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AI Development Built to Survive Production

AI development covers the full lifecycle of an intelligent system: identifying where a model genuinely beats a rule, designing the data flow around it, building the application layer, evaluating accuracy before launch, and operating the result once real users depend on it every day.

  • Free 30-minute scoping call
  • Costed scope before you commit
  • You own the code and the IP
Scope Your AI ProjectAll Services

How it runs · 6 steps

  1. Feasibility and use-case selection
  2. Architecture and model selection
  3. Evaluation set before build
  4. Build in reviewable increments
  5. Harden and launch
  6. Operate and re-evaluate

Built with

  • Claude
  • GPT
  • Llama
  • Mistral
  • Python
  • TypeScript
  • FastAPI
  • Next.js

More in AI Services

Questions first?Talk to an engineer

Most AI projects die between the demo and the Monday morning

The gap is rarely the model. It is retrieval quality, edge-case handling, cost per request, latency, and what happens when a provider returns an error at 2am. We build for that half of the problem first, because it is the half that decides whether a project survives its first quarter in production.

That also means we start by ruling things out. Some problems are better solved with a database query, a validation rule, or a redesigned form — and we will say so before you spend a budget on a model.

The Problem

What this usually fixes

  • A prototype that nobody trusts

    It demos well and no one uses it, because there is no way to see why it answered what it did, no way to correct it, and no record of what it changed.

  • Accuracy nobody can quantify

    "It seems to work well" is the only measure available, so there is no way to tell whether last week's prompt change improved things or quietly broke twenty other cases.

  • Costs that arrive as a surprise

    Token spend scales with usage in ways a pilot never reveals. Without per-request measurement and spend caps, success is what breaks the budget.

  • A system that decays on its own

    Providers retire model versions on their own schedule. Output quality shifts without a line of your code changing, and nothing catches it until a customer does.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Feasibility and use-case selection

    We rank candidate problems by value and by how well a model actually fits them, and we name the ones that do not need AI at all.

  2. Architecture and model selection

    Frontier API, open-weight on your infrastructure, or hybrid — chosen on accuracy, latency, cost per request, and data residency rather than on what is fashionable.

  3. Evaluation set before build

    A written eval set with pass thresholds agreed before implementation starts, so "done" is a number you approved rather than an opinion.

  4. Build in reviewable increments

    Working software on a real environment every one to two weeks, so direction is corrected while correcting it is still cheap.

  5. Harden and launch

    Retries, cross-provider fallbacks, spend caps, structured logging on every model call, and alerting on quality drift — live before go-live.

  6. Operate and re-evaluate

    Your eval set is re-run against new model versions before migrating, so provider changes are caught by a test rather than by a customer.

What You Get

Why teams choose this

  • Accuracy is a number, not a claim

    Every engagement defines what "good enough to launch" means before the build, and measures against it after.

  • Cost per request is known

    Spend is instrumented from the first call and capped per model, so scale is a plan rather than an invoice shock.

  • Failures are traceable

    Structured logs on every generation — inputs, sources, cost, latency — so a regression is attributed to a change instead of debated.

  • You can hand it to another team

    Architecture notes, runbooks, and a readable codebase are part of delivery, not a later phase.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Models
  • Claude
  • GPT
  • Llama
  • Mistral
Engineering
  • Python
  • TypeScript
  • FastAPI
  • Next.js
Data & retrieval
  • PostgreSQL
  • pgvector
  • Redis
Operations
  • OpenTelemetry
  • Sentry
  • Docker
Industries

Who this is for

AI development pays back fastest where document volume is high, decisions repeat, or customer contact is constant.

  • Healthcare
  • Financial services
  • Insurance
  • E-commerce & retail
  • Logistics
  • Legal & professional services
  • Education
  • Manufacturing
Questions

AI Development: common questions

Related reading

What does AI development actually involve?

AI development involves five distinct pieces of work: choosing which business problem a model genuinely improves, selecting and integrating that model, building the application and data layer around it, measuring accuracy against an agreed threshold before launch, and operating the system afterwards as models and costs change. The engineering effort is usually weighted toward the last three. Integrating a model is a small part of the work; making its output trustworthy, affordable, and correctable is most of it.

How long does an AI development project take?

A focused AI feature inside an existing product typically takes four to eight weeks; a new AI product with its own application layer usually takes three to six months. The variables that move the estimate most are data readiness, the number of systems the AI must integrate with, and the accuracy bar the use case demands. A two-to-three week paid discovery produces a costed plan before the larger commitment, which is the point at which a timeline becomes worth quoting.

Do you work with our existing engineering team?

Yes. Roughly half our AI work is done inside a client codebase alongside their own engineers, and the other half is delivered as a standalone system. When working with an in-house team we set up the evaluation harness, model layer, and observability so your engineers can extend it without us. Knowledge transfer is scheduled as part of the engagement rather than compressed into a handover meeting at the end.

What if AI turns out to be the wrong tool for our problem?

We tell you during scoping, before you commit a budget. A validation rule, a database query, or a redesigned form beats a model more often than the market admits, and those solutions are cheaper to run and easier to trust. Discovery is priced as its own engagement and the output is yours either way, so the recommendation costs us the build rather than costing you the budget.
Sectors

Where we apply this

The same work looks different depending on the constraints it has to hold. These cover what changes.

Related

Services that pair with this

Tell us what you are trying to automate

A free 30-minute call with an engineer. We will tell you whether a model is the right tool, and roughly what it takes if it is.

Scope Your AI ProjectSee Our Work