LLM Integration Company in Boston

How many systems of record the model has to read from is the number that matters here. Engagements start at a scoped LLM integration into existing Boston systems (a Confluence copilot, a contract extraction pipeline, a clinical summarisation tool) over eight to fourteen weeks, covering data audit, retrieval index build, prompt engineering, evaluation harness against a labelled golden set, and a production deployment with monitoring, a multi-system agentic integration across Epic, Veeva Vault, Bloomberg, or SimCorp, or a heavy clinical or regulated build for Mass General Brigham, Moderna, Vertex, or Fidelity that needs 21 CFR Part 11 e-signature, HIPAA BAA validation, and SEC 17a-4 retention. Codazz quotes fixed fees so finance partners at Bain Capital style sponsors can lock the budget before approval.

Yes. HIPAA-compliant LLM builds are most of our Boston clinical work. We run inference on AWS Bedrock with Anthropic Claude under the AWS BAA, or self-host Llama 3 and Mistral inside the client VPC when the data classification rules out hosted models. Every prompt and completion passes a PHI detection layer (regex plus an NER model fine-tuned on i2b2 corpora), redactions are logged for the OCR audit trail, and no PHI leaves us-east-1. We produce a HIPAA risk analysis, a Privacy Impact Assessment, and a BAA package suitable for Mass General Brigham, Boston Children's, Dana-Farber, or Tufts Medical Center procurement. Clinician review and IRB sign-off happen before production exposure, not after.

Pharma LLM workflows at Moderna, Vertex, Biogen, Sanofi Cambridge, Pfizer Cambridge, and Takeda live inside Veeva Vault, Master Control, or equivalent eQMS platforms. Our integrations bind every LLM-generated artifact (CSR section drafts, IND modules, deviation summaries) to a Part 11-compliant electronic signature with linked audit trail, role-based access, and tamper-evident hash chains. The model never writes directly to the regulatory record; it produces a reviewable draft that a qualified author signs into Vault. We document the system under GAMP 5 Category 4 or 5, deliver IQ, OQ, and PQ scripts, and align with FDA's draft AI/ML guidance and the EMA Reflection Paper on AI. Computer System Validation is part of scope, not a separate vendor.

We build a labelled golden set from the client's own data (typically 300 to 1,500 examples reviewed by domain experts at Fidelity, Wellington, MGB, or Moderna), then run Ragas, TruLens, or DeepEval against every model and prompt change in CI. Faithfulness, context precision, answer relevance, and hallucination rates are tracked over time; regressions fail the build. For clinical summarisation we add expert clinician review on a sampled subset under the IRB protocol. For investment-grade work at MFS, Putnam, or Eaton Vance we add SEC Rule 17a-4 retention on every completion plus a challenger model comparison your model risk committee can sign. The evaluation harness is yours to keep and to extend after we leave.

Yes. We have wired LLMs into Epic via the HL7 FHIR R4 API and Epic's App Orchard / Showroom (now Vendor Services) patterns at Mass General Brigham style clients, into Veeva Vault via the Vault REST API for regulated content authoring at Moderna and Vertex pace biotech, into Bloomberg through B-PIPE and BQL at Fidelity / MFS style investment shops, and into SimCorp Dimension and Charles River IMS at State Street and Wellington style asset managers. The integration layer enforces auth, PHI/PII redaction, and audit logging so the LLM sits behind your existing entitlement model rather than around it. Vendor-required certifications and security reviews are part of scope.

Massachusetts Chapter 93A treats unfair or deceptive AI behaviour as a consumer protection violation, with statutory damages and a fee-shifting provision the AG and private plaintiffs have used aggressively. We ship a model card disclosing capabilities and limitations in plain English, a user-facing disclosure when an LLM is in the loop, and an opt-out path where the use case allows. For personal data we apply 201 CMR 17 (the Massachusetts data security regulation) controls: written information security program, encryption at rest and in transit, role-based access, vendor due diligence, and breach response. Our build deliverables include a Chapter 93A risk memo your general counsel can adopt or amend, not a generic policy pulled from a template library.

Yes, when the data classification or regulatory exposure makes a hosted frontier model untenable. We have fine-tuned Llama 3 8B and 70B, Mistral 7B and Mixtral, and Qwen 2.5 family models on biotech regulatory corpora, asset manager research archives, and insurance claims notes. Training runs on AWS us-east-1 GPU instances (P5 / G6) inside the client VPC, with the resulting weights staying in the client S3 bucket. We deliver SHAP-style retrieval attribution where the architecture allows, document hyperparameters and training data lineage for model risk review, and align with the NIST AI Risk Management Framework. For most Boston use cases a strong RAG layer on Claude or GPT-4 beats fine-tuning on cost and recall; we will say so when that is the right answer.

Our default defences include input validation against known injection patterns (the OWASP LLM Top 10), tool-use allowlists that constrain agent actions to a named set of APIs, output filtering for PHI/PII and known credential formats, and a separate evaluator model that scores responses before they leave the system. For high-value targets at Fidelity, Bain Capital, Liberty Mutual, or Moderna we add a sandboxed retrieval policy that prevents the model from reading documents the requesting user is not entitled to under the client's existing RBAC, plus continuous red-teaming against synthetic injection corpora. Logging captures every prompt, retrieved chunk, tool call, and completion in an immutable WORM store sized for SEC Rule 17a-4 or HIPAA retention, whichever is longer.