AI Applications
Production LLM apps that stay predictable once real users arrive.
Production LLM apps engineered to hold up once real, messy user input replaces clean test prompts.
AI sets the pace here, but engineers set the standard — every model output that reaches a real user has passed through an evaluation step and, for anything consequential, a human review gate.
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
Design the prompts
Structured prompting with tested fallbacks for edge cases.
Load test
Real-world input variety run through before launch, not just happy paths.
Monitor in prod
Ongoing observability so quality regressions get caught fast.
Iterate on real queries
Actual user prompts, not just the test set, shape what gets tuned next.
Frequently asked
Structured prompting, retrieval grounding where relevant, and an evaluation suite that runs before every change ships — plus a human review gate on anything user-facing.
Usually yes. Most AI work is integrated into an existing codebase rather than built as a separate app, wired in behind a feature flag so it can roll out gradually and get rolled back instantly if something's off.
We design fallbacks and guardrails up front — the goal is that a bad model response degrades gracefully (a clear 'I'm not sure', a human handoff) rather than silently misleading a user.
We're not tied to one vendor — OpenAI, Anthropic's Claude and open-weight models are all in regular use, chosen per task based on cost, latency and quality trade-offs rather than brand preference.
A concrete evaluation suite specific to the task — pass rate, hallucination rate, latency — run before every prompt or model change ships, not just a subjective 'looks good' check.
No. Your data is used to ground retrieval and evaluate outputs for your own product; it isn't sent off for third-party model training.
A focused AI feature can often reach a working staging version in 1–3 weeks; agentic or multi-tool workflows typically take longer because the evaluation loop takes more iteration to get trustworthy.
We help you estimate and monitor token/API cost as part of the build, and design prompts and retrieval to keep it proportionate to the value the feature delivers.
Who this is for
- Teams with a clear AI use case, not just a hunch that they 'should have AI somewhere'
- Products with an existing user base to layer AI into, where the workflow already exists
- Founders who want AI without the hype — a feature that measurably saves time, not a demo gimmick
- Teams that tried building this in-house and hit reliability or evaluation problems
- Companies that need guardrails and audit trails around AI output, not just a raw model call
What you walk away with
- A written spec of exactly what the model is — and isn't — trusted to decide
- An evaluation suite that runs before every prompt/model change ships
- Guardrails and fallback behaviour for low-confidence responses
- Observability into real usage once it's live
- A rollback plan if quality regresses after a change
Who works on this
AI & Full-Stack Developer · QA · Testing
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