An invite-only network. No application form.
Nobody applies here. Every engineer arrives on someone’s name, and that only gets them a seat. Then the gates: scored on the technology they claim, a real problem under a deadline, a final interview, and training on our standard. Most never get through.
You decide what gets built. They make sure it holds.
Before
Agreed with you first
You set the priorities. How it gets built is agreed with you before code is generated, and written down so every agent follows the same rules.
During
Nothing ships unread
One engineer runs several agents and reads everything they hand back, in short cycles rather than one review at the end. Code that only looks right does not ship.
After
They stay on the hook
Whoever shipped it owns it in production and is reachable when it goes down. Nobody hands over a pull request and disappears.
All three, or none
Agreed up front, read before it ships, owned after. Without all three, the same tools just produce more mess, faster.
Four gates, then training
Vouched for
They arrive through someone we already trust. No cold applications enter the network.
Technical assessment
Scored against the specific technology they will be matched into.
Real work, submitted
A working problem solved and documented within a set window, not a take-home quiz.
Final interview
Technical and personal. The notes go to you with the profile rather than staying with us.
Then the training centre
Clearing the gates gets someone into training, not onto a client. Everyone is trained in-house before placement, by our own engineering leadership, on how we expect agents to be directed and what has to be true before code ships. That is a training centre rather than an onboarding call, and it is the reason two engineers with identical tools produce very different work.
What we can take on today
Product engineering
TypeScript, React, Next.js, Vue, Svelte, React Native, Swift, Kotlin, Node, Go, Python, Django, Rails, Laravel, .NET, GraphQL
AI and data
LLM applications and agents, RAG and vector search, fine-tuning, evals, PyTorch, MLOps, dbt, Snowflake, BigQuery, Databricks, Airflow, Kafka
Platform and DevOps
AWS, GCP, Azure, Kubernetes, Docker, Terraform, CI/CD, Postgres, Redis, observability, incident response, cloud cost tuning
Quality and hardening
Automated testing, load and performance work, auth and payments, security review, legacy migrations
This is not the whole list. If your stack is not named here, ask: assessment is per technology and we cover far more than fits on a page. Specialist ML sits at the top of the band, up to $6,000 a month, and everything else starts at $4,000 all-in.
Tell us the stack and we’ll name three engineers
Assessment scores and our interview notes on each, inside 48 hours. If we are thin on your stack, we will say so rather than send someone close enough.