01
Stuck in pilot purgatory
The demo impressed everyone eight months ago. It's still a demo — because nobody designed for accuracy verification, error handling, or the compliance review that was always coming.
Services
AI development services
Most AI projects die between the demo and the deployment — blocked by compliance, unverifiable accuracy, or costs nobody modeled. We build LLM systems designed for the review that kills the others: validated outputs, humans in the loop, and an audit trail your compliance officer will actually sign.
Who this is for
You’ll recognize the situation
02
Your data can't leave the building
Legal has ruled: no customer data in third-party training sets, no PHI outside covered infrastructure. Every vendor pitch you've seen quietly fails that test.
03
Nobody can say if it's working
The model 'seems good' but there's no labeled baseline, no accuracy number, and no way to spot drift — so every executive review reopens the question of whether to kill it.
What’s included
AI development, itemized
LLM application development
Document processing, extraction, and workflow automation built on strict schemas — every output validated against type and plausibility rules before it touches your systems.
Retrieval-augmented generation
RAG over your own corpus with source-grounded answers — every claim carries a pointer back to the document it came from, so users can verify instead of trust.
Human-in-the-loop workflows
Review queues where people confirm high-stakes outputs in one glance — confidence scores routing attention to where the model is least sure.
Evaluation and guardrails
Hand-labeled baselines, per-field accuracy tracking, and monthly drift audits — so quality shows up in a report before it shows up in production.
Private and compliant deployment
Models running inside your boundary or under BAA-covered infrastructure, PHI and PII excluded from logs, prompts version-pinned and change-controlled.
Cost and latency engineering
Model routing, caching, and batch strategies that keep unit economics predictable — you'll know the cost per document before you scale, not after.
How it runs
Four phases, no surprises
01
Diagnose
Two weeks with your documents and workflow, building a hand-labeled evaluation set. If the accuracy ceiling is too low to ship, we tell you now.
02
Plan
An architecture your compliance team reviews before we build it — data boundaries, review points, and audit trail designed in, not bolted on.
03
Build
The pipeline ships against the evaluation set from week one, with accuracy tracked per field and the review UI built alongside the model work.
04
Scale
Drift monitoring, monthly accuracy audits, and cost dashboards — then handover, with your team owning prompts and thresholds through change control.
−71%
intake handling time on a recent engagement
96%
field-level extraction accuracy before review
100%
of records carrying a named human approval
Illustrative figures from anonymized engagement profiles.
Case study
What that looks like shipped
A 14-clinic outpatient network was drowning in faxed referrals. A HIPAA-conscious LLM pipeline — with a human reviewing every record — cut intake handling time 71%.
Read the full case study
In depth
LLM systems that survive compliance review
Most enterprise AI projects don't fail on model quality — they fail in a conference room, when compliance asks three questions the demo was never built to answer: where does the data go, who is accountable for each output, and how do you know it's right. An AI development company that can't answer those questions in architecture, rather than assurances, is building you a very expensive proof of concept.
The systems that pass review share three patterns. Evaluation against a hand-labeled baseline, so accuracy is a measured number per field rather than an impression. Human-in-the-loop review, so a named person approves anything that matters before it touches a system of record. And hard data boundaries — PHI kept inside covered infrastructure, excluded from logs and traces, with prompts and model versions pinned under change control.
That's the shape of the intake pipeline we built for a multi-clinic healthcare provider, a 14-clinic network whose previous AI vendor was abandoned because nobody could audit the product. Ours extracts referral fields into a strict schema, shows each value beside its highlighted source snippet, and routes low-confidence fields to a coordinator first. The compliance officer signed off because the audit trail records what the model proposed and what a human approved — for every record.
The unglamorous half of LLM integration services is everything around the model: OCR and classification upstream, schema and plausibility validation on every field, HL7 FHIR posting downstream, and a review UI fast enough that confirming a value takes one glance instead of a re-key. The model is maybe a fifth of the code. Teams that treat it as the whole product are the teams whose pilots never leave the lab.
When not to use AI — and what to build instead
A useful test before any engagement: can you write down the rules? If a person can enumerate the logic — route claims above a threshold to a senior adjuster, flag invoices that don't match a purchase order — you want deterministic code, not a model. It's cheaper to run, trivial to test, and explains itself in an audit. When a struggling LLM prototype fails this test, the fix we propose is a few hundred lines of validation logic — not a better prompt.
