Toolchain selection and governance
- Selection by team size and stack — code assistants, agents, review, test generation
- Unified accounts, permissions and cost control
- Data compliance boundaries and an internal usage policy
Product 03 · System building
AI engineering delivery systems
Get AI genuinely into your engineering workflow, rather than buying your team a few tool licences. What we deliver is a system that keeps running without us.
Why buying AI tools did not raise throughput
- Plenty of tools, everyone using them differently, wildly inconsistent output quality.
- Nobody validates AI-generated code, so review costs went up rather than down.
- The codebase is too large for the AI to see enough context, so its answers are unusable.
- There is no measurement, so nobody can say whether it helped — and therefore nobody can justify it.
- Security and compliance questions are unresolved, so usage stays hidden and small-scale.
The hard part was never model capability. It is engineering discipline: how context is supplied, how output is verified, how the workflow connects, how quality is defended. Those are system problems, not tool problems.
What we deliver
Context engineering standards
- Restructuring the codebase so AI can actually read the project
- Project-level rule files and a prompt template library
- Machine-readable conventions for requirements and technical docs
Delivery pipeline changes
- Automated review and quality gates wired into CI/CD
- Test generation and regression strategy
- Explicit human/machine boundaries: what must always be reviewed by a person
Measurement and improvement
- Observable metrics: cycle time, rework rate, review time
- A metrics dashboard and a monthly retrospective routine
- Continuous adjustment of standards and tooling based on the data
Team capability
- Hands-on training by role: developers, QA, tech leads
- Pairing through one complete cycle on real work
- Internal rollout materials and a Q&A channel
Deliverables
- System design document and rollout roadmap
- A running pipeline and its configuration
- Standards documents and prompt template library
- Metrics dashboard and retrospective templates
How we run it
- 01
Assessment
Look at the codebase, the process and current AI usage to find the real bottleneck.
- 02
System design
A coherent design for tooling, standards, pipeline and metrics, with staged goals.
- 03
Pilot
Run one real business line end to end to produce a repeatable template.
- 04
Rollout and training
Role-based training, spread the template to other teams, ship the usage standards.
- 05
Measure and iterate
Review against metrics, adjust, and hand a self-sustaining system to your team.
FAQ
Are you just going to recommend a pile of tools and leave?
No. The core deliverables are the standards, the pipeline and the measurement mechanism; tools are one part of it. The project ends when your team can run it without us — not when the documents are handed over.
Our stack is fairly old. Does this still apply?
It can, but we assess feasibility first. AI assistance is usually weaker on legacy stacks; if the return does not justify the effort we will say so rather than push ahead.
How do you handle code security?
The assessment begins by drawing the data boundary: which code may leave the intranet and which must stay behind a private deployment. The proposal states the data flow at each step and gives compliance recommendations.
Can you guarantee a percentage improvement?
We do not promise a specific percentage. What we promise is a measurement mechanism that lets your own data answer the question. Anyone quoting you a precise figure deserves your suspicion.
Start with an assessment
Tell us your team size, stack and current AI usage. We will give you an initial read: which parts are worth attacking first, and which are not worth the investment yet.
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