Generative AI Company in Boston

Documentation burden separates a Boston copilot demo from a Boston copilot in production. The bands are a scoped Boston generative AI proof of concept over six to ten weeks, covering data audit, a RAG baseline or fine-tuned model, an evaluation harness, and a hosted demo with logging, a custom production copilot (clinical scribe, biotech protocol assistant, mutual fund research copilot, support deflection bot) including evaluation, prompt injection hardening, MRM or clinical engineering documentation, and audit-grade logging, or a full production generative AI system with multi-step agents, multiple models, fine-tuning, and enterprise integrations. Boston rates sit at parity with New York and slightly above Atlanta and Austin because of the MIT, Harvard, and biotech talent premium. USD, fixed-fee.

Yes. We build ambient scribing, discharge summarisation, prior authorisation draft generation, clinical inbox triage, and patient-facing assistants at the scale Mass General Brigham, Boston Children's, Dana-Farber, Beth Israel Deaconess, and Brigham and Women's operate at. We sign Business Associate Agreements aligned with each hospital's template, route PHI exclusively through HIPAA-eligible AWS Bedrock or Azure OpenAI configurations, ship immutable audit logs satisfying 45 CFR 164.312(b), build explicit consent surfaces tied to the clinical workflow, and run prompt injection red-teaming before production push. The FDA January 2025 draft guidance on AI-enabled device software functions and the HHS Office for Civil Rights 2024 guidance on AI in health care are mapped into the design pack. We do not pretend to be a clinical informatics consultancy; we deliver production AI engineering and coordinate with the hospital's informatics team.

21 CFR Part 11 (electronic records and signatures) and EU GMP Annex 11 require closed systems to maintain an audit trail of operator actions on records that document the date and time of operator entries and actions that create, modify, or delete electronic records, plus electronic signatures with two distinct identification components. For generative AI in biotech (protocol drafting, regulatory submission, manufacturing batch record assistance) at Moderna, Vertex Pharmaceuticals, Biogen, Sanofi Cambridge, Pfizer Cambridge, or Takeda, we ship immutable audit trails (typically append-only ledgers in PostgreSQL with cryptographic hash chaining, or AWS QLDB), role-based electronic signature surfaces, IQ/OQ/PQ documentation, and a Computer System Validation pack the sponsor's QA can present to a regulator. The FDA September 2022 draft on Computer Software Assurance reduces validation burden for non-direct-product-impact tools, and we structure scope accordingly.

Yes. We build investment research copilots, regulatory filing automation (10-K, 10-Q, ADV, N-CSR, N-PORT, N-CEN), portfolio commentary drafting, and internal knowledge search for the kind of asset-management operation Fidelity Investments, State Street, MFS, Wellington Management, Putnam, Eaton Vance, BNY Mellon Boston, John Hancock, Liberty Mutual, and Bain Capital run in the city. Deployments stay inside SEC Rule 17a-4 immutable retention (typically AWS Bedrock with CloudTrail to an S3 bucket under Object Lock in Compliance mode, or the client's existing SEC 17a-4 archive on Mimecast or Smarsh), FINRA 4511 books and records, the firm's MRM framework (model risk management) typically modelled on Federal Reserve SR 11-7 even though investment managers are not bank-supervised, and the SEC proposed predictive data analytics rule (Release No. IA-6353, July 2023, still pending as of mid-2026 but anticipated). Every prompt and completion is logged for audit.

When a project requires genuine research (novel architectures, interpretability work, alignment research, formal verification of model behaviour, rare-event detection) we scope collaborations with MIT CSAIL, Harvard SEAS, the Kempner Institute, the MIT-IBM Watson AI Lab, or BU and Northeastern AI faculty rather than overselling in-house capability. Our core team handles applied engineering, RAG, fine-tuning, eval harnesses, MLOps, and productionisation, which is where most Boston generative AI projects actually stall. For standard work (RAG over enterprise knowledge, fine-tuning Llama or Mistral on client data, evaluation, prompt injection hardening) no academic partner is needed. We will tell you up front which bucket your problem fits into, and where the answer is genuine research we will point you at the MIT-IBM Watson AI Lab industry programs and Kempner partnership tracks that exist for exactly that.

Prompt injection is the OWASP top risk for LLM applications and the most common failure mode we see in shipped Boston copilots. Our defence stack runs in layers. At input we use Lakera Guard, NVIDIA NeMo Guardrails, or AWS Bedrock Guardrails for known-pattern filtering, plus structured-input enforcement (the user message never becomes part of the system prompt directly). At retrieval we sanitise indexed documents for instruction-like content, watermark trusted sources, and apply provenance scoring. At generation we use structured output (JSON schema enforcement, function-calling constrained tools) so the model cannot freely emit instructions to downstream systems. At output we re-validate against an allow-list of tool calls and run an OWASP LLM Top 10 review. We run automated red-teaming (Garak, Promptfoo prompt injection suites, internal adversarial prompts) before every production push, and we document residual risk in the model card.

A clinical ambient scribe or discharge summariser at Mass General Brigham, Boston Children's, or Beth Israel scale takes sixteen to twenty-eight weeks from kickoff to production, including HIPAA BAA, prompt injection red-teaming, clinical pilot, and integration with Epic or Meditech. A biotech protocol-drafting copilot at Moderna, Vertex, Biogen, or Sanofi Cambridge scale takes fourteen to twenty-four weeks including 21 CFR Part 11 audit trails and IQ/OQ/PQ. A mutual fund research analyst copilot at Fidelity, State Street, or Wellington scale takes twelve to twenty-two weeks including SEC 17a-4 immutable retention and MRM documentation. A support deflection bot at HubSpot, Wayfair, or CarGurus scale takes eight to sixteen weeks. Fixed-fee.

Most custom generative AI engineering qualifies for the Section 41 federal Research and Development tax credit (which covers wages, supplies, and contract research for qualified research activities meeting the four-part test: permitted purpose, technological in nature, elimination of uncertainty, and process of experimentation). Eligible activity generally includes novel architecture work, fine-tuning experimentation, evaluation harness development, prompt injection defence research, and systematic investigation, not routine integration or off-the-shelf API wiring. Massachusetts also offers a state R&D credit (10% of the federal qualified research expenses incurred in Massachusetts, 7.5% above a base period under M.G.L. c. 63 sec. 38M). We deliver time-tracked logs, technical narratives, experiment histories, and failed-hypothesis documentation aligned with IRS Form 6765. Final eligibility sits with your R&D tax credit consultant and the IRS.