Custom apps and the AI behind them.
Web, iOS, Android, and the agents, retrieval, and evals that make them feel like the future. Same senior engineers who scaled Casper, ran the Obama donation platform during debate nights, and supported 60+ Hillary campaign staff with a 3-engineer infrastructure team.
Four kinds of work.
Greenfield products.
Idea to production. Stack chosen for AI-native development from day one.
Rebuilds.
Replatforming when the legacy stack is the bottleneck. The team has shipped this kind of work as Lerna monorepos at Hillary, FlexPages micro-frontends at Flexport, and an Rspack migration that hit ~50% of HubSpot's app fleet.
Production AI.
Agents, RAG, document understanding, knowledge graphs. Evals you can read, traces you can search, a cost line you can defend.
Performance and reliability.
The kind of work you do when an engineer's reputation is on the line. Build time improvements, infra hardening, debugging the failure mode that scales.
This is what changes the math.
Parallel coding agents, supervised.
We run up to 8 Cursor agents at once, scoped by a senior engineer. The engineer reviews every PR. The agent does the typing. Our peak was 46 PRs merged in a single week. Acceptance rate on agent output sits around 79.6%. Inference cost runs about $9,500 per active week. We publish that number on purpose.
Evals and observability are not optional.
Every agent system we ship has LangSmith traces, Sentry coverage, and a test suite that runs against frozen golden examples. If we cannot measure it, we will not call it shipped.
The senior engineer is the contract.
The person on the call with you is the same person reviewing the code. There is no account manager between us. There never has been.
Three engagements, on the record where we have permission.
Home management platform (anonymized)
We embedded a six-person team running parallel coding agents and shipped 89% faster against the team's pre-agent baseline, while building the multi-agent backbone (RAG, knowledge graphs, document understanding) the product runs on today.
Loan servicing client (anonymized)
Three-phase build: a document understanding pipeline that extracts and validates loan data, a natural-language retrieval assistant for internal staff, and the recurring reporting and email workflows that used to live in someone's calendar.
Consumer brand (anonymized)
Technical design and ground-up build of a data-intensive web app on Next.js, Bun, Postgres, and AWS. Architecture chosen for one reason: 90% of the work would be done by coding agents under senior review.
Sized to a real problem, not to a sales target.
Engagements run two to six months. Most start with a senior lead and scale to two to four engineers. Optional retainer at the end if you want to keep the team.
We are honest about fit.
Probably yes.
- Pre-seed through Series B, occasionally later
- Technical founder, VP Eng, or CPO on the call
- Specific problem with a specific constraint (timeline, headcount, technical debt)
- Already shipped software at least once
Probably not.
- Enterprise procurement
- "We want to explore AI" without a defined problem
- No existing engineering function
- Looking for a 50-person consultancy
Bring us a problem
and a constraint.
We will tell you whether we are the right fit. Same business day.