Backend engineering · AI integration

We build the systems that make AI work in production.

Backend development with AI built in, custom harnesses that put models to work on your data and tools, and the applications your team and customers actually use.

Start a project See what we build

harness/support_agent.py
# your tools, your data, your rules
harness = Harness(
    model="[your model]",
    tools=[crm.lookup, tickets.create, billing.refund],
    guardrails=[pii_redact, refund_limit(500)],
    memory=VectorStore("kb_prod"),
    evals=EvalSuite("support_v3"),
    observe=Tracing("otel"),
)

result = harness.run(ticket)
# logged · evaluated · auditable

APIs & servicesLLM integrationRAG & retrievalAgent harnessesEvals & observabilityWeb & mobile apps

01 / Services

Three practices. One engineering team.

Backend development with AI integration

APIs, services and data pipelines designed with model calls as a first-class part of the system, not a bolt-on.

· LLM features inside existing products
· Retrieval over your documents and databases
· Queues, caching and cost controls for inference
· Migration of prototypes to production services

Custom AI harnesses

The orchestration layer around a model: tools it can call, context it sees, limits it respects, and the evals that prove it works.

· Agent loops wired to your internal tools
· Guardrails, permissions and human review
· Evaluation suites and regression testing
· Tracing, logging and cost visibility

Application development

Web and mobile applications built end to end, from data model to interface, with AI features where they earn their place.

· Internal tools and operator dashboards
· Customer-facing web and mobile apps
· Auth, billing and integrations
· Ongoing maintenance and support

02 / What is a harness

The model is one part. The harness is the product.

A raw model call answers questions. A harness lets it take actions inside your business safely: it decides what the model sees, which systems it can touch, and how every run is checked and recorded.

CONTEXT

What it knows

Retrieval, memory and prompts tuned to your domain.

TOOLS

What it can do

Typed connections to your APIs, databases and SaaS.

GUARDRAILS

What it may not do

Permissions, limits and approval steps for risky actions.

EVALS

How you know it works

Test suites run on every change, before users see it.

OBSERVABILITY

What happened

Traces, costs and outcomes for every run.

03 / Process

From first call to production.

01

Discovery

We map your systems, data and the workflow worth automating, then define what success looks like.
02

Prototype

A working slice against real data, with an eval set so results are measured from day one.
03

Build

Production backend, harness and interface, with tests, security review and documentation.
04

Operate

Monitoring, model upgrades and iteration, or a clean handoff to your team.

04 / Engagements

Ways to work with us.

AI readiness sprint

[DURATION]

Architecture review, use-case shortlist and a scoped build plan with cost estimates.

Fixed-scope build

[DURATION]

A defined harness, service or application delivered to production with tests and docs.

Embedded team

Monthly

Senior engineers working inside your roadmap, standups and repos on an ongoing basis.

05 / Waitlist

We take on a limited number of projects at a time.

Join the waitlist and we will reach out as soon as the next project slot opens. Waitlist members are contacted in order.

NEXT OPENING

[MONTH YEAR]

SLOTS PER QUARTER

[N]

06 / Contact

Tell us what you want AI to do in your business.

Send us a few details about your project. We review every request and get back to you as our schedule allows.

[hello@company.com] [City, Time zone]