-trial.

Building a RAG system for these documents is a different engineering problem than general enterprise search. Clinical trial tables have statistical context that standard PDF loaders destroy. Protocol amendments create version conflicts that naive RAG pipelines retrieve incorrectly. 21 CFR Part 11 requires audit trails that most vector databases do not support out-of-the-box.

We have built RAG for GxP environments, with audit trail infrastructure and evaluation included as part of the scope.

How we work

Tell us about your clinical documents and we will scope a pipeline.

What production RAG for life sciences requires.

A general-purpose RAG system built for enterprise document search will not work correctly on clinical documentation without significant modifications. The failures are specific and predictable: table cells that lose their row-column context during extraction, protocol versions that coexist in the index after an amendment, clinical synonyms that fragment retrieval across equivalent terms.

The regulatory layer adds requirements that do not exist in other industries. 21 CFR Part 11 mandates audit trails for electronic records access in GxP environments. A medical writer at Biogen querying a clinical database to support a regulatory submission is accessing a GxP record. Every query must be logged with user identity, timestamp, and retrieved content.

The document types are also diverse within a single company. Moderna's internal document library includes mRNA manufacturing process descriptions, clinical trial protocols, IND amendments, PSUR safety reports, and label text. Each type has a different chunking strategy, different metadata requirements, and different retrieval accuracy expectations. A single pipeline handles all of these, but the configuration is document-type specific.

Beyond Boston's large pharma and biotech, this applies to life sciences services companies (PRA Health Sciences, ICON) and the Harvard/MIT-adjacent startups building AI-native drug discovery platforms who need their own internal document retrieval infrastructure before their first IND filing.

The table extraction problem in clinical documents.

Clinical study reports are dense with tables. Adverse event summaries, patient baseline characteristics, efficacy endpoints, pharmacokinetic parameters. Getting these tables into a RAG pipeline intact requires a different extraction path than prose text.

What naive extraction destroys

Default PDF loaders flatten table rows into lines of text. 'Grade 3 neutropenia | 12.3% | 4.1%' becomes three separate text spans with no relationship preserved. A query on adverse event rates retrieves numbers without their row labels, the answer is meaningless.

Table-aware extraction with pdfplumber

pdfplumber's table detection mode identifies cell boundaries, column headers, and row labels before text extraction. We store each cell with row_label, column_label, table_id, and source_page in chunk metadata. Statistical values remain interpretable in retrieval.

Multi-page table handling

Clinical study reports have tables that span 3-4 pages. Standard chunking splits these at page boundaries. We detect table continuation markers and merge multi-page tables into a single indexed unit before chunking.

Statistical context preservation

Footnotes on clinical tables carry critical statistical context: confidence intervals, p-values, ANCOVA model specifications. We co-locate footnote content with the table cells they annotate so retrieval returns the value and its statistical qualifier together.

Audit trails and version management for GxP documents.

Immutable query audit logs

Every query is written to an append-only log store with user identity (via SSO), timestamp, query text, retrieved chunk IDs and version IDs, and generated response. Logs are exported on demand for regulatory submissions. The log store is write-once, records cannot be modified or deleted without a separate privileged administrative action that itself generates an audit event.

Protocol amendment chunk invalidation

When a protocol amendment arrives, chunks from the superseded version are marked status: superseded in metadata. Active queries filter to status: active only. The superseded chunks remain in the index for historical audit queries with a date-range filter. This supports retrospective safety analyses that need the original protocol definition, not the amended one.

Clinical synonym resolution

Adverse event, adverse drug reaction, and ADR are the same concept in MedDRA and UMLS ontologies but may appear differently across documents from different sponsors. We apply a synonym expansion step at query time: a user query for 'adverse drug reaction' also retrieves chunks using 'adverse event' and 'ADR.' This prevents fragmented retrieval that misses relevant content because the terminology does not match exactly.

Per-document-type chunking configuration

Clinical protocols chunk differently than manufacturing batch records, which chunk differently than PSUR narratives. Each document type in your corpus gets a configured chunking strategy: chunk size, overlap, metadata fields extracted, table handling mode, and synonym expansion dictionaries. The pipeline applies the correct strategy based on a document_type metadata field set at ingestion.

Questions about clinical document RAG and GxP compliance.

