RAG Development · Boston, MA

Custom RAG development in Boston.

Healthcare and life sciences RAG is a different animal. HIPAA applies. 21 CFR Part 11 applies. The Business Associate Agreement applies. The embedding model choice changes because clinical and biomedical text doesn't look like the general web corpus that powers text-embedding-3-large. The evaluation bar is higher because the wrong answer in clinical decision support has consequences that the wrong answer in e-commerce search does not.

We build HIPAA-compliant RAG pipelines for Boston hospitals, biotech R&D teams, and pharma regulatory groups. Deployments live inside your VPC against BAA-eligible inference (Anthropic on Bedrock, Azure OpenAI), with audit-grade query logging from day one.

Pricing is scoped per engagement based on corpus scale, compliance scope, and evaluation depth.

How we work

Tell us what clinical or research workflow you need retrieval for.

The technical reality of clinical and biomedical RAG.

Biomedical text is not general text. A clinical note contains medication abbreviations, ICD-10 codes, anatomical references, and lab value shorthand that general embeddings either miss entirely or map to incorrect semantic neighborhoods. A PubMed abstract uses gene symbols, pathway names, and disease ontology terms that have specific meaning in a UMLS or MeSH hierarchy. Running this corpus through text-embedding-3-large works, but it leaves a meaningful chunk of retrieval quality on the table.

PubMedBERT, BioBERT, and SciBERT exist because of this. They are pre-trained on biomedical literature and produce embeddings that respect the actual semantic structure of the domain. For a Moderna mRNA platform team searching across internal study reports and external literature, the domain-trained model can be the difference between an acceptable system and a useful one. We benchmark these models against your actual corpus rather than picking based on published numbers.

The compliance overlay is everywhere. PHI in clinical notes triggers HIPAA. Electronic records used in FDA submissions trigger 21 CFR Part 11. Educational records from a Boston EdTech client trigger FERPA. Each of these is concrete and each shapes the architecture. PHI never leaves your VPC. BAAs are in place with every inference provider. Query and answer logs go to immutable storage with the appropriate retention horizon. Access controls partition the corpus so a clinician can only retrieve from their own panel of patients.

Clinical decision support adds a guardrail layer on top. Generation prompts include refusal templates for any query that would constitute a treatment recommendation without clinician verification. Confidence thresholds are higher than in non-clinical settings. The system explicitly surfaces what it does not know, with paragraph-level citations to the sources it does have. The product goal is to make a clinician faster at finding the right answer, not to substitute for the clinician.

What we build for Boston healthcare and life sciences teams.

Six components, each shaped by HIPAA, 21 CFR Part 11, and the specific document shapes of clinical and biomedical work.

HIPAA-compliant VPC deployment

All ingestion, embedding, and inference inside your VPC. KMS customer-managed keys for encryption at rest. TLS 1.3 in transit. BAA-eligible inference via Anthropic on Bedrock or Azure OpenAI. No third-party SaaS touches PHI.

Biomedical embedding selection

Benchmarking of PubMedBERT, BioBERT, SciBERT, and Voyage-3 / text-embedding-3-large on your actual corpus and question set. Domain-adapted fine-tuning when the recall lift justifies the cost.

Clinical document ingestion

EHR note parsing (HL7 v2, FHIR R4, CDA documents), clinical protocol ingestion with section-aware chunking, structured field extraction from medication and lab entries. Patient identifiers carried as metadata for access control, never as part of the embedded text.

Regulatory document pipeline

FDA submissions (510(k), IND, NDA), study protocols, investigator brochures, and literature ingestion with PubMed PMID and DOI metadata. Citations render with the regulatory-grade precision required for inspection.

Clinical guardrails and confidence thresholds

Refusal templates for treatment recommendations without clinician verification. Confidence-thresholded retrieval that surfaces uncertainty rather than hallucinating. Explicit 'no high-confidence answer' state surfaced to the user.

21 CFR Part 11 audit logging

Every query and answer to immutable storage (S3 Object Lock or Azure Blob immutable) with user identity, timestamp, retrieved chunks, and generated text. Six-year retention. Structured JSON format exportable to your existing GxP archive.

Built for Boston healthcare and life sciences.

