LLM Integration · Boston, MA

LLM integration in Boston.

Boston's software runs into regulators early. Health systems and digital-health products carry HIPAA. Biotech R&D tools live near GxP. Student software answers to FERPA. LLM integration here means embedding language models into those products with the compliance architecture designed in from the first commit, because retrofitting it is a rebuild.

We embed LLM features into your existing product: clinical text extraction, literature summarization, drafting, classification, and grounded search. Prompt design, model benchmarking on your data, structured output, and audit logging are part of the standard build.

How we work

Tell us what the feature does and which regulations it touches.

The technical reality of LLM features in Boston software.

Healthcare integrations are constrained more by the data path than the model. PHI requires BAA-eligible inference, private networking, minimum-necessary prompts, and audit logs. Within those constraints, the feature set is wide open: visit-note summarization, message drafting for clinician review, intake classification, coding support. The discipline is keeping the human in the loop where clinical judgment lives and making the model's uncertainty visible instead of smoothing it over.

Biotech text is its own dialect. Gene symbols, assay names, and pipeline shorthand defeat generic prompts, so we build domain glossaries with your scientists and force extractions through typed schemas with confidence scores. The evaluation set gets labeled by people who can tell a right answer from a plausible one, which in this domain is not the same skill as engineering.

Literature features earn trust through citations or lose it permanently. Retrieval-grounded generation with paper-and-section citations, plus an explicit refusal when the corpus is silent, is the only pattern we ship to scientific audiences.

Higher-ed and edtech products inherit FERPA's school-official framework, which is mostly a contracts-and-logging problem with light engineering attached: subprocessor terms, minimal student data in prompts, disclosure logs. The teams that handle it during the build close institutional deals months faster than the teams that improvise during procurement review.

What we build for Boston teams.

Six integration patterns we scope most often for healthcare, biotech, and education software.

HIPAA-eligible inference path

Azure OpenAI or Bedrock inside your tenancy, private networking, minimum-necessary prompt construction, and audit logging mapped to your existing retention policy.

Clinical text extraction

Typed-schema extraction over notes and intake documents with specialty glossaries, confidence scores, and clinician-labeled evaluation before any production traffic.

Literature and report summarization

Retrieval-grounded summaries with DOI/PMID-level citations and a tuned refusal path, over published literature and internal study reports alike.

Clinician- and staff-facing drafting

Message and documentation drafts that route through human review by design, with the edit distance tracked so you can see the feature earning its keep.

FERPA-aware student features

School-official subprocessor configuration, minimal-data prompts, and disclosure logging, documented so institutional procurement reads it without a meeting.

Evaluation with domain reviewers

Labeled eval sets built with your clinicians or scientists, regression runs on every change, and quality reporting your compliance reviewers accept.

Built for Boston's regulated software economy.

Boston's concentration of healthcare systems, biotech and life-sciences R&D, and universities means the software built here meets compliance review earlier than software built almost anywhere else. The integration patterns that survive are the ones designed for that review: auditable data paths, grounded outputs, visible uncertainty, and paperwork that answers counsel's questions before they are asked.

Deployment targets follow the institution. Health systems and digital-health vendors mostly land on Azure OpenAI or Bedrock inside their own tenancy. Biotech teams split between cloud tenancy and self-hosted open-weight models when pipeline data policy demands it. Edtech runs on enterprise endpoints with zero-retention terms and FERPA-shaped contracts.

We work with Boston teams remotely, with security reviews and weekly demos on video in Eastern hours. Typical engagements run two to six weeks, with regulated data paths landing at the longer end.

Questions from Boston buyers.

Our product touches PHI. What does a HIPAA-compliant LLM feature actually require?: Four concrete things. Inference through a BAA-eligible channel: Azure OpenAI under the Microsoft BAA or Anthropic models on AWS Bedrock, never a consumer API key. A data path where PHI stays inside your cloud tenancy, with the model endpoint reached over private networking. Audit logging of every request that touches PHI (user, timestamp, input, output) to storage with your existing retention policy. And minimum-necessary discipline in prompt construction, so the model sees the fields the feature needs rather than the whole chart. We design this as the skeleton of the integration, not a layer bolted on after the demo, because the bolt-on version always means rebuilding the data path.
Clinical notes are full of abbreviations and shorthand. Do general models handle them?: Better than most teams expect, imperfectly enough to matter. Frontier models resolve common clinical abbreviations well, but ambiguous shorthand (does 'MS' mean multiple sclerosis, mitral stenosis, or morphine sulfate?) needs context the prompt has to supply. Our standard approach: a glossary of your specialty's abbreviations injected into the system prompt, extraction schemas that force the model to commit to an expansion with a confidence score, and an evaluation set labeled by a clinician on your team. For high-stakes fields we add a rule that unresolved ambiguity renders as flagged-for-review rather than a guess. Measured on real notes, that combination typically moves accuracy from demo-grade to deployable.
Can an LLM feature summarize research literature with citations our scientists can trust?: Yes, if retrieval grounds it. The pattern that works for biotech and academic teams: papers and internal reports are ingested with their metadata (DOI, PMID, section structure), the model answers only from retrieved passages, and every claim in the summary carries a citation to the specific paper and section. When the corpus does not support an answer, the feature says so instead of improvising, because a fabricated citation costs you the scientific audience permanently. We tune the refusal threshold with your scientists during the pilot. The same plumbing handles internal study reports, conference abstracts, and grant text, with access controls deciding who sees what.
We build student-facing software. How does FERPA change the integration?: FERPA treats education records the way HIPAA treats PHI, with a school-official exception doing the work a BAA does in healthcare. Practically: your contracts with institutions need to cover the model vendor as a subprocessor under the school-official provisions, prompts should carry the minimum student data the feature needs, and disclosure logging needs to show which records a given request touched. Vendor choice matters less than configuration; the enterprise tiers of the major providers all support zero-retention operation. We document the data flow per institution-facing requirements, because your university customers' counsel will ask, and the deals move faster when the answer is already written down.