How do you handle the table extraction problem in clinical study reports without losing statistical context?: Standard PDF loaders (PyMuPDF, pdfplumber in default mode) flatten table rows into single lines of text, losing the column structure that gives statistical values their meaning. An adverse event rate of '12.3%' is meaningless without its row header (the event name) and column header (the treatment arm). We use pdfplumber with explicit table detection settings or unstructured.io's table extraction mode to parse row-column relationships before chunking. Each table cell is stored with its row_label, column_label, table_id, and source_page in chunk metadata. A query like 'what was the incidence of Grade 3 neutropenia in the treatment arm' retrieves the correct cell value along with the full table context, not a stripped number with no attribution.
Can a RAG system satisfy 21 CFR Part 11 audit trail requirements?: Yes, with the right logging architecture. 21 CFR Part 11 requires audit trails for any system that creates, modifies, or accesses GxP records, including electronic retrieval systems that touch GxP documents. Our Boston life sciences RAG deployments include: immutable query logs (written to append-only storage, not a mutable table), timestamped records of every document ingestion event, user authentication tied to each query via SSO integration, and a log export API for regulatory submission. The audit trail records the query, the user who submitted it, the document chunks that were retrieved (with their version IDs), and the generated response. This satisfies the audit trail requirement for read-access systems. If your RAG deployment will be used to modify GxP records rather than query them, a more comprehensive validation process is required and we scope that separately.
How does chunk invalidation work when a protocol amendment supersedes an earlier version?: When a protocol amendment is filed (e.g., an IND amendment at Moderna or a label update at Vertex), we trigger an invalidation run that marks all chunks from the superseded document version as status: superseded in vector store metadata. A new version_id is assigned to the updated document and its chunks. Active queries apply a metadata filter that excludes superseded chunks by default, so a medical writer asking 'what is the current primary endpoint for this study' retrieves the amended protocol, not the original. The prior version's chunks are not deleted. They remain for historical audit queries, accessible via a date-range filter that specifies 'show me the protocol version in effect on [date].' This matters for retrospective safety reporting where you need to show what the original endpoint definition was.
What retrieval accuracy is achievable on clinical terminology?: On well-structured clinical documents with proper synonym handling, we consistently achieve recall@3 above 0.87 on clinical terminology queries in our evaluation sets. The main challenge is synonym resolution: 'adverse event,' 'adverse drug reaction,' and 'ADR' all refer to the same concept but may appear differently in different documents. We address this with query rewriting (expanding the user query to include known synonyms before retrieval) and a medical terminology thesaurus (UMLS or MedDRA mappings) applied at both ingestion and query time. For Sarepta's gene therapy documentation or Biogen's neurology clinical trial reports, which use highly specialized terminology, we fine-tune the synonym mapping using a glossary extracted from your actual documents rather than relying on general-purpose medical ontologies.

Scope your life sciences document pipeline.

Tell us which document types are in scope, whether GxP audit trail requirements apply, and roughly how many documents you are working with. We reply within one business day.

Hire a RAG developer in Boston.

We have built RAG for GxP environments, with audit trail infrastructure and evaluation included as part of the scope.

What production RAG for life sciences requires.

The table extraction problem in clinical documents.

What naive extraction destroys

Table-aware extraction with pdfplumber

Multi-page table handling

Statistical context preservation

Audit trails and version management for GxP documents.

Immutable query audit logs

Protocol amendment chunk invalidation

Clinical synonym resolution

Per-document-type chunking configuration

Questions about clinical document RAG and GxP compliance.

How do you handle the table extraction problem in clinical study reports without losing statistical context?

Standard PDF loaders (PyMuPDF, pdfplumber in default mode) flatten table rows into single lines of text, losing the column structure that gives statistical values their meaning. An adverse event rate of '12.3%' is meaningless without its row header (the event name) and column header (the treatment arm). We use pdfplumber with explicit table detection settings or unstructured.io's table extraction mode to parse row-column relationships before chunking. Each table cell is stored with its row_label, column_label, table_id, and source_page in chunk metadata. A query like 'what was the incidence of Grade 3 neutropenia in the treatment arm' retrieves the correct cell value along with the full table context, not a stripped number with no attribution.

Can a RAG system satisfy 21 CFR Part 11 audit trail requirements?

Yes, with the right logging architecture. 21 CFR Part 11 requires audit trails for any system that creates, modifies, or accesses GxP records, including electronic retrieval systems that touch GxP documents. Our Boston life sciences RAG deployments include: immutable query logs (written to append-only storage, not a mutable table), timestamped records of every document ingestion event, user authentication tied to each query via SSO integration, and a log export API for regulatory submission. The audit trail records the query, the user who submitted it, the document chunks that were retrieved (with their version IDs), and the generated response. This satisfies the audit trail requirement for read-access systems. If your RAG deployment will be used to modify GxP records rather than query them, a more comprehensive validation process is required and we scope that separately.

How does chunk invalidation work when a protocol amendment supersedes an earlier version?

When a protocol amendment is filed (e.g., an IND amendment at Moderna or a label update at Vertex), we trigger an invalidation run that marks all chunks from the superseded document version as status: superseded in vector store metadata. A new version_id is assigned to the updated document and its chunks. Active queries apply a metadata filter that excludes superseded chunks by default, so a medical writer asking 'what is the current primary endpoint for this study' retrieves the amended protocol, not the original. The prior version's chunks are not deleted. They remain for historical audit queries, accessible via a date-range filter that specifies 'show me the protocol version in effect on [date].' This matters for retrospective safety reporting where you need to show what the original endpoint definition was.

What retrieval accuracy is achievable on clinical terminology?

On well-structured clinical documents with proper synonym handling, we consistently achieve recall@3 above 0.87 on clinical terminology queries in our evaluation sets. The main challenge is synonym resolution: 'adverse event,' 'adverse drug reaction,' and 'ADR' all refer to the same concept but may appear differently in different documents. We address this with query rewriting (expanding the user query to include known synonyms before retrieval) and a medical terminology thesaurus (UMLS or MedDRA mappings) applied at both ingestion and query time. For Sarepta's gene therapy documentation or Biogen's neurology clinical trial reports, which use highly specialized terminology, we fine-tune the synonym mapping using a glossary extracted from your actual documents rather than relying on general-purpose medical ontologies.

Scope your life sciences document pipeline.

Tell us which document types are in scope, whether GxP audit trail requirements apply, and roughly how many documents you are working with. We reply within one business day.