AI Integration — our deepest focus
AI that survives contact with your business.
The demo is the easy part. The hard part is everything after: what happens when the model is confidently wrong, when a customer sees a hallucinated answer, when the invoice from your provider arrives, and when your data ends up somewhere you didn't intend.
The gap
Most AI projects don't fail technically.
They fail because nobody decided what the system should do when the model gets it wrong — and in production, it will.
A prototype that works eight times out of ten is a great demo and an unacceptable product. The engineering that closes that gap is unglamorous: a defined response contract, screening on the way out, a plan for the failure case, and a cost model you can forecast before you commit.
That engineering is what we do. It's also what our founder wrote a book about.
Questions we ask before writing code
- What does the system do when the model returns something wrong or unusable?
- Who sees the output — an employee who can sanity-check it, or a customer who can't?
- What data leaves your building to make this work, and does it need to?
- What does this cost per query, and what does that become at ten times the volume?
- If your provider changes pricing, terms, or models next year, what breaks?
- How will you know if quality degrades six months from now?
Where it pays off
The work AI is genuinely good at.
These are the patterns that reliably return more than they cost for a small or mid-sized business. Everything else deserves a harder look before you fund it.
Paperwork & document processing
Pulling structured data out of invoices, forms, contracts, and PDFs that currently get retyped by hand. High volume, well-defined, and easy to verify.
Search across your own knowledge
Letting staff ask questions in plain language against your documents, policies, and history — with citations back to the source so answers can be checked.
Inquiry triage & routing
Classifying, summarizing, and routing incoming email, tickets, and requests so the right person sees the right thing without a human sorting the queue.
Drafting & summarizing
First-draft quotes, reports, summaries, and correspondence that a person reviews and sends. The reviewer stays in the loop; the blank page disappears.
Structured extraction at scale
Turning unstructured notes, transcripts, and free-text fields into clean records your existing reporting can finally use.
AI-native products
New applications where the AI is the product, not a feature — built with the response contract, guardrails, and cost model designed in from the first line.
Deployment
Your data doesn't have to leave the building.
There is more than one place a model can run, and the right choice is a business decision as much as a technical one. We build so that decision stays reversible.
On the device
The model runs on the phone, tablet, or workstation itself. Nothing is transmitted, there is no per-query cost, and the feature works with no connection at all.
We ship this in production today — our own app runs fully offline inference.
On your network
A model hosted on a server you own, reachable only from inside your network. Full control over data residency, no usage billing, and capacity you can size to your load.
Often the right answer for regulated or confidential workloads.
In the cloud
Frontier models via a managed provider when the task genuinely needs the extra capability. Metered, scalable, and appropriate when the data involved permits it.
We work across major providers rather than reselling one.
The architecture that makes it work
One response contract. Any provider behind it.
We build a provider-agnostic layer: your application talks to a single, stable response schema, and the model behind that schema is a configuration choice. An on-device model, a server on your own network, and a cloud provider are interchangeable without touching application code.
- No lock-in Provider pricing changes, model deprecations, and terms revisions become a config change instead of a rewrite.
- Test cheap, run appropriate Develop against a local model at zero cost; deploy against whatever the workload actually justifies.
- Graceful degradation If a provider is down or a budget is hit, traffic falls back to a lower tier rather than failing outright.
Built into the response path
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A defined response schema
Every response conforms to a contract your code can rely on. Malformed output is caught at the boundary, never rendered to a user.
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Automated screening
Content is checked against domain-specific hazard rules before it reaches anyone — the same approach that ships in our own consumer product.
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Explicit refusal handling
When the system shouldn't answer, it says so clearly and routes to a human, rather than improvising something plausible.
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Traceability
Prompts, responses, and decisions are logged so you can investigate a bad output rather than guess at it.
Guardrails
The part that isn't in the demo.
Guardrails belong in the pipeline, not in a policy document. If safety depends on a human remembering to check, it will fail on a busy Friday.
We build screening and refusal handling into the response path itself, so every answer passes through it — including the ones nobody anticipated.
Next step
Bring us the workflow, not the buzzword.
Tell us what your people spend their time on. We'll tell you which parts AI would genuinely improve, which parts it wouldn't, and what each would cost to run.