- Challenge
- Engineers used AI tools individually, without governance, and nobody could say what it had done for delivery.
- Approach
- An AI-native engineering framework: a business plan under the organisation's control, standards enforced as checks on every change, skills per role, and measurement.
- Outcome
- AI-executed delivery 46% → 81% in one quarter, about 2× throughput, every change human-reviewed, no production defect traced to AI-written code.
Case Studies
Measured results, not adjectives.
Each case study states the problem, the constraints, what we changed and the numbers before and after. Clients are described rather than named, because our work is covered by non-disclosure agreements.
- Challenge
- A 36-service platform's knowledge lived in code, tickets, a stale wiki and a few people's heads, and the sources disagreed.
- Approach
- One knowledge base capturing what the product does and what the business intended, reconciled against the product on every change.
- Outcome
- Every ambiguity resolved; 10 hidden divergences found in the first 30 capabilities; development up to 3× faster as engineers stopped waiting for answers.
Financial software product · Proprietary AI model
Near real-time AI inside a financial product, over 99% accurate
- Challenge
- Millions of small decisions a month made by hand; the existing model was right about half the time on the hardest cases.
- Approach
- Ten candidate models compared on the same terms, the winner fine-tuned on the client's own history, calibrated to know when to ask, and compressed to run in milliseconds.
- Outcome
- Over 99% accurate on the 87% of decisions it makes alone (97.6% overall), hardest cases up by 34–43 points, model ten times smaller and 11× faster.
Enterprise platform · Performance & running cost
Faster screens and half the storage, without a rewrite
- Challenge
- The most-used screen took 5 to 12 seconds to open, and nobody could say how large the databases should be.
- Approach
- Measured first, fixed the structural causes rather than the symptoms, moved every setting into source and put each result under a test.
- Outcome
- 85 database round trips became 9 and a lookup went from 506 ms to 2 ms; the largest tables shrank by 50.7%.
- Challenge
- Good written standards nobody enforced: twenty services handled failures their own way, and an AI reviewer ran with no knowledge of the rules at USD 20 a day.
- Approach
- Standards moved into shared code and the compiler; the AI review rebuilt to cite a written rule for every finding and to test itself on seeded defects.
- Outcome
- 20 of 20 services consistent, 612 more tests passing, review cost cut sevenfold, one harness across three repositories.
Financial services · Cloud architecture
A cloud-native platform built to scale, without the bill scaling with it
- Challenge
- A product serving many small funds from one platform: near-real-time figures, growth with the customer base, and a cost per customer close to zero.
- Approach
- Event-driven processing that scales with the work and idles when there is none, managed services where cheaper than running your own, and a cost model checked before the build.
- Outcome
- A scalable platform with near-real-time processing whose cost grows with usage rather than capacity, run by a small team.
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