Boston is the densest concentration of healthcare, biotech, and pharma R&D in the country, with a Cambridge-Kendall Square biotech corridor that has no real equivalent anywhere else. Mass General Brigham (the merged Partners Healthcare system), Beth Israel Lahey, Tufts Medicine, and Boston Children's run enormous clinical document libraries. Moderna, Vertex, Biogen, Sarepta, and dozens of earlier-stage biotechs operate study report archives and regulatory submission corpora. Insulet (Omnipod) and other medical device firms generate 510(k) submission packages and predicate device research that are precisely the right shape for retrieval-augmented search.

The non-clinical Boston tech stack matters too. HubSpot, Toast, Klaviyo, and Wayfair are deep customers for marketing and product-data retrieval. EdTech players (with the universities and the K-12 vendors that orbit them) bring FERPA into the same picture: educational records are protected, audit logs are required, and the architecture pattern looks more like the HIPAA pattern than the standard SaaS pattern. We have built both shapes.

Deployments target whichever cloud you already trust for protected workloads. AWS us-east-1 with HIPAA-eligible services is the most common choice. Azure with the signed Microsoft BAA is equally well supported for Microsoft-stack hospital systems. On-prem deployments against an existing OpenShift or Tanzu footprint are an option for the small number of clients where cloud is still not permitted. We work with Boston teams remotely; the security review meetings happen on video, and the BAA negotiation happens in parallel with discovery so it doesn't become the schedule bottleneck it usually is.

Questions from Boston healthcare and biotech buyers.

Can a RAG system be HIPAA-compliant when it touches PHI in clinical notes or research data?: Yes, but the compliance work shapes the architecture from the first line. We deploy inside your VPC with no third-party SaaS in the data path, encryption at rest via KMS-managed keys (CMK, not AWS-managed), encryption in transit via TLS 1.3, and a Business Associate Agreement in place with every component vendor. For Anthropic and OpenAI, that means using the BAA-eligible enterprise tiers (Anthropic Enterprise on AWS Bedrock, Azure OpenAI with the signed Microsoft BAA). All queries, retrieved chunks, and generated answers go to an immutable audit log with a six-year retention horizon. Role-based access at the chunk level ensures a clinician querying their own panel can never retrieve another clinician's PHI. We have built this against Epic Caboodle exports and against custom clinical research data warehouses.
Which embedding models actually work for biomedical text versus general-purpose ones?: Biomedical text is dense with terminology that general embeddings handle poorly. The current sensible options are PubMedBERT (pre-trained on PubMed abstracts and full-text), BioBERT, and SciBERT for self-hosted scenarios where the corpus is purely biomedical. For mixed corpora that combine clinical notes, research literature, and regulatory documents, Voyage AI's voyage-3-large or text-embedding-3-large with a domain-adapted fine-tune typically wins. We benchmark all four on your actual queries. For one Boston pharma R&D client searching across roughly 200,000 internal study reports, PubMedBERT outperformed text-embedding-3-large by 11 points on recall@5 against a hand-labeled question set. For a clinical decision support pipeline over EHR notes, the reverse was true because the notes contained large amounts of generic prose that PubMedBERT handled worse than a general model.
How do you handle FDA submission research workflows where citation accuracy is regulated?: FDA 510(k) submissions, IND applications, and 505(b)(2) NDA filings all require predicate device research, prior art reviews, and literature support that the regulatory team has to cite precisely. Citations in our systems carry the source document name, section heading, page, paragraph, and (where applicable) PubMed PMID or DOI. The retrieval system flags the confidence of each cited chunk and refuses to generate an answer when no chunk passes the threshold. For 21 CFR Part 11 compliance on electronic records, every query and answer gets logged with the user identity, timestamp, retrieved chunks, and generated text. The combination of paragraph-level citation plus immutable audit log gives the regulatory team an evidence trail that holds up under inspection.
What does a Mass General Brigham or Moderna-shaped engagement actually look like from start to deployment?: Healthcare and biotech engagements run eight to twelve weeks because the compliance review is real, not theatrical. Weeks one and two are scoping, BAA execution, security review, and integration design against your existing identity provider (Epic Hyperdrive SSO, Microsoft Entra ID, or Okta). Weeks three to six are ingestion and chunking: clinical protocols, FDA submissions, study reports, and literature each get their own parsing path. Weeks five through eight are retrieval tuning with a clinician-labeled or scientist-labeled question set (typically 150 to 250 real queries) and Ragas-style evaluation. Weeks eight through ten are guardrail engineering: clinical decision support requires hard limits on what the system will assert without explicit human verification. Final weeks cover audit logging, role-based access, and the production deployment inside your VPC.

