AI Consultant · Boston, MA

Hire an AI consultant in Boston.

Boston's biotech and life sciences companies, including Moderna, Vertex, and Biogen, face AI decisions that require domain-specific judgment. AI in drug discovery has genuine scientific value in protein structure prediction and target identification. It has very limited value in clinical trial design, which remains primarily a human judgment problem. An independent consultant who knows the difference saves a two-year internal project.

FDA's AI/ML action plan creates regulatory uncertainty for AI tools in clinical workflows. HIPAA BAA negotiation with AI vendors takes three to six months when done properly. Both of these constraints affect project timelines and should be part of any AI strategy for a Boston life sciences company.

Fixed engagement, scoped to your stage and priorities. Five to eight weeks. Written deliverables yours to keep.

How we work

Tell us about your AI strategy question.

Where AI has real value in life sciences, and where it does not.

Where the value is real:

Protein structure prediction using AlphaFold-derivative models has changed what is possible in target identification. The scientific validation is peer-reviewed and the capability is not a vendor claim. AI in genomics data analysis, including variant calling and pathway analysis on large proprietary datasets, produces measurable improvements in throughput and reproducibility. Document analysis and literature review acceleration are proven capabilities with clear ROI for research teams.

Where the value is often overstated:

Clinical trial design remains a human judgment problem. AI can surface historical trial data and identify protocol risks, but the core decisions about patient populations, endpoint selection, and statistical design require clinical expertise that AI models do not have. Vendors who claim AI can optimize your clinical trial design are usually selling a data retrieval tool with an AI label attached. Patient selection algorithms for active trials carry FDA regulatory risk that should be evaluated before any vendor is engaged.

The FDA regulatory pathway problem.

FDA's AI/ML Software as a Medical Device framework creates a regulatory category for AI tools that make or inform clinical decisions about individual patients. A clinical decision support tool that outputs a recommendation a clinician acts on may qualify as a medical device under 21 CFR Part 820, requiring either 510(k) clearance or a De Novo request before deployment.

The analysis of whether a tool meets the SaMD definition should happen before development begins. Building a clinical AI tool and then discovering it requires FDA clearance means either delayed deployment, a regulatory submission you did not budget for, or a tool that cannot be used in the clinical workflow it was designed for. Moderna and Vertex have regulatory affairs teams that catch this. Smaller Boston biotechs often do not.

The consulting deliverable for any FDA-adjacent use case includes a regulatory risk assessment: does this tool meet the SaMD definition, what submission pathway would apply if it does, and what design choices reduce regulatory burden without reducing clinical utility.

Vendor due diligence for HIPAA.

Most AI vendors are not BAA-ready out of the box

A vendor who says they are HIPAA compliant has usually completed an internal self-assessment. A vendor who is BAA-ready has a signed BAA template, a documented subprocessor list, and a risk analysis under 45 CFR 164.308(a)(1). The due diligence process to confirm actual BAA readiness takes three to six months when done properly.

Starting due diligence after signing the contract is expensive

The common mistake: a business unit selects an AI vendor and signs a software agreement before legal has reviewed the BAA terms. If the BAA negotiation fails or the vendor cannot meet your requirements, you are either locked into a contract with a non-compliant tool or paying lawyers to unwind the agreement.

Subprocessors are the overlooked risk

An AI vendor who has signed your BAA may pass PHI to subprocessors who have not. Every subprocessor that receives PHI must also have a BAA in place. Ask for the vendor's subprocessor list before signing. A vendor who cannot produce this list does not have their compliance posture in order.

What the due diligence checklist covers

Signed BAA template review, subprocessor list with BAA status for each, risk analysis documentation, breach notification procedure and timeline, cloud infrastructure HIPAA eligibility confirmation, and data residency documentation. We provide this checklist as a deliverable and can review vendor responses against it.

How we work.

1
Discovery call
One hour. We map your data assets, your AI questions, and any existing regulatory or compliance constraints. We scope the engagement and give you a fixed price.
2
Structured interviews
Sessions with research, IT, and a business stakeholder. For life sciences companies, we also speak with regulatory affairs if any use cases touch clinical workflows.
3
Analysis and drafting
Use case prioritization, regulatory risk assessment for FDA-adjacent use cases, HIPAA vendor due diligence checklist, and a 90-day implementation roadmap. Draft delivered for your review.
4
Review and handoff
Final session to walk through findings. All deliverables are yours. No retainer, no ongoing obligation.

Questions about AI consulting in Boston.

