AI & Machine Learning Company in Boston

Whether PHI touches the pipeline is the first question on any Boston ML budget, and it changes everything downstream. From there: a scoped AI proof of concept at Boston rates over six to ten weeks, covering data audit, a baseline model, and a hosted demo with HIPAA controls if PHI is in scope, a custom production ML model (fraud scoring, oncology decision support, document extraction, protein property prediction) including MLOps, monitoring, a model card, and a 201 CMR 17 WISP update, or a full production AI system with RAG, multiple models, fine-tuning, and FDA SaMD documentation. Boston rates sit above most US metros because of the MIT, Broad, and Mass General Brigham talent premium and the regulatory overhead biotech and healthtech work carries. We give fixed-fee proposals rather than open time and materials estimates so your finance team can plan against an actual number.

When a model meets the FDA’s SaMD definition, our discovery phase runs a pathway assessment to decide between 510(k), De Novo, or a Predetermined Change Control Plan (PCCP) under the FDA’s 2023 guidance. We design the development process against the FDA’s Good Machine Learning Practice guiding principles, document intended use, predicate devices, and clinical evaluation strategy from week one, and build validation evidence into every sprint rather than reconstructing it the week before submission. Outputs include a model card, a clinical validation report, software lifecycle documentation aligned with IEC 62304, cybersecurity documentation aligned with the FDA premarket cybersecurity guidance, and a PCCP if the system will continue to learn post-clearance. Final regulatory strategy stays with your regulatory affairs lead and your FDA counsel.

Yes. We ship RAG systems, ambient documentation pilots, clinical summarisation, and agent workflows for Boston hospitals and biotech. For PHI workloads we stay inside HIPAA-eligible AWS services (Bedrock, SageMaker, HealthLake, Comprehend Medical) under a signed Business Associate Agreement, or Azure OpenAI through the HIPAA-eligible tier when the client is Microsoft-aligned. We pattern deployments after public work from Mass General Brigham’s AI program and the Dana-Farber AI initiative, including strict PHI redaction with Comprehend Medical, prompt injection defences, human-in-the-loop review gates for any output that reaches a clinician, and evaluation harnesses that run before every production push. Output logs feed existing hospital SIEM, audit, and quality management systems so your privacy officer is not chasing data after the fact.

Massachusetts 201 CMR 17.00 is the Standards for the Protection of Personal Information of Residents of the Commonwealth. Every company that owns or licenses personal information about a Massachusetts resident must maintain a documented Written Information Security Program (WISP) covering administrative, physical, and technical safeguards, encryption of personal data in transit and at rest on portable devices, vendor oversight, and incident response. Our Massachusetts engagements ship with a 201 CMR 17 risk classification, an attached WISP update describing how the AI system handles personal information, encryption controls on all training and inference paths, and an updated vendor management entry so your compliance lead can answer an Attorney General inquiry without scrambling. We treat this as table stakes, not a billable extra.

When a project requires genuine research (novel model architectures, biology-specific foundation models, rare-event detection, or reinforcement learning in production), we scope collaborations with MIT CSAIL affiliated faculty, MIT-IBM Watson AI Lab researchers, or Broad Institute groups rather than overselling in-house capability. Our core team handles applied engineering, MLOps, validation, and productionisation, which is where most Boston AI projects actually stall on the way from a Nature Methods preprint to a working hospital deployment. For standard work (RAG, fine-tuning, classical ML, imaging on established architectures, retrieval over scientific literature) no academic partner is needed. We will tell you up front which bucket your problem fits into, and we have introduced Boston clients to MIT and Broad sponsorship programs when the roadmap genuinely needs that depth.

Defense AI work in the Boston Route 128 belt (BAE Systems FAST Labs, Draper, MITRE Bedford, Lincoln Laboratory adjacencies) frequently touches export-controlled technical data under ITAR (USML categories for defense articles) or EAR (Commerce Control List dual-use items). Our defense engagements run behind a documented Technology Control Plan, with US-person staffing on any role that touches controlled technical data, segregated infrastructure inside AWS GovCloud (US) or on premise inside the prime’s accredited environment, and access logging suitable for a State Department or BIS audit. We do not stand up offshore access to ITAR-controlled data under any commercial pressure. Final export classification stays with your empowered official and your trade compliance counsel; we build the engineering controls that make their job defensible.

We default to AWS us-east-1 in Northern Virginia and us-east-2 in Ohio for storage, training, and inference, both inside the HIPAA-eligible service catalog under a signed BAA when PHI is involved. Azure East US and East US 2 are the equivalent fallback for Microsoft-aligned clients. For genomic and federally funded research workloads we follow NIH guidance on cloud platforms and dbGaP-controlled access where applicable. For LLMs that must stay inside a hospital boundary or a biotech’s own VPC we self-host Llama 3, Mistral, or BioMedLM on EC2 GPU instances or on premise inside the client’s data center. We document residency, BAA coverage, and any cross-border processing in the model card and the data processing agreement so HIPAA auditors and IRB reviewers have a clear answer.

A typical Boston biotech or hospital model (oncology decision support, imaging triage, protein property prediction, clinical document extraction) takes sixteen to twenty-six weeks from kickoff to production, assuming clean historical data, IRB or research committee approval secured in parallel, and validation documentation as part of scope. Week 1 to 4 is data audit, IRB scoping, and baseline. Week 5 to 14 is modelling, iteration, clinical validation, and bias review. Week 15 to 22 is MLOps, monitoring, shadow mode deployment, and internal validation review. Week 23 onward is gradual rollout under a documented monitoring plan. If FDA submission is in scope, add eight to sixteen weeks for the submission package and predicate device work. We have shipped to this cadence inside HIPAA-regulated stacks on the Longwood Medical Area side and the Kendall Square side.