Engineering productivity
Assisted development, review, and testing workflows with measured throughput and quality effects.
Industry
Technology & software
Platforms, infrastructure, and enterprise software — where AI is both the product and the cost line.
Where we start
Technology companies face the AI question twice: how to build it into their products, and how to absorb it into their own cost base and engineering process.
The second question is often neglected and frequently the larger prize. Engineering throughput, support economics, and cloud spend all move materially when AI is applied to the delivery system itself.
We work with product and engineering leadership on the operating model for both, and on the pricing and packaging decisions that AI-native products force.
Typical results
- +18%
- merged pull requests per engineer after workflow redesign
- -27%
- cost per support resolution
- 2
- questions: what AI does to the product, and to the cost base
Illustrative client outcomes, representative of engagements in this sector.
Talk to this practiceHow we help
Product strategy
Where AI changes the product, the pricing model, and the moat — and where it does not.
Support and services
Tiered automation of support with clear escalation economics.
Cloud and infrastructure
Cost and capacity models for AI workloads, including inference economics at scale.
Evidence
What this looks like in practice
- +18%
- merged pull requests per engineer after workflow redesign
- -27%
- cost per support resolution
- 2
- questions: what AI does to the product, and to the cost base
Next step
Bring us your technology & software problem
A ninety-minute working session is usually enough to agree the baseline, the owner, and the first evidence gate.