Ready to scope your pipeline?

Tell us what clinical or research workflow needs retrieval and what compliance scope applies. We reply within one business day with a rough scope and price range.

Custom RAG development in Boston.

Pricing is scoped per engagement based on corpus scale, compliance scope, and evaluation depth.

The technical reality of clinical and biomedical RAG.

What we build for Boston healthcare and life sciences teams.

Six components, each shaped by HIPAA, 21 CFR Part 11, and the specific document shapes of clinical and biomedical work.

HIPAA-compliant VPC deployment

Biomedical embedding selection

Benchmarking of PubMedBERT, BioBERT, SciBERT, and Voyage-3 / text-embedding-3-large on your actual corpus and question set. Domain-adapted fine-tuning when the recall lift justifies the cost.

Clinical document ingestion

Regulatory document pipeline

Clinical guardrails and confidence thresholds

21 CFR Part 11 audit logging

Built for Boston healthcare and life sciences.

Questions from Boston healthcare and biotech buyers.

Can a RAG system be HIPAA-compliant when it touches PHI in clinical notes or research data?

Yes, but the compliance work shapes the architecture from the first line. We deploy inside your VPC with no third-party SaaS in the data path, encryption at rest via KMS-managed keys (CMK, not AWS-managed), encryption in transit via TLS 1.3, and a Business Associate Agreement in place with every component vendor. For Anthropic and OpenAI, that means using the BAA-eligible enterprise tiers (Anthropic Enterprise on AWS Bedrock, Azure OpenAI with the signed Microsoft BAA). All queries, retrieved chunks, and generated answers go to an immutable audit log with a six-year retention horizon. Role-based access at the chunk level ensures a clinician querying their own panel can never retrieve another clinician's PHI. We have built this against Epic Caboodle exports and against custom clinical research data warehouses.

Which embedding models actually work for biomedical text versus general-purpose ones?

Biomedical text is dense with terminology that general embeddings handle poorly. The current sensible options are PubMedBERT (pre-trained on PubMed abstracts and full-text), BioBERT, and SciBERT for self-hosted scenarios where the corpus is purely biomedical. For mixed corpora that combine clinical notes, research literature, and regulatory documents, Voyage AI's voyage-3-large or text-embedding-3-large with a domain-adapted fine-tune typically wins. We benchmark all four on your actual queries. For one Boston pharma R&D client searching across roughly 200,000 internal study reports, PubMedBERT outperformed text-embedding-3-large by 11 points on recall@5 against a hand-labeled question set. For a clinical decision support pipeline over EHR notes, the reverse was true because the notes contained large amounts of generic prose that PubMedBERT handled worse than a general model.

How do you handle FDA submission research workflows where citation accuracy is regulated?

FDA 510(k) submissions, IND applications, and 505(b)(2) NDA filings all require predicate device research, prior art reviews, and literature support that the regulatory team has to cite precisely. Citations in our systems carry the source document name, section heading, page, paragraph, and (where applicable) PubMed PMID or DOI. The retrieval system flags the confidence of each cited chunk and refuses to generate an answer when no chunk passes the threshold. For 21 CFR Part 11 compliance on electronic records, every query and answer gets logged with the user identity, timestamp, retrieved chunks, and generated text. The combination of paragraph-level citation plus immutable audit log gives the regulatory team an evidence trail that holds up under inspection.

What does a Mass General Brigham or Moderna-shaped engagement actually look like from start to deployment?

Healthcare and biotech engagements run eight to twelve weeks because the compliance review is real, not theatrical. Weeks one and two are scoping, BAA execution, security review, and integration design against your existing identity provider (Epic Hyperdrive SSO, Microsoft Entra ID, or Okta). Weeks three to six are ingestion and chunking: clinical protocols, FDA submissions, study reports, and literature each get their own parsing path. Weeks five through eight are retrieval tuning with a clinician-labeled or scientist-labeled question set (typically 150 to 250 real queries) and Ragas-style evaluation. Weeks eight through ten are guardrail engineering: clinical decision support requires hard limits on what the system will assert without explicit human verification. Final weeks cover audit logging, role-based access, and the production deployment inside your VPC.

Ready to scope your pipeline?

Tell us what clinical or research workflow needs retrieval and what compliance scope applies. We reply within one business day with a rough scope and price range.