Artificial Intelligence

Not without your explicit instruction. We deploy using enterprise API arrangements and configurations where customer content is not used for provider model training, and we document the data flow for each integration so you can verify it.

By constraining the problem. Answers are grounded in retrieved source documents with citations, confidence thresholds route uncertain cases to a person, outputs are validated against schemas where structure matters, and evaluation sets are run against every change. Residual error is reduced and monitored, not eliminated, and we say so plainly.

For generative and retrieval-based applications, an organised document corpus is usually enough. Predictive machine learning does need historical data with reliable labels, and the feasibility review establishes early whether you have it.

Through model selection matched to task difficulty, caching, prompt and context efficiency, batching where latency permits, and per-tenant usage limits with cost monitoring and alerts.

Yes. Most of our AI work integrates into existing ERP, CRM, document management and support systems rather than replacing them.

We hold to a published set of commitments on every AI engagement: a named person is accountable for each deployed system, users are told when they are interacting with AI, accuracy is measured before deployment rather than asserted, anyone materially affected can reach a human and contest the outcome, bias is tested for where decisions affect people, and client data is not used to train third-party models. We also decline work: covert surveillance of individuals, deceptive impersonation, and automated decisions about people's rights without human review. You can read the full position on our quality and security page.