# AI Adoption, Delivered by Embedded Engineers | Super Cat > Super Cat moves AI from pilot to daily team use: embedded engineers ship the first system, train your team and hand over the runbook. UK and Hong Kong. Source: https://www.super-cat.tech/ai-adoption Last modified: 2026-10-24 AI Adoption · UK & Hong Kong # AI adoption, delivered by embedded engineers Written by [Cat Yung](https://www.super-cat.tech/cat-yung), Founder, Super Cat Technology. Last updated 24 October 2026. Super Cat moves AI from a pilot into daily team use: we embed engineers, ship the first production system, train the team and hand over the runbook. The measure is adoption, not a demo. Expert-Vetted top 1% · 100% Job Success Score (source: Upwork profile, October 2026) · UK clients in mortgage broking and health economics · production AI at a 5,000-employee US enterprise and a Big 4 firm Book a call See the proof sprint-1.board week 2 In review - #21 Monitoring + handover runbook PR #34 · 1 approval - #24 Retry + timeout on tool calls PR #36 · draft - #18 Deploy to staging behind SSO PR #31 · checks passed Shipped - #12 Retrieval eval set + baseline PR #19 · merged - #15 Citation check in answers PR #27 · merged - #18 Deploy to staging behind SSO PR #31 · merged PR #27 citation check in answers - return answer + if not answer.citations: return refuse("no source") burndown · story points 37 commits 3 PRs merged Sprint 1 · deliverable shipped to your repo Illustration ## What does AI adoption mean here? Adoption means three things at once: a system in daily use, a team that can run and change it, and a named owner inside the business. A pilot that never leaves the demo environment is not adoption, however good the model. We build the first production system with your team, then train that team to operate it. The handover is the product: code, an evaluation suite, a runbook and an owner who can say what changed. ## Why do pilots stall? ### No owner A team experiments, but nobody inside the business is accountable for the system going live. ### No evaluation The pilot is judged on a curated demo, not on measured performance against real work. ### No handover The vendor leaves with the knowledge, so the team cannot change the system after launch. ## Which adoption model fits your team? ### Embedded engineers Your team builds the system with our engineers, in your repositories and your stand-ups. The capability stays in-house after we leave. Best when your team wants to own the system. ### Forward-deployed engineers Our engineer ships into your operations, works beside the people who will use the system, and hands over the runbook. Best when the system has to run in the business, not in a demo. ### Fractional CAIO A part-time AI executive decides what to adopt, buy or skip, sets the roadmap and reports to the board. Best when leadership needs an AI owner without a full-time hire. ## How does an engagement run? - 01 ### Map the workflow We map the work, the data and the constraints, and pick the first use case by value, effort and risk. - 02 ### Ship the first system A production system in your environment, not a demo, with an evaluation suite from week one. - 03 ### Train the team Your people learn to run, monitor and change the system. The runbook is written with them, not for them. - 04 ### Hand over the runbook Code, evaluation suite, runbook and ownership. Or we stay on a retainer and operate it for you. - 05 ### Measure adoption Who uses it, how often, and what changed in the workflow. Adoption is the metric, not the demo. ## What proof do you have? ### A UK mortgage broker Four teams run 51 departmental skills and five live boards inside Claude, with human sign-off. Read the case → ### A London health-economics consultancy SLR screening and JCA extraction across 27 EU countries, every row traceable. Read the case → ### Big 4 enterprise RAG A fully on-premise RAG system over a compliance library, with every answer cited to its source paragraph. Read the case → ### 24/7 AI call centre 200,000 calls a month across 60 brands, with human handoff when confidence drops. Read the case → ## What does it cost? Time and materials. A five-day discovery sprint defines the first system; embedded delivery typically runs two to eight weeks. We can then hand over, or stay on a monthly retainer to operate the system. No fixed-scope package, because the scope should follow what the team actually adopts. ## AI adoption: frequently asked questions ### Is this consulting, staffing or delivery? All three in one engagement: we advise on what to adopt, embed engineers to build it, and hand over a system your team runs. The commercial model is time and materials, not a fixed-scope package. ### How long does AI adoption take? A discovery sprint is five days. The first production system typically takes two to eight weeks, depending on scope. Adoption is measured after handover, not at the demo. ### Do you train our team or only hand over code? Both. Training is part of the engagement: your team learns to run, monitor and change the system, and the runbook is written with them. Handover includes the code, the evaluation suite and the runbook. ### Can you work with our existing AI tools? Yes. We work with the models, cloud and tools you already use, including Claude, OpenAI, Azure, AWS and on-premise infrastructure. Vendor-neutral means we recommend what fits the constraint, including not using AI. ### What does “adopted” mean, measurably? A named owner, people using the system in their daily work, a defined workflow that changed, and a metric the client agrees to. We do not count a demo as adoption. First-Sprint Data Scientist ## A vetted engineer embedded in your team, a shipped deliverable by end of sprint 1. Pick a time below and you will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship. [Book a call](https://calendly.com/super-chain/catyung?utm_source=super-cat.tech&utm_campaign=first-sprint-data-scientist&utm_content=ai-adoption)Cat Yung Hong Kong & London time zones Book a call · 30 min with Cat Yung