Hire an LLM Engineer, Boston

Hire an LLM engineer in Boston.

Boston-area biotech and pharma companies, Moderna in Cambridge, Vertex Pharmaceuticals on Fan Pier, Biogen in Cambridge, the Broad Institute, face a specific LLM engineering problem: the standard approach of prompting a general-purpose model and accepting its output does not work when the output feeds a regulatory submission or a clinical database.

The two requirements that shape every Boston biomedical LLM pipeline: every extracted value must be traceable to its source location in the document, and hallucinations must be detectable and caught before they reach the data store. We build the citation layer and the reconciliation checks that make this possible.

Pricing is scoped to the pipeline: regulatory-grade builds with full validation packages cost more than a straightforward extraction pipeline.

How we work

Describe the clinical or regulatory document you need to extract from.

What makes biomedical LLM engineering different.

General-purpose models were not trained on the specific terminology used in ICH E6 guidelines, FDA drug approval documents, or the statistical methods sections of Phase III trial reports. Terms like "per-protocol population", "estimand", and "multiplicity-adjusted p-value" have precise meanings that a general model may paraphrase incorrectly. The extraction pipeline needs either a domain-adapted model or explicit terminology instruction in the system prompt.

Long documents are the norm in clinical settings. A clinical study report can run 500 to 1,000 pages. Passing the full document to an LLM is not viable on context window size or cost grounds. We design section-level chunking strategies that split the document along its defined structure (ICH sections, table identifiers, appendix markers) and route each chunk to the appropriate extraction prompt.

Hallucination in a clinical extraction is not a UX problem, it is a regulatory risk. An extracted adverse event incidence rate that differs from the source document by 0.1% is a data integrity issue. Every extraction in our biomedical pipelines includes a source citation. A post-extraction reconciliation step retrieves the cited location from the source document and verifies that the text there supports the extracted value.

For pipelines that touch GxP data at Moderna or Biogen, we document the validation requirements for 21 CFR Part 11 compliance: electronic audit trails, model and prompt version locking, and a validation package (IQ/OQ/PQ) showing that the system behaves as specified. This is part of the deliverable, not an afterthought.

Where biomedical LLM pipelines typically fail.

Terminology paraphrase errors

General models substitute near-synonyms for defined regulatory terms. Fix: system prompt glossary for your specific therapeutic area and regulatory framework.

Cross-section context loss

Adverse event tables reference definitions from earlier sections. Chunked extraction loses this context. Fix: include section-level context headers in each chunk.

Nested schema assembly errors

Multi-field records where fields come from different document sections are assembled incorrectly. Fix: hierarchical schema design with per-section extraction and a separate assembly step.

Undetected hallucinations

The model invents a plausible-sounding value when the source is ambiguous. Fix: mandatory source citation on every extraction, with reconciliation check against the cited location.

Evaluation for regulatory-grade pipelines.

The evaluation set for a Boston biomedical pipeline is built from historical documents with known-correct field values, verified by your scientific staff. We measure field-level precision and recall separately for each document section type: narrative, table, appendix. Precision and recall requirements are agreed before build starts.

For clinical study reports destined for FDA submission, a field precision of 99%+ is often required for primary endpoint data. For internal research databases at the Broad Institute, 95% may be acceptable with a clear human review step for flagged records. We calibrate the confidence threshold to hit the precision target you specify.

Post-launch monitoring runs a weekly sample of 50 documents through the pipeline, compares against manual extraction by a qualified reviewer, and reports accuracy per field type. If accuracy drifts below threshold, it surfaces as an alert before it affects a submission.

Technical questions for Boston biotech teams.

