AI-Native Engineering
Most teams treat LLM calls as an afterthought. We help you build the evaluation harness, observability layer, and deployment pipeline that turn AI experiments into reliable production features — without slowing your team down.
30 minutes. No pitch deck. We'll tell you honestly whether we can help.
The model isn't the problem. The problem is the engineering around the model: how it's deployed, monitored, tested, and updated. That's what we fix.
Ad-hoc prompt strings scattered across the codebase, no evaluation harness, no observability. Every new model release breaks something. You know this is fragile — you just haven't had time to fix it properly.
Copilot subscriptions for every developer. A €200K fine-tuning experiment that's still in staging. A chatbot that hallucinates 20% of the time in prod. The gap between "using AI" and "benefiting from AI" is wider than most teams realise.
Traditional code is deterministic. AI features aren't. Your test suite, your CI/CD pipeline, your incident response playbook — none of them account for non-determinism, prompt drift, or model degradation. That's a category of risk your current process doesn't catch.
Your team is under pressure to ship AI features fast. But every shortcut compounds: more prompt sprawl, less observability, higher blast radius when something goes wrong. You need someone who's already navigated this trade-off at scale.
Free Resource
Find out if your data, team, and infrastructure are ready for AI. Free. Takes 5 minutes.
The same discipline that makes critical infrastructure reliable — applied to your AI layer.
Every AI feature ships with an evaluation harness. We define success metrics, build golden datasets, and gate releases on evals — not vibes.
Prompt versions, model responses, latency, cost per inference, and user outcomes — all observable. You can't improve what you can't measure.
We design clean contracts between your AI layer and the rest of your stack. Swap models without rewriting integrations. Upgrade prompts without touching business logic.
Feature flag rollouts, shadow scoring, A/B testing AI variants, and rollback triggers — the same engineering discipline you apply to critical infrastructure, applied to AI.
AI features shipped to production
Years building at Amazon, JPMorgan scale
Engagements reached production
We work with engineering teams who have AI in production, or are serious about getting there.
Series B SaaS, 60 engineers
AI features scattered across five product areas, each built differently. No shared infrastructure, no consistent eval strategy.
FinTech, 90 engineers, regulated environment
Need AI in the product but can't afford hallucinations. Require audit trails, explainability, and a deployment process that satisfies risk.
B2B platform, 40 engineers
First AI feature shipped under pressure. Works most of the time. Nobody is confident in the edge cases and there's no harness to find them.
Scale-up, building AI-native product from scratch
Founding team that wants to get the architecture right before hiring 20 engineers. One bad early decision compounds for years.
100% of our engagements have reached production. We guarantee yours will too.
If we don't ship a working AI feature within the agreed engagement period, we continue working — at no additional cost — until it's live.
That's not a marketing claim. It's a contractual commitment.
Investment
Engagements typically start at £25,000 for a focused 90-day embedded engagement.
Every engagement includes: audit, strategy, build, and handoff. No hidden phases.
AI-native engineering means building software systems where AI capabilities are treated with the same engineering rigour as any other critical component: observable, testable, deployable in stages, and rollback-safe. It's the difference between "we have an LLM call somewhere in the codebase" and "our AI layer is a first-class engineering concern."
We specialise specifically in the failure modes that appear when AI enters a production codebase — prompt drift, model degradation, non-deterministic outputs, eval coverage gaps, and inference cost spirals. Most generalist firms don't have practitioners who've shipped AI features at scale.
Yes. Most teams using LLM APIs have accumulated prompt sprawl, missing evals, and no observability. We often spend the first two weeks mapping what's actually in production and building the foundation that should have been there from the start.
Typical engagements run 8–16 weeks. We start with a two-week audit, then move into systematic improvements — eval harness, observability, CI/CD gates — in four-week sprints. Most teams continue with a lighter ongoing retainer after the core work is done.
We embed with 2–3 of your senior engineers. Expect 30–50% of their time on the AI engineering workstream. We work in your codebase, your CI/CD environment, and your sprint cycle — not on a separate track.
Yes. We're model-agnostic and have production experience with OpenAI, Anthropic, Google, Mistral, and open-source models via Ollama and Hugging Face. We help you architect for flexibility, not lock-in.
30-minute discovery call. We'll tell you exactly what your AI engineering needs — and whether we're the right fit.
Book a Discovery Call