The engineering studio of

Try it. Refine it.
Ship it.

We are the engineers behind itry.ai. We design, build and ship AI features inside your product, then hand over code your team can own.

Start a project See how we work

your-project.ts

handover.md

 1  import { studio } from "itry.dev";
 2  
 3  try {
 4    const feature = await studio.build(yourIdea, { stack: yourStack });
 5    ship(feature);
 6  } catch (risk) {
 7    // found in week one, not in production
 8    await studio.rescope(risk);
 9  }

$ git log --oneline
try     working prototype on your real data
refine  evals, edge cases, cost and latency
ship    deployed in your stack, handed to your team

// what we build

AI features that run in production, not in a slide deck.

itry.ai sets the strategy. itry.dev writes the code. Bring us in for one piece or the whole build.

Assistants and copilots

Chat, search and drafting features built into your product and grounded in your own content.

LLM apps · retrieval · guardrails

Agents and automation

Multi-step workflows that take real actions in your systems, with a human in the loop where it matters.

tool use · approvals · audit trail

Integrations and APIs

Models wired into the tools you already run, behind an API your other teams can call.

APIs · webhooks · your stack

Data pipelines

The unglamorous part: ingesting, cleaning and indexing your data so the model has something true to work from.

ingestion · indexing · freshness

Evals and tuning

Tests for model output, so quality is measured instead of argued about, plus cost and latency work.

eval suites · CI · cost control

Pairing and handover

We build alongside your engineers and leave documentation, tests and a team that can run it without us.

pairing · docs · your repo

// how we work

The name is the process.

Every engagement runs the same three blocks, in this order.

try {

Prototype on real data

We start with a short, fixed-scope sprint on your actual inputs. You see something working before you commit to a full build.

} refine {

Find what breaks

Wrong answers, edge cases, slow calls and runaway cost get caught by evals and load tests, and we tell you plainly if the idea does not hold up.

} ship {

Publish and hand over

It goes live in your infrastructure, in your repo, with the tests and docs your engineers need to keep it running.

// engagements

Three ways to bring us in.

01 · validate

Prototype sprint

One idea, one small team, one working prototype. The cheapest way to find out whether it is worth building.

Working demo on your data
Written go or no-go recommendation
Estimate for the full build

02 · build

Build project

We take a feature from prototype to production and stay through launch. Most clients start here after a sprint.

Production feature in your stack
Eval suite and monitoring
Documentation and handover

03 · extend

Embedded engineers

Our engineers join your team, your standups and your codebase for as long as you need the extra hands.

Senior AI engineering capacity
Pairing with your developers
Scale up or down as you go

// not sure what to build yet?

Start with the strategy at

If the question is where AI fits in your business, not how to build it, our consulting side handles roadmaps, ROI analysis and team training.

Visit

// start a project

The worst case is you try again.

Tell us what you want to build. We will reply with questions, a rough shape for the work and whether a prototype sprint makes sense.

Try it. Refine it.Ship it.