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Services

AI services built for production,not for the demo.

Six service lines, each a standing practice with an engineer who owns it end to end. If what you need isn't here, we'll say so and point you elsewhere.

Turn repetitive operational work into monitored pipelines.

We map the processes your team runs by hand, then replace the rule-shaped and model-shaped steps with automated pipelines that keep an audit trail, and leave a human in the loop wherever the cost of being wrong is high.

  • Document intake and extraction
  • Workflow orchestration
  • Human-in-the-loop approvals
  • Monitoring and audit trail
  • Python
  • Temporal
  • PostgreSQL
  • Redis
Full details

Ship one product into every market without a release train per locale.

We wire string extraction, translation and reintegration into the same pipeline that builds your product, so a new locale becomes a build condition rather than a separate project with its own timeline.

  • Continuous string extraction
  • Locale-aware layout QA
  • Translation memory as code
  • Release gating by locale
  • TypeScript
  • Python
  • ICU MessageFormat
  • GitHub Actions
Full details

Machine translation you can put in front of customers.

We pair engine output with an LLM post-edit pass against your terminology and tone, then score every segment so reviewers open only what actually needs judgment, and their corrections improve the routing.

  • Per-pair engine benchmarking
  • Terminology and tone enforcement
  • Automated quality estimation
  • Reviewer feedback loop
  • Python
  • LLM APIs
  • COMET / QE models
  • FastAPI
Full details

A test suite your team actually trusts.

We derive coverage from the paths users really take, treat flake as a defect rather than weather, and deliver failures with a reproduction attached so triage becomes a decision instead of an investigation.

  • Path-derived end-to-end coverage
  • Flake detection and quarantine
  • Visual regression
  • API and contract testing
  • Playwright
  • TypeScript
  • pytest
  • GitHub Actions
Full details

Model-backed features built like the rest of your product.

Retrieval with a freshness contract, prompts under version control, evals that block a bad merge, and cost and latency instrumented from the first deploy. These are the parts that decide whether a feature survives its first quarter.

  • Retrieval and context pipelines
  • Prompts and evals in CI
  • Guardrails and fallbacks
  • Agents and tool use
  • TypeScript
  • Next.js
  • Python
  • LLM APIs
Full details

Internal tools for the workflow your team actually has.

Review queues, annotation surfaces, operations consoles and model-assisted internal search, scoped to one workflow, built to be boring, and handed over with the source so it never becomes a dependency on us.

  • Review and annotation surfaces
  • Operations consoles
  • Model-assisted internal search
  • Role-based access
  • Next.js
  • TypeScript
  • Python
  • PostgreSQL
Full details
Engagementmodels

Three ways towork with us.

Pick the shape that matches your situation. All three end with source and documentation in your hands.

Project build

Fixed scope, fixed price

We agree the scope and definition of done up front, then deliver against it. Best when you know what you need and want a predictable number.

Best for

A defined outcome with a deadline

  • Written scope and acceptance criteria
  • Fortnightly demos
  • Full source handover
  • 30-day post-launch support
Discuss this model

Dedicated retainer

Monthly, capped hours

Most common

A set number of senior engineering hours each month, prioritized by you. Best once something is live and needs to keep evolving.

Best for

Ongoing work with shifting priorities

  • Named engineers, consistent team
  • Priorities you control monthly
  • Monitoring and incident response
  • Rolling 30-day terms
Discuss this model

Embedded team

Per engineer, monthly

Our engineers work inside your process: your repo, your standups, your review. Best when you have capacity gaps rather than a discrete project.

Best for

Extending an in-house team

  • Works in your tooling and rituals
  • Direct access to your team
  • Knowledge transfer as you go
  • Scale up or down quarterly
Discuss this model
How wework

From first call to productionin weeks, not quarters.

Four stages, in this order, on every engagement. The order matters more than the speed. Most of what goes wrong in this work goes wrong because someone built before they watched.

  1. 01Week 1

    Discovery

    We map the process as it actually runs, agree what success looks like in writing, and identify the constraint worth removing first.

    hands over
    • Process map
    • Written definition of done
    • Fixed scope and quote
  2. 02Week 1–2

    Architecture

    We design the system around your stack, data residency rules and release cadence, then review the plan with your engineers before writing anything.

    hands over
    • Technical design
    • Integration plan
    • Risk and cost model
  3. 03Week 2–8+

    Build

    Two-week cycles with something demonstrable at the end of each. The narrowest useful version reaches production early and is instrumented from the first deploy.

    hands over
    • Working software in your repo
    • Tests and CI
    • Fortnightly demos
  4. 04Ongoing

    Deploy & hand over

    Production rollout with monitoring, a runbook, and a support window while your team takes ownership. An optional retainer follows only if you want one.

    hands over
    • Deployment and monitoring
    • Runbook and docs
    • Handover session
Starthere

Tell us the process,not the solution.

The most useful first message describes what someone on your team does by hand today and how often. That is enough for us to tell you whether it is worth building.

What happens next
  • A named engineer reads it, not a form inbox
  • Reply within 24 hours, even if we're not the right fit
  • A 30-minute call to trace the process end to end
  • A fixed-scope quote, or an honest no