Get AI out of the pilot and into production.
We find where AI actually moves your numbers, prove it against your own data, and ship it into the systems your business already runs on — inside your cloud, with the audit trail your compliance team will ask for.
- Evals
- Fine-tuning & RFT
- Agents
- Voice AI
- Document AI
- Embedded engineers
- →routeintent: billing_dispute → agent.v4
- →retrieve12 passages · your policy corpus
- →tool_callcrm.lookup(account) · 240ms
- →eval_gategrounded 0.96 · PII clean · passed
- →resolveanswered · 1 of 43 escalated to a human
Engineers who have shipped at
The real problem
Most AI projects die after the demo
Not because the technology failed. Because nobody could prove what it was worth, nobody tested it on real inputs, and nobody got it past security. That’s the part we do differently.
Nobody can say what it's worth
The demo impressed everyone and then sat there, because no one measured what it saves, what it earns, or what it costs to run.
We size the opportunity in hours and dollars before a line of code is written.
It works in the demo, not on your data
Accuracy that looks fine on ten test questions falls apart on the messy, real inputs your team deals with every day.
We build an eval set from your real cases and prove the accuracy before launch.
Legal and security stop it at the door
The moment it touches patient records, client files, or payment data, the off-the-shelf tool is disqualified.
We build inside your cloud, sign a BAA, and hand you the audit trail.
System anatomy
We build the whole stack
Most vendors sell you one layer and leave the rest to you. Here’s every layer of a production AI system, and what we deliver at each.
Interface
Where your customers and staff actually meet the system.
Agents & orchestration
The part that does the task instead of talking about it.
Model layer
The right model for the job, tuned to your language.
Knowledge & data
Answers from your business, not from the open internet.
Evaluation & guardrails
The proof that it behaves, before and after launch.
Run & observe
Keeping it fast, cheap and accurate once it's live.
What we deliver
Everything from first audit to live system
Every engagement is built for your workflow and your data. No reselling someone else’s chatbot.
AI opportunity audit
Start hereFind the AI work that actually pays
We map every candidate use case against the hours it saves and the revenue it protects, then tell you which one to build first — and which ones to leave alone.
- Use-case scoring in hours and dollars
- Build, buy or skip recommendation
- A costed roadmap you own
Forward-deployed engineering
Put a senior AI engineer inside your team
Our engineer works in your repo, your stack and your standups — turning your team's idea into a working system instead of another backlog ticket.
- Embedded in your workflow, not a black box
- Prototype to production in weeks
- Your team keeps the knowledge
Evaluation & benchmarking
Prove it works before it touches a customer
We turn your real cases into a scored test suite, so every model, prompt and release is measured against the same bar instead of argued about in a meeting.
- Golden dataset built from your cases
- Model bake-off on your own data
- Regression gates in your pipeline
Fine-tuning, RFT & distillation
Make the model an expert in your business
When general models get your terminology, tone or edge cases wrong, we train on your data — supervised or reinforcement — and distill it down so it runs cheaper and faster.
- Supervised and reinforcement fine-tuning
- Distilled for lower cost and latency
- Benchmarked against the base model
Voice AI
Stop losing the calls nobody answers
A voice agent that picks up every call, books the appointment, answers the routine question, and hands the real ones to your team with the context already gathered.
- Answers 24/7, no hold queue
- Books straight into your calendar
- Clean handoff with a call summary
Document AI
End the manual paperwork shift
Intake forms, invoices, claims, contracts and scans read, extracted and filed into your systems — with a confidence score and a human check where it matters.
- Extraction from messy real-world files
- Confidence scoring and review queue
- Straight into your systems of record
Agent engineering
Agents that finish the task, not just chat
We connect models to your tools and data through MCP and typed integrations, so the system books, updates, files and escalates instead of producing text about it.
- Tool use and MCP integrations
- Multi-step workflows with approvals
- Human-in-the-loop where stakes are high
Knowledge & RAG
Answers from your business, not the internet
Your policies, product docs, contracts and history made searchable and answerable — with citations, so anyone can check where the answer came from.
- Retrieval tuned on your corpus
- Cited, checkable answers
- Access rules follow the user
LLMOps & governance
Keep it accurate after launch day
Live systems drift. We instrument yours with tracing, cost and latency budgets, and scheduled evals, so you find out before your customers do.
- Tracing and cost control
- Scheduled eval runs and alerting
- Named contact and response times
Not sure which of these you need? .