LLMs also lose when volume is low and error cost is high. A team handling thirty documents a day doesn't need a pipeline, and building the review machinery to catch rare, expensive mistakes can cost more than the mistakes. And where no ground truth exists, nobody can label a baseline, which means nobody can measure accuracy, which means you would be shipping something you can't defend. That's a no.
This is why our engagements start with a two-week feasibility phase that is allowed to end the project. We build the labeled evaluation set, measure the accuracy ceiling on your real documents, and model the unit cost. The phase is a genuine kill gate: when the numbers say AI is the wrong tool, the project ends there — an outcome that costs two weeks instead of two quarters, and one most vendors are not structured to deliver.
Measuring AI ROI in cycle time, not benchmarks
Model benchmarks don't appear on a P&L. The number that survives an executive review is cycle time: minutes per document, days of backlog, time from referral to first appointment. Before we write a prompt, we baseline the workflow — how long each step takes, where the queue builds, what a fully loaded hour of the person doing it costs — because ROI is a before-and-after comparison, and you need the before.
At the healthcare provider, that framing produced numbers finance could use: median handling time fell from 18 minutes to just over 5, a four-day backlog cleared within six weeks of go-live, and time-to-first-appointment dropped by three days. Field-level accuracy — 96% before review — appears in the monthly audit, but nobody funded the project for it. They funded the cleared backlog.
Unit economics get the same treatment. Cost per document is modeled in the feasibility phase — model calls, review minutes, infrastructure — and compared against the manual cost it replaces. Routing keeps it honest: a small model classifies, a larger one extracts, caching absorbs the repeats. If the pipeline plus human review costs more per document than the person it assists, we will tell you, because shipping it anyway just moves the loss onto a dashboard.
Tools we reach for
A proven, hireable stack
Chosen so your team can maintain, extend, and hire for everything we leave behind.
- Python
- TypeScript
- PostgreSQL
- pgvector
- AWS Bedrock
- FastAPI
- Docker
- Terraform
FAQ
The questions buyers actually ask
Answered the way we’d answer them on a call — specifics included.
That uncertainty is exactly why we start with a fixed-price feasibility phase: two weeks, a labeled evaluation set, and a measured accuracy ceiling. If the numbers don't support a build, you've spent two weeks — not two quarters. The build itself then runs time-and-materials against milestones with the evaluation gate at each one.
You own everything we write — pipeline code, prompts, evaluation sets, and infrastructure, all in your repositories with IP assigned on payment. Where we use foundation models, they run under your accounts and agreements, so there's no dependency on ours.
At least four contracted hours with your working day. AI builds live or die on fast feedback from your domain experts, so we schedule review sessions inside your hours and keep the labeling and threshold decisions in a shared channel your team can see.
The feasibility phase is the ramp-up: by the end of two weeks we've processed a sample of your real documents and built a labeled baseline with your experts. Domain knowledge gets encoded into schemas and validation rules — not left in one engineer's head.
Yes — we build LLM systems so that no PHI leaves BAA-covered infrastructure and nothing enters any model's training set. Data boundaries are designed with your compliance team before code is written, PHI is excluded from logs and traces, and the audit trail records what the model proposed and what a human approved.
You'll know before production: the evaluation set gates every milestone, and the human-review layer means low-confidence outputs route to people rather than into your systems. If drift degrades quality later, the monthly audit catches it. And as with all our work, you can stop at any milestone with 30 days' notice.
No. What we hand over is prompts, schemas, and thresholds under change control — closer to configuration than research. A backend engineer on your team can own it after handover, supported by a runbook and the monthly accuracy audit, which is a report to read rather than an experiment to run. If a change ever does need model work — a new document type, a new language — that's a scoped engagement, not a standing team.
The pipeline treats the model as a swappable component behind one interface, and the evaluation set is your insurance policy: point a candidate model at it, compare per-field accuracy and cost, and promote it only if it wins. Model versions are pinned, so upgrades happen when you choose them, not when a vendor pushes them. Model churn becomes a routine eval run instead of a rewrite.
Cost per document is modeled in the feasibility phase and tracked on a dashboard from the first week of the build — model calls, retries, review minutes, infrastructure. The engineering that keeps it flat is routing and caching: a small model handles classification, the large one runs only where extraction is hard, and repeated content never pays twice. You'll know the unit cost curve before you commit to volume, and the dashboard flags drift in spend the same way the audit flags drift in accuracy.
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