AI application development

The AI feature your roadmap keeps postponing.

Copilots, assistants, and retrieval features built as real software, with evals, guardrails, and cost controls shipped alongside them.

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An AI application is a product feature, not a demo. The demo takes a weekend. The hard part is what happens after: wrong answers, runaway token spend, a prompt that regressed on Tuesday, and nobody able to explain why. We build the feature and the machinery that keeps it honest, because a copilot without evals is a support ticket waiting to be filed.

What we ship

Everything under one roof.

  • Copilots and assistants

    In-product assistants that read your data and take real actions, not chat widgets.

  • RAG and semantic search

    Chunking, hybrid retrieval, and re-ranking tuned against a judgment set, not vibes.

  • Document intelligence

    Extraction and classification over contracts, invoices, and forms, with confidence scores.

  • Evals and guardrails

    A labeled eval set, a regression gate on every deploy, and refusal behavior you chose.

  • Cost and latency control

    Model routing, caching, and budgets, so one feature cannot quietly triple your bill.

  • Tracing and observability

    Open the exact trace behind a bad answer instead of guessing from a screenshot.

How we build

Evals before launch, not after the complaint.

Every AI feature we ship carries the tooling that keeps it working. That tooling is the deliverable as much as the feature is.

  • Eval harness

    Scores each build against a labeled set drawn from your real queries.

    Trigger
    Runs on every pull request.
    Output
    A pass or fail with the regressed cases named.

  • Retrieval tuner

    Sweeps chunking, embedding, and re-ranking settings against the judgment set.

    Trigger
    Runs when the corpus or the question mix changes.
    Output
    The configuration that scored best, with the numbers behind it.

  • Red-team agent

    Probes for prompt injection, data leakage, and off-policy answers.

    Trigger
    Runs before launch and monthly after.
    Output
    A ranked findings list, exploit first.

  • Spend watcher

    Tracks tokens per feature, per tenant, and per user against budget.

    Trigger
    Runs continuously.
    Output
    An alert with the trace that caused the spike, before the invoice.

If a user reports a bad answer, we can open the exact trace that produced it. That is the bar.

How we work

How the work actually runs.

  1. 01

    Pick one

    One feature, one user, one measurable job. We say no to the other four for now.

  2. 02

    Build the eval

    A labeled set of real queries and right answers, agreed before any code ships.

  3. 03

    Ship behind a flag

    Live for a small cohort. We watch traces, cost, and refusals against the eval.

  4. 04

    Widen or kill

    The eval decides. Features that do not clear the bar get cut, not shipped quietly.

Who we serve

Categories we already know.

  • B2B SaaS
  • Legal and professional services
  • Fintech
  • Healthcare
  • Logistics
  • Marketplaces

What clients say

4.6average across 4 verified reviews

Read them on Clutch
  • I was particularly impressed by their creative approach and attention to detail.

    Videography & photography company · website, SEO + design

  • They delivered the project on time.

    Watch retailer · Shopify store build

  • Cubitrek always had a positive mindset and was kind.

    Personal training company · video + social media

Questions buyers ask us.

  • An agent runs a workflow on its own and decides its next step. An AI application is a feature inside software a person is already using. Same engineering discipline, different shape. Many clients end up with both.

  • That is the usual case. We work inside your repo, your stack, and your release process. We do not require a rewrite, and we will say so early if your data layer makes retrieval genuinely hard.

  • Whichever clears your eval set at acceptable cost and latency. We build model routing in from the start so switching is a config change, not a rebuild. Vendor lock-in is a choice, not a requirement.

  • We scope that in week one. Options run from a provider with a no-training agreement, through your own cloud tenancy, to open models you host. Each has a real cost and we will show you it.

  • Retrieval grounded in your sources, refusal behavior you define, confidence thresholds, and citations back to the source document. Then an eval set that fails the build when accuracy drops.

  • Usually six to ten weeks for one feature, live behind a flag, with the eval harness and tracing handed over. After that you either widen it yourselves or we keep going.

  • You do, all of it. We push to your repositories and your cloud accounts. The eval set is yours too, and it is the most valuable thing we hand over.

The rest of the line.

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Ready to start AI applications?

A 15-minute call. We map the goal, look at what exists, and come back with a scoped plan.

Send us the goal instead