How we build
Nothing ships on a hunch
Four stages, each with a gate that has to be cleared before we move on. It’s slower to promise and far faster to finish.
- 01
Discovery & Scoping
A short working session with the people doing the work, to find where the time and money actually goes.
Gate: one use case with a number attached to it.
- 02
Define Success Metrics
We assemble a scored eval set from your real cases and benchmark candidate models against it.
Gate: a target accuracy everyone has agreed to.
- 03
Production Launch
One end-to-end workflow in production — integrated, monitored, and handling real volume.
Gate: it beats the eval bar on live traffic.
- 04
Scale & Optimize
Expand coverage, tune cost and latency, and wire evals into your release pipeline so quality can't regress silently.
Gate: regressions fail the build, not the customer.
Ways to work with us
Start where it makes sense
Four entry points, from a two-week audit to engineers embedded in your team. You can change shape later.
AI opportunity audit
2 weeks · fixed fee
A fixed-fee sprint that ends with a scored use-case shortlist, a costed roadmap, and an honest answer on whether to build at all.
Best for teams who know AI matters but not where to start
Production build
Fixed scope · fixed price
A defined system, a defined price. We design, build, evaluate and deploy it into your stack, then support it.
Best for a specific system you already want shipped
Embedded engineers (FDE)
Month to month
Senior AI engineers working inside your team on your roadmap, shipping alongside your people month to month.
Best for teams with the direction but not the AI capacity
Managed AI operations
Ongoing retainer
We keep live systems accurate and affordable — monitoring, scheduled evals, model upgrades and on-call cover.
Best for AI already in production that has to keep behaving
Model-agnostic by design
We pick the model that wins on your data
Not the one we have a deal with. And we build so it can be swapped next year without a rewrite.
Models
Agents & orchestration
Voice
Evals & observability
Data & retrieval
Cloud & delivery
Shipped, not theoretical
We cut a hiring cycle in half for Creva.ai
Voice interviews, resume screening and candidate ranking, running autonomously — so recruiters only spend time on the shortlist that matters.
What we delivered
- AI voice interview agent with real-time scoring
- Resume parser and job-description match engine
- Automated candidate ranking dashboard
- ATS integration for a clean pipeline handoff
- Recruiter analytics: bottleneck detection and insights
AI stack used
AI that meets your governance, security and compliance standards
When your work involves patient records, client files or payment details, everyday AI tools often aren’t allowed. Here is how we protect your data and keep every system accountable.
HIPAA-compliant, with a signed BAA
We build to HIPAA requirements and sign a Business Associate Agreement before any work involves patient data.
Deployed in your own cloud
Your data stays inside your infrastructure. You own the code, the prompts and the test data we create.
Your data is never used for training
We use enterprise AI services with training turned off, and remove sensitive details before any data leaves your systems.
A full audit trail
Every question, source, action and answer is recorded, so you can show a regulator or client exactly what happened.
People approve high-risk decisions
You decide which actions need human sign-off. The system pauses and waits for approval instead of acting on its own.
Access follows your existing permissions
The AI only sees and shares what each user is already allowed to access, using the roles you manage today.
Need this documented for your security review? .
Questions buyers actually ask
We build a scored test set from your own real cases before development starts, and agree a target the system has to hit. Every release is measured against it, so accuracy is a number you can check rather than an opinion.
A senior engineer from our team who works inside yours — in your repo, your tools and your meetings — instead of delivering from behind a wall. It's the fastest way to get an idea from your team into production without hiring for AI skills you only need for a few months.
Usually not first. Better prompts, retrieval and tooling solve most problems more cheaply. We fine-tune when the general model keeps getting your terminology, tone or edge cases wrong, or when distilling to a smaller model would cut your running costs significantly.
Whichever one wins on your data. We benchmark candidates — including Claude, OpenAI, models on AWS Bedrock, and open-weight options — against your eval set, and we build so the model can be swapped later without a rewrite.
Yes. We deploy inside your cloud account, use model endpoints that don't train on your data, redact sensitive fields, log every decision for audit, and will sign a Business Associate Agreement for healthcare work.
An opportunity audit takes a couple of weeks. A first production workflow is typically live in weeks rather than quarters, because we ship one end-to-end slice first instead of building everything before anything works.
Tell us what’s costing you the most.
We’ll tell you honestly whether AI is the right answer, what it would take, and what it would be worth. In plain language, no jargon.