How do you prevent hallucination when extracting from clinical study reports?: Every extracted value is verified against the position in the source document it came from. The pipeline requires the model to return a citation with every extraction: document name, section identifier, page number, and table or figure reference if applicable. A post-extraction reconciliation step checks that the cited location contains text that supports the extracted value. If reconciliation fails, the record is flagged for human review. This does not eliminate hallucination, but it makes every hallucination detectable and catchable before the value reaches a regulatory submission.
Can an LLM pipeline handle nested schemas in clinical study reports?: Yes, with explicit schema design upfront. A single adverse event record in a clinical study report might require 8 to 12 fields spread across the narrative section, the adverse event table, and the laboratory results appendix. We define a hierarchical JSON schema before building the prompt, and the model is instructed to extract each field from its defined source section. Context window management matters here: for long documents, we use section-level chunking rather than passing the full document, with a separate assembly step that joins the per-section extractions into the final record.
What does 21 CFR Part 11 compliance require for an LLM pipeline?: For GxP data, the pipeline needs: audit trails on every extraction (who triggered it, which model version, which prompt version, what input hash), electronic signature on human review sign-offs, and a validation package showing that the system does what it claims to do. We document the validation requirements and build the audit log into the pipeline. The validation IQ/OQ/PQ package is part of the deliverable for clients at Moderna, Vertex, or Biogen who need it.
How do domain-adapted models improve extraction on biomedical text?: General-purpose models underperform on clinical trial terminology, drug mechanism descriptions, and statistical methods sections because these use precise defined terms that appear rarely in general training data. Domain-adapted approaches include: using BioMedBERT for embedding-based retrieval steps, providing a clinical terminology glossary in the system prompt for key defined terms, and few-shot prompting with examples drawn from documents in your specific therapeutic area. For Broad Institute genomics data, the terminology differs from an oncology clinical study report, so the adaptation is task-specific.

Ready to scope your clinical data extraction pipeline?

Share a sample document and describe the fields you need to extract. We will review it and reply with a technical approach and price range within one business day.

Hire an LLM engineer in Boston.

Pricing is scoped to the pipeline: regulatory-grade builds with full validation packages cost more than a straightforward extraction pipeline.

What makes biomedical LLM engineering different.

Where biomedical LLM pipelines typically fail.

Terminology paraphrase errors

General models substitute near-synonyms for defined regulatory terms. Fix: system prompt glossary for your specific therapeutic area and regulatory framework.

Cross-section context loss

Adverse event tables reference definitions from earlier sections. Chunked extraction loses this context. Fix: include section-level context headers in each chunk.

Nested schema assembly errors

Multi-field records where fields come from different document sections are assembled incorrectly. Fix: hierarchical schema design with per-section extraction and a separate assembly step.

Undetected hallucinations

The model invents a plausible-sounding value when the source is ambiguous. Fix: mandatory source citation on every extraction, with reconciliation check against the cited location.

Evaluation for regulatory-grade pipelines.

Technical questions for Boston biotech teams.

How do you prevent hallucination when extracting from clinical study reports?

Every extracted value is verified against the position in the source document it came from. The pipeline requires the model to return a citation with every extraction: document name, section identifier, page number, and table or figure reference if applicable. A post-extraction reconciliation step checks that the cited location contains text that supports the extracted value. If reconciliation fails, the record is flagged for human review. This does not eliminate hallucination, but it makes every hallucination detectable and catchable before the value reaches a regulatory submission.

Can an LLM pipeline handle nested schemas in clinical study reports?

Yes, with explicit schema design upfront. A single adverse event record in a clinical study report might require 8 to 12 fields spread across the narrative section, the adverse event table, and the laboratory results appendix. We define a hierarchical JSON schema before building the prompt, and the model is instructed to extract each field from its defined source section. Context window management matters here: for long documents, we use section-level chunking rather than passing the full document, with a separate assembly step that joins the per-section extractions into the final record.

What does 21 CFR Part 11 compliance require for an LLM pipeline?

For GxP data, the pipeline needs: audit trails on every extraction (who triggered it, which model version, which prompt version, what input hash), electronic signature on human review sign-offs, and a validation package showing that the system does what it claims to do. We document the validation requirements and build the audit log into the pipeline. The validation IQ/OQ/PQ package is part of the deliverable for clients at Moderna, Vertex, or Biogen who need it.

How do domain-adapted models improve extraction on biomedical text?

General-purpose models underperform on clinical trial terminology, drug mechanism descriptions, and statistical methods sections because these use precise defined terms that appear rarely in general training data. Domain-adapted approaches include: using BioMedBERT for embedding-based retrieval steps, providing a clinical terminology glossary in the system prompt for key defined terms, and few-shot prompting with examples drawn from documents in your specific therapeutic area. For Broad Institute genomics data, the terminology differs from an oncology clinical study report, so the adaptation is task-specific.

Ready to scope your clinical data extraction pipeline?

Share a sample document and describe the fields you need to extract. We will review it and reply with a technical approach and price range within one business day.