How do you evaluate whether an AI drug discovery claim is scientifically credible versus a marketing claim?: Ask for a peer-reviewed publication or a preprint on bioRxiv that describes the model architecture, training data, and validation methodology. A vendor who cannot point to external scientific validation for their drug discovery AI is selling a claim, not a capability. Specifically: ask what target classes the model has been validated on, whether the validation used prospective or retrospective data, and what the false positive rate is on held-out test compounds. Protein structure prediction claims from AlphaFold-derivative models are credible. Claims about predicting clinical trial success from molecular structure alone should be treated skeptically until you see prospective validation data.
What FDA guidance applies to AI tools used in clinical trial design or patient selection?: FDA's AI/ML-Based Software as a Medical Device action plan governs AI tools that qualify as Software as a Medical Device under 21 CFR Part 820. Clinical trial patient selection algorithms can qualify if they influence a clinical decision about an identifiable patient. The relevant guidance documents are the 2021 AI/ML SaMD action plan, the 2023 draft guidance on marketing submission recommendations for predetermined change control plans, and the 2024 final guidance on marketing submissions for device software functions. The key question is whether your AI tool meets the SaMD definition. Tools that analyze de-identified aggregate data for protocol design typically do not. Tools that output individual patient eligibility determinations are more likely to. That determination should happen before you build, not after.
What specific questions should you ask an AI vendor to determine if they are actually BAA-ready versus claiming they are?: Ask for their signed BAA template, not a verbal confirmation. Ask for their HIPAA risk analysis documentation under 45 CFR 164.308(a)(1). Ask whether their subprocessors that receive PHI have also signed BAAs, and ask for the list of those subprocessors. Ask about their breach notification procedure and the timeline for notification under 45 CFR 164.410. Ask whether their cloud infrastructure is covered by AWS HIPAA-eligible services, Azure compliance offerings, or GCP HIPAA-covered services. A vendor who answers all of these questions with documentation is BAA-ready. A vendor who says they are HIPAA compliant but cannot produce a signed BAA template and a subprocessor list is not. Starting vendor procurement before you have a signed BAA in hand means you may need to redo the selection process if the BAA negotiation fails.
What does a typical AI strategy engagement look like for a 200-person Boston biotech that has not done any AI yet?: The engagement starts with a structured inventory of your data assets: what proprietary datasets you have, what quality they are, and which AI use cases those datasets could support. Most 200-person biotechs have more usable data than they realize and less than vendors imply. The use case analysis then maps each candidate to your stage of development. A company in Phase I trials has different AI priorities than a company in Phase III. For a 200-person biotech with no prior AI investment, a typical engagement takes five to six weeks and produces a prioritized use case analysis, a regulatory risk assessment for any FDA-adjacent use cases, a HIPAA vendor due diligence checklist, and a 90-day roadmap for the first implementation.

Ready to work through your AI strategy question?

Hire an AI consultant in Boston.

Fixed engagement, scoped to your stage and priorities. Five to eight weeks. Written deliverables yours to keep.

Where AI has real value in life sciences, and where it does not.

Where the value is real:

Where the value is often overstated:

The FDA regulatory pathway problem.

Vendor due diligence for HIPAA.

Most AI vendors are not BAA-ready out of the box

Starting due diligence after signing the contract is expensive

Subprocessors are the overlooked risk

What the due diligence checklist covers

How we work.

Discovery call

One hour. We map your data assets, your AI questions, and any existing regulatory or compliance constraints. We scope the engagement and give you a fixed price.

Structured interviews

Sessions with research, IT, and a business stakeholder. For life sciences companies, we also speak with regulatory affairs if any use cases touch clinical workflows.

Analysis and drafting

Use case prioritization, regulatory risk assessment for FDA-adjacent use cases, HIPAA vendor due diligence checklist, and a 90-day implementation roadmap. Draft delivered for your review.

Review and handoff

Final session to walk through findings. All deliverables are yours. No retainer, no ongoing obligation.

Questions about AI consulting in Boston.

How do you evaluate whether an AI drug discovery claim is scientifically credible versus a marketing claim?

Ask for a peer-reviewed publication or a preprint on bioRxiv that describes the model architecture, training data, and validation methodology. A vendor who cannot point to external scientific validation for their drug discovery AI is selling a claim, not a capability. Specifically: ask what target classes the model has been validated on, whether the validation used prospective or retrospective data, and what the false positive rate is on held-out test compounds. Protein structure prediction claims from AlphaFold-derivative models are credible. Claims about predicting clinical trial success from molecular structure alone should be treated skeptically until you see prospective validation data.

What FDA guidance applies to AI tools used in clinical trial design or patient selection?

FDA's AI/ML-Based Software as a Medical Device action plan governs AI tools that qualify as Software as a Medical Device under 21 CFR Part 820. Clinical trial patient selection algorithms can qualify if they influence a clinical decision about an identifiable patient. The relevant guidance documents are the 2021 AI/ML SaMD action plan, the 2023 draft guidance on marketing submission recommendations for predetermined change control plans, and the 2024 final guidance on marketing submissions for device software functions. The key question is whether your AI tool meets the SaMD definition. Tools that analyze de-identified aggregate data for protocol design typically do not. Tools that output individual patient eligibility determinations are more likely to. That determination should happen before you build, not after.

What specific questions should you ask an AI vendor to determine if they are actually BAA-ready versus claiming they are?

Ask for their signed BAA template, not a verbal confirmation. Ask for their HIPAA risk analysis documentation under 45 CFR 164.308(a)(1). Ask whether their subprocessors that receive PHI have also signed BAAs, and ask for the list of those subprocessors. Ask about their breach notification procedure and the timeline for notification under 45 CFR 164.410. Ask whether their cloud infrastructure is covered by AWS HIPAA-eligible services, Azure compliance offerings, or GCP HIPAA-covered services. A vendor who answers all of these questions with documentation is BAA-ready. A vendor who says they are HIPAA compliant but cannot produce a signed BAA template and a subprocessor list is not. Starting vendor procurement before you have a signed BAA in hand means you may need to redo the selection process if the BAA negotiation fails.

What does a typical AI strategy engagement look like for a 200-person Boston biotech that has not done any AI yet?

The engagement starts with a structured inventory of your data assets: what proprietary datasets you have, what quality they are, and which AI use cases those datasets could support. Most 200-person biotechs have more usable data than they realize and less than vendors imply. The use case analysis then maps each candidate to your stage of development. A company in Phase I trials has different AI priorities than a company in Phase III. For a 200-person biotech with no prior AI investment, a typical engagement takes five to six weeks and produces a prioritized use case analysis, a regulatory risk assessment for any FDA-adjacent use cases, a HIPAA vendor due diligence checklist, and a 90-day roadmap for the first implementation.

Ready to work through your AI strategy question?