AI Consulting in Cambridge

Yes, and it's one of the higher-leverage applications for biotech operators in Kendall Square. The core problem is that submission packages — IND amendments, NDA modules, CMC sections — require pulling from multiple source systems (lab notebooks, formulation records, stability data repositories) and assembling them into a structured format that meets specific FDA guidance. That assembly work is currently done by scientists or regulatory affairs staff who check documents manually against a checklist and rebuild the package from scratch each submission cycle. Workflow automation handles the retrieval and assembly layer: pulling from defined source folders, applying the right document templates, flagging missing sections, and generating a first-draft package the regulatory lead reviews rather than authors. The human still reviews and signs off — that step doesn't change. What changes is how many hours they spend on rote assembly versus actual scientific review. This is not legal advice on FDA compliance; your regulatory counsel owns that piece.

SBIR-funded spinouts are actually a good fit for fixed-price automation builds precisely because budget is constrained and every hour of founder time has a high opportunity cost. The $99 AI readiness audit is a reasonable first step — it maps where your operational drag is actually coming from before any build is scoped. Common high-value targets at this stage: NIH progress report drafting from existing lab data, invoice-to-subcontract matching for budget reconciliation, and onboarding flows for new lab members or contractors who need to get productive without burning the PI's week. The Founder Review Call at $497 is designed for this stage — ninety minutes, written prioritization memo, no commitment to a build. If the audit or call surfaces a workflow that saves more than it costs in the first quarter, the math works even on a Phase I budget. If it doesn't, we'll tell you that, too.

Data scoping is the first conversation, not an afterthought. For research-adjacent workflows, we use read-only API connections to source systems wherever possible — pulling from a defined folder structure or a specific database view rather than broad system access. For AI-assisted steps, we route through enterprise model endpoints (Anthropic, Azure OpenAI) that carry contractual no-training, zero-retention terms, so your formulation data or unpublished sequence information is not used to train any model and is not retained after the request completes. The signed data processing terms are part of the engagement file before any credential changes hands. If your counsel or tech transfer office has specific requirements around data residency or export controls (common for ITAR-adjacent research), those constraints get mapped in the audit phase and baked into the architecture. We've worked with teams that have very strict data-handling requirements; the builds take longer to scope, but the approach is the same.

Internal ML talent and operational automation solve different problems and rarely overlap. Your researchers are building models, running experiments, and publishing. Workflow automation handles the connective tissue between tools: routing meeting notes to the right project folder, syncing grant milestone tracking to your project management system, generating first-draft reports from structured data so a researcher isn't starting from a blank doc. The builds we scope don't require your ML team's involvement and don't compete with their priorities. In practice, internal AI teams often have the longest list of operational frustrations because they see exactly where the process is broken and don't have cycles to fix it themselves. The audit conversation tends to move fast with technical teams because they can articulate the problem precisely — which shortens the scoping work and usually gets a first build into production faster than with less technical operators.

It depends on what the audit surfaces, but two patterns show up most often. For biotech R&D teams, the first build is usually a document assembly or reporting automation — something that takes a repeating, high-stakes document workflow (regulatory filings, progress reports, CMC sections) and produces a structured first draft from source data the team already has, rather than requiring a scientist to assemble it from scratch. For health-tech and mid-market tech firms, the first build is more often an internal knowledge or routing system — a tool that answers common questions from the team's own documentation, routes support tickets correctly, or handles the onboarding checklist for new hires without requiring a senior person to run it manually. We rarely recommend starting with more than one capability. One thing deployed and working in four weeks beats three things half-built in three months.