AI Engineering
LLM-powered assistants, chatbots and intelligent automation, wrapped in the guardrails and evaluation tooling that keep them predictable once real users show up.
LLM-powered assistants, chatbots and intelligent automation, wrapped in the guardrails and evaluation tooling that keep them predictable once real users show up.
We treat this the same way we treat every capability: scoped honestly up front, built by the senior engineer who owns it end to end, and shipped with the tests and monitoring that keep it reliable after launch — not just on demo day.
We're deliberately not the studio that quotes you a number and disappears until the deadline. Expect short, regular check-ins, staging access from early on, and a straight answer any time something turns out to be harder — or easier — than it looked at the start. If a piece of this isn't the right fit for your team, we'll say so directly rather than stretching the engagement to fill a quarter.
How we build it
Define the task
Scope exactly what the model should — and shouldn't — be trusted to decide.
Ground it in your data
Retrieval over your own documents, not just the model's memory.
Evaluate before ship
Automated eval suites catch regressions before a prompt or model change reaches users.
Monitor real usage
Ongoing observability into live queries, so drift and edge cases surface fast instead of quietly accumulating.
Frequently asked
No — most projects combine this with one or two other capabilities. We scope the exact mix on a short discovery call before any contract is signed, so you're never paying for work you don't need.
AI scaffolds boilerplate, drafts edge-case tests and speeds up repetitive parts of the build. Every AI-touched line is still reviewed and merged by a senior engineer before it ships — AI sets the pace, the engineer sets the standard.
Fixed-price for well-scoped builds, hourly for ongoing or open-ended work. You'll get a number before we start, not after, and any scope change is flagged with its cost impact before we act on it.
A named senior engineer owns this capability end to end for your project — not a rotating pool of contractors. You'll know exactly who's building your product and can talk to them directly.
Both. A large share of our work is extending or rescuing an existing codebase — we start with a short technical audit so estimates reflect the code as it actually is, not as it's assumed to be.
It depends on the project, but we lean toward proven, well-supported tooling over the newest framework of the month — the goal is something your team (or ours) can maintain confidently for years, not just demo well on day one.
Unit and integration tests are written alongside the feature, not bolted on afterward, and AI-generated edge-case tests are reviewed by an engineer before they're trusted in CI.
Working, deployed software in your own repository and infrastructure — plus documentation, environment access and a short handover call so your team (or ours, if we continue supporting it) can pick it up confidently.
Who this is for
- Founders shipping a first product and want it built right the first time
- Teams replacing a fragile legacy system that's started costing more to maintain than rebuild
- Companies scaling past their MVP and hitting the limits of what got them here
- Product leads who need a senior owner for this specific piece, not a generalist spread thin
- Teams that tried an agency or freelancer before and got burned by scope creep or ghosting
What you walk away with
- A scoped, written plan before any code is written
- Working software deployed to your own domain/infra
- Tests covering the core flows, not just the happy path
- Documentation and a recorded handover walkthrough
- 2 weeks of post-launch support included as standard
Who works on this
AI & Full-Stack Developer · QA · Testing
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