Ready to scope your integration?

Tell us the feature, the data it touches, and the regulatory scope. We reply within one business day with a rough scope and a fixed price range.

LLM integration in Boston.

The technical reality of LLM features in Boston software.

What we build for Boston teams.

Six integration patterns we scope most often for healthcare, biotech, and education software.

HIPAA-eligible inference path

Azure OpenAI or Bedrock inside your tenancy, private networking, minimum-necessary prompt construction, and audit logging mapped to your existing retention policy.

Clinical text extraction

Typed-schema extraction over notes and intake documents with specialty glossaries, confidence scores, and clinician-labeled evaluation before any production traffic.

Literature and report summarization

Retrieval-grounded summaries with DOI/PMID-level citations and a tuned refusal path, over published literature and internal study reports alike.

Clinician- and staff-facing drafting

Message and documentation drafts that route through human review by design, with the edit distance tracked so you can see the feature earning its keep.

FERPA-aware student features

School-official subprocessor configuration, minimal-data prompts, and disclosure logging, documented so institutional procurement reads it without a meeting.

Evaluation with domain reviewers

Labeled eval sets built with your clinicians or scientists, regression runs on every change, and quality reporting your compliance reviewers accept.

Built for Boston's regulated software economy.

We work with Boston teams remotely, with security reviews and weekly demos on video in Eastern hours. Typical engagements run two to six weeks, with regulated data paths landing at the longer end.

Questions from Boston buyers.

Our product touches PHI. What does a HIPAA-compliant LLM feature actually require?

Four concrete things. Inference through a BAA-eligible channel: Azure OpenAI under the Microsoft BAA or Anthropic models on AWS Bedrock, never a consumer API key. A data path where PHI stays inside your cloud tenancy, with the model endpoint reached over private networking. Audit logging of every request that touches PHI (user, timestamp, input, output) to storage with your existing retention policy. And minimum-necessary discipline in prompt construction, so the model sees the fields the feature needs rather than the whole chart. We design this as the skeleton of the integration, not a layer bolted on after the demo, because the bolt-on version always means rebuilding the data path.

Clinical notes are full of abbreviations and shorthand. Do general models handle them?

Better than most teams expect, imperfectly enough to matter. Frontier models resolve common clinical abbreviations well, but ambiguous shorthand (does 'MS' mean multiple sclerosis, mitral stenosis, or morphine sulfate?) needs context the prompt has to supply. Our standard approach: a glossary of your specialty's abbreviations injected into the system prompt, extraction schemas that force the model to commit to an expansion with a confidence score, and an evaluation set labeled by a clinician on your team. For high-stakes fields we add a rule that unresolved ambiguity renders as flagged-for-review rather than a guess. Measured on real notes, that combination typically moves accuracy from demo-grade to deployable.

Can an LLM feature summarize research literature with citations our scientists can trust?

Yes, if retrieval grounds it. The pattern that works for biotech and academic teams: papers and internal reports are ingested with their metadata (DOI, PMID, section structure), the model answers only from retrieved passages, and every claim in the summary carries a citation to the specific paper and section. When the corpus does not support an answer, the feature says so instead of improvising, because a fabricated citation costs you the scientific audience permanently. We tune the refusal threshold with your scientists during the pilot. The same plumbing handles internal study reports, conference abstracts, and grant text, with access controls deciding who sees what.

We build student-facing software. How does FERPA change the integration?

FERPA treats education records the way HIPAA treats PHI, with a school-official exception doing the work a BAA does in healthcare. Practically: your contracts with institutions need to cover the model vendor as a subprocessor under the school-official provisions, prompts should carry the minimum student data the feature needs, and disclosure logging needs to show which records a given request touched. Vendor choice matters less than configuration; the enterprise tiers of the major providers all support zero-retention operation. We document the data flow per institution-facing requirements, because your university customers' counsel will ask, and the deals move faster when the answer is already written down.

Ready to scope your integration?

Tell us the feature, the data it touches, and the regulatory scope. We reply within one business day with a rough scope and a fixed price range.