AI development company for custom AI solutions that survive production.

An agent is software that reads a situation, decides, and takes an action inside a system that matters. That last clause is where most projects fail. Something that answers questions is a demo. Something that issues a refund, updates a policy record or routes a ticket is a production system with every obligation that implies.

We build agents with three properties before anything else. They cite their evidence, so a person can check the reasoning in a glance rather than trusting a paragraph. They carry a confidence threshold, below which the work goes to a human with the context already assembled. And they cannot take a destructive action alone.

Veyra is the clearest example. A triage agent inside an existing helpdesk now resolves 61% of tickets without a human, median first response moved from four hours to eight minutes, and satisfaction went up rather than down. The 39% it does not touch reach a person with the account and billing context already attached.

That design is not caution for its own sake. It is what makes the number hold at ninety days instead of quietly decaying in month two, when the easy cases have all been handled and what is left is the awkward tail.

Most companies do not need a new AI product. They need the software they already run to do three specific things it cannot do today, and those three things happen to need a model.

We do that as an integration rather than a rebuild: a model layer behind your existing API, reading from the systems you already have, writing back through paths your team already audits. Nordwind kept its policy system, its document store and its identity provider. What changed was that a claim arrived at an adjuster pre-read, with coverage checked and a recommendation attached.

The engineering that decides whether this works is unglamorous. Rate limits and retries with backoff. Streaming, so a four-second answer feels like one. Caching, so a repeated question is not a repeated bill. Structured outputs validated against a schema, so a malformed response fails loudly instead of writing nonsense into a database.

And fallbacks, so a provider outage degrades one feature instead of taking the product down with it. Every one of those is ordinary software engineering, which is why the AI practice sits on top of the software development practice rather than beside it.

Retrieval is where most custom AI solutions are won or lost. A model can only be as good as the context it is handed, and assembling the right context out of your documents, records and history is ordinary engineering rather than prompt craft.

We build the context layer explicitly. Chunking that respects the structure of a document instead of a fixed token count. Hybrid search that combines vector similarity with keyword matching, because exact identifiers matter as much as meaning. Reranking before anything reaches the model. Metadata filters so a query never sees a document the user is not allowed to read.

Permissions are enforced at retrieval, not by asking a model to be discreet. That is the difference between a system a security review passes and one it does not.

Citations come back with every answer, pointing at the source paragraph. That single feature does more for adoption than any model upgrade. At Nordwind it is the reason adjusters accepted recommendations in week one: nine citations, ten seconds, move on. When retrieval quality is the bottleneck we fix retrieval — reaching for a larger model to paper over bad context is the most expensive way to not solve a problem.

Nothing reaches production on a feeling. Before the build starts we assemble an evaluation set from your own historical records, with the known-correct outcome for each case. At Nordwind that was 1,200 closed claims. That set becomes the definition of done.

Every change replays against it in CI. We track accuracy, the shape of the errors, how often the system correctly declines to answer, latency and cost per task. Regressions block a merge exactly the way a failing unit test does.

The number to agree in week one is not accuracy in the abstract. It is the accuracy at which this beats the process you have today, with the escalation path counted in. A system that is right 88% of the time and knows when it is unsure is worth more than one that is right 94% of the time and confidently wrong about the rest.

Three rules apply to every agent we ship. First, no destructive action without a person: refunds, cancellations, payments, deletions and anything that writes to a system of record outside a reversible queue require a human click. That is not a limitation we expect to lift as models improve.

Second, a confidence threshold that is tunable and observable. Every routing decision is logged with its score and its evidence, so an operations lead can see why a case went to a human and move the threshold using data rather than instinct.

Third, an audit trail: inputs, retrieved context, model version, output and the human decision that followed, retained as long as your policy requires. When somebody asks in six months why the system did that, the answer is a record rather than a shrug.

Prompt injection is treated as a security problem rather than a prompt problem. Untrusted content is fenced, tools are scoped to the minimum permission they need, and nothing a model produces is executed without validation. The same review we apply to any other production system applies here.

When the question is whether an AI product is worth building at all, the answer costs less than the argument about it. Bastion Health went from a scoping call to a live care-planning MVP in eight weeks, with real clinicians using it.

The shape is fixed: two weeks of audit and interface design, five to eight weeks of build with a demo every fortnight, one or two weeks of hardening and handover. Scope is cut to one workflow that matters, done properly, rather than five sketched.

We insist on real users before launch rather than a stakeholder demo. An MVP only the sponsor has touched teaches you nothing, and the interface decisions that make an AI feature trustworthy only become visible when somebody who does the job every day disagrees with the output and has to do something about it.

A narrow single-workflow MVP starts at $15,000. Most land between $35,000 and $70,000, and the audit week tells you which end of that you are at before you commit.

The model is maybe a third of the work. The rest is the interface that decides whether anyone believes it. Ten years of interface craft is the reason the Nordwind workbench hit 95% adoption in its first week, and the reason nobody asked for the old five screens back.

We are not tied to a provider and neither should you be. Every build goes behind one internal interface, so the model is a configuration value. Changing it is a config change plus an evaluation run, not a rebuild, which is the whole reason the evaluation suite exists.

Model selection happens per task, not per project. A cheap fast model does classification and routing. A frontier model handles the one reasoning step that genuinely needs it. A small local model covers anything that cannot leave your network. Most production pipelines we run use two or three models together.

The cost difference against using the largest available model for every step is usually somewhere between five and ten times, for no measurable change in the outcome that matters. That is a decision worth making deliberately rather than by default.

We benchmark at least two alternatives before launch and write down the result, so that when a better model ships next quarter you run the suite, see the delta, and decide with evidence rather than a press release.

The default posture is that your data stays in your cloud account. Model calls go to zero-retention endpoints, or to a model running inside your own network where policy requires it. Nothing you give us is used to train anybody’s model.

We work inside the boundary you already have: your identity provider, your network, your logging, your retention rules. For regulated clients the security review happens during the audit week rather than as a surprise in week ten, and it covers where prompts are logged, what a support engineer can see, and how long a transcript survives.

Where you have a data processing agreement, we sign it. Where you need residency in a particular region, we build to it. Where a vendor term does not meet your policy, we say so before the architecture depends on that vendor.

The full posture, including retention defaults and how access is handled during a build, is written up on the security page.

A production AI feature has a monthly bill, and you should see it before you commit rather than in the first invoice. We instrument cost per task from week one and show it beside accuracy on one dashboard, because those two trade against each other and the trade is your decision to make, not ours.

Volume and context size drive almost all of it. A routing agent that reads one short ticket costs a fraction of a claims pipeline that reads six documents per case. Managed vector storage at the volumes these systems run is typically a few hundred dollars a month at most.