Ship the AI features your roadmap keeps promising.
Your team is busy keeping the core product running. We add a senior pod that ships AI features, agents and infrastructure work alongside them, in your repos and your process.
- Code in your repos from day one
- IP assigned to you
- Senior engineers only
- A live demo every week
- Fixed scope before we build
(01)Sound familiar?
What we usually hear first.
AI on the roadmap, every quarter
Customers and the board ask for it, and your team has no time to research, evaluate and ship it.
Demos that never reach production
Prototypes work on the happy path, then accuracy, latency and cost stall the launch.
Infrastructure slowing releases
Manual deploys, noisy alerts and a rising cloud bill eat engineering time.
Hiring takes months
Senior AI and platform engineers are hard to find and slow to onboard.
(03)Where to start
A low-risk first step.
Starting offer
AI Feature Pilot
One AI feature in production, with numbers to prove it.
We pick one high-value workflow, build it against real cases and ship it with monitoring, typically in two to three weeks.
What you get
- Use case and success metric agreed up front
- Evaluation set built from your real data
- Production release behind a feature flag
- Accuracy and cost dashboard
(04)Why teams pick us
Proof, not promises.
Case study · Kaching
A marketplace where creators pay with their influence
A two-sided platform where Instagram creators book experiences at cafés, restaurants and bars, and repay them with content instead of money.
Read the case studyWorks inside your team
Your repos, your code review, your standups. No parallel universe.
Production first
Evaluation, guardrails and monitoring are part of the build, not an afterthought.
Model-agnostic
OpenAI, Claude, Gemini or open-source, chosen on accuracy, latency and cost.
(05)Questions
Can you work in our codebase and process?
Yes. We work in your repositories, follow your review process and join your rituals, as an embedded pod or on a specific workstream.
How do you handle our customers’ data?
We can use enterprise model endpoints that don’t train on your data, keep data in your own cloud, apply role-based access to retrieved content and log every action for audit.
What if the AI feature isn’t accurate enough?
We agree an accuracy threshold before launch and measure against an evaluation set from day one, so you know early whether the feature is ready, needs work, or isn’t worth shipping.
How do engagements usually start?
With a one- to two-week discovery to pick the workflow and the metric, followed by the pilot. Many teams then keep a pod on for the next features.