Booking new projects from Jan 2027
AI-native engineering studio · Est. 2019
US-based · Remote-first
Since 2019 we've shipped 90+ products for US companies the hard way. Now every one of our senior engineers runs a fleet of coding agents: same judgment, three times the output.
(01) Story
Then2019 – 2024
Lending platforms, clinic software, logistics backends. Systems that still run years later, for clients who still call us. That's where our judgment comes from, and judgment is the one part you can't hand to an agent.
Now2025 →
In 2025 we rebuilt how we work around coding agents. Agents draft code, tests and docs around the clock; engineers do the part that takes fifteen years to learn: deciding what to build, and what not to. It isn't a new studio. It's the old one, at a speed that shouldn't be possible.
| Year | PRs per engineer per week |
|---|---|
| 2019 | 5.6 |
| 2020 | 5.9 |
| 2021 | 6.2 |
| 2022 | 6.4 |
| 2023 | 7.3 |
| 2024 | 8.4 |
| 2025 | 16.9 |
| 2026 | 25.1 |
(02) Services
Agents that take real actions in your systems: support, operations, sales, back office. With guardrails, audit logs and a human in the loop where it matters.
Tool use · MCP · Workflows · HITL
Search and Q&A over your documents, tickets and databases that cites its sources and knows when it doesn't know.
RAG · Hybrid search · Reranking
Evaluation suites, regression gates, tracing, cost and latency budgets. The difference between a demo and a product.
Evals · Tracing · Guardrails · Cost
AI features inside an existing web or mobile app, without a rewrite: extraction, summaries, smart search, voice.
LLM APIs · Voice · Document AI
Web platforms, SaaS and internal tools. Architecture, frontend, backend and the unglamorous parts in between.
TypeScript · React · Python · Go
Native iOS and Android, or cross-platform when the budget says so. On-device models when data shouldn't leave the phone.
Swift · Kotlin · React Native · Core ML
Infrastructure as code, data pipelines, GPU inference and a cloud bill you can explain to your CFO.
AWS · GCP · Kubernetes · vLLM
Where AI will actually pay off, what data it needs and what it costs to run. Or a rescue for the pilot that never left the demo.
Workshops · Data review · ROI model
(03) How we build
01Human
A senior engineer turns the goal into a written spec, interfaces and acceptance tests.
02Agents
Coding agents draft the implementation, tests and docs in parallel branches. Hours, not sprints.
03Human + agent
A senior reads every line. A review agent checks security, edge cases and style before they do.
04CI
Unit, integration and eval suites run on every merge. Scores are allowed to go one way: up.
05Human
A named engineer approves the release and stays on call for it. Accountability doesn't get automated.
(04) Selected work
C—01HealthtechTexas2025–now
Sessions are recorded with patient consent. The system drafts SOAP notes into the EHR and flags missing billing codes; therapists review and sign. HIPAA-compliant, runs in the client's AWS account.
−48 min
Documentation per therapist, per day
Speech-to-text · LLM · FHIR · AWS
C—02FintechNew York2025
Reads formation documents and bank statements, checks state registries and sanctions lists, drafts the analyst memo. Analysts still make every decision. Live in ten weeks.
−55%
Analyst time per application
Python · LLM · Postgres · AWS
C—03E-commerceCalifornia2025
Connected to Shopify and the help desk: order status, returns, exchanges. Every release gated by a 400-case eval set built from real tickets; anything unclear goes to a person.
41%
Tickets resolved without a human
TypeScript · MCP · Shopify · Gorgias
C—04InsuranceOhio2026
First-notice-of-loss emails, photos and PDFs become structured claims in the core system, routed to the right adjuster. Adjusters stopped retyping and started reviewing.
−31%
Handling time per claim
Python · Vision LLM · Guidewire · Azure
References available on request — ask us
(05) Process
Step 01 · W1
Workshops, a look at your data, risks. You get a written scope, an eval plan and an estimate you can budget around.
Step 02 · W2–3
Flows, UI and architecture, plus a working prototype on your real data. Not a clickable mock.
Step 03 · W3–10
Agents draft, seniors decide. Staging from day one, a demo every Thursday, every merge signed off by a human.
Step 04 · W10–11
Eval gates, load tests, a security review and a staged rollout. Launch day should be uneventful.
Step 05 · W12→
Monitoring for quality, cost and drift. Support with an SLA, or a clean handover to your team.
(06) Engagement
Model 01
Prove it on your data before you commit. Four weeks, fixed price, a clear go/no-go at the end.
from$35k/ 4 weeks
Model 02Most common
A tech lead and two senior engineers, each running their own agents, working only on your product. QA and design as needed.
from$48k/ month
Model 03
You know what you need. We agree on scope, price and date after discovery, then ship it.
from$90k/ project
(07) Stack
(08) Studio
How we work
/01
Everyone on your project has shipped and supported production systems. Agents multiply experience; they don't replace it.
/02
Every AI feature ships with a test set built from real cases and a score it has to beat. If we can't measure it, we don't launch it.
/03
Code, prompts, eval sets, fine-tuned weights and cloud accounts live in your organization from day one. No lock-in.
/04
If scope, risk or a model's behavior changes, you hear about it the same day, together with options, not excuses.
(09) Clients
“We'd burned two quarters on an AI pilot that never left the demo. They had it in production in ten weeks, with an eval dashboard our compliance team actually trusts.”
“Our therapists stopped taking notes home. Nobody had to be convinced to use it.”
“They were the first vendor that told us what not to automate. Adjusters still make every call; they just don't retype emails anymore.”
(10) Careers
Fully remote across the US, flexible hours, an uncapped model budget and no more than one project at a time. Send a CV and a link to something you've built.
(11) FAQ
Whatever wins on your data. Frontier APIs from Anthropic, OpenAI and Google for reasoning-heavy work; open-weight models like Llama, Qwen or Mistral when data has to stay in your cloud or costs need to drop. We benchmark candidates on your eval set before picking one, and keep it swappable.
No. We use enterprise API terms with zero data retention, or self-hosted models inside your cloud account. Your data, prompts and eval sets stay yours, and we're happy to sign a DPA or BAA.
Evals first. Every AI feature gets a test set built from real cases, automated scoring and regression gates in CI. On top of that: guardrails, fallbacks, tracing in production and a human in the loop wherever a mistake is expensive.
Not when seniors are in charge. Agents draft code, tests and docs; a senior engineer designs the system, reviews every line and owns every merge. In practice you get more tests and better documentation than a traditional team has time for.
AI pilots run $35–60k. Most production builds land between $90k and $350k, depending on integrations and compliance. Dedicated squads start at $48k a month, and senior engineers on hourly engagements are $175 an hour. You get a written estimate after the first call.
An AI pilot can start within a week of signing. Dedicated teams usually take one to two weeks to assemble.
In the US, all of it. We're a US company with engineers from Eastern to Pacific time, so your team works your business hours: no overnight handoffs, no 6 a.m. calls.
You do. IP transfers in full with each paid invoice: code, prompts, eval sets and any fine-tuned weights. Repositories, cloud and model-provider accounts live in your organization from the start.
Yes, it's a big part of what we do. We start with a one-to-two-week audit of the code, data and evals, and give you a written report with risks, quick wins and a plan, whether or not you continue with us.
Monthly, in USD, under a US contract. Pilots and fixed-scope projects are billed by milestones; teams by the month, with time reports attached. Model and cloud usage is billed at cost, or straight to your own accounts.
(12) Contact
A tech lead, not a salesperson, replies within one business day. The first call is free and comes with an honest opinion on whether AI is the right tool at all.
Write to us