Our approach
Most AI consultancies want you to keep paying them. We want you to stop needing us. Here's how we think about the work.
Principles
We build controls around what you own: agent platforms with usage logging, input sanitisation, output filtering, and monitoring. Provider-side behaviour remains outside your control, so we help you mitigate that through prompt engineering and response validation.
We evaluate current frontier models based on your data residency requirements, cost constraints, and latency needs—not hype. Different use cases often warrant different models.
Demos impress stakeholders; production systems serve users. We design for error handling, graceful degradation, monitoring, and the reality that models behave unpredictably.
We establish baseline metrics before deployment and track specific improvements: time-to-completion, error rates, throughput, cost-per-task. No vague claims of 'productivity gains'.
We train your team on prompt engineering, evaluation methods, and operational monitoring. The goal is independence, not a recurring consulting dependency.
Honest constraints
AI systems have real limitations. We'd rather tell you upfront than let you find out in production.
Fit
Not because we're precious—because the work only succeeds with the right conditions.