AI & ML Engineering
Build AI and machine learning features for a defined task and approved data, with quality evaluation, human review, and monitoring.
Why?
An impressive demo is only a starting point. A useful AI feature needs representative evaluations, clear limits, and a safe fallback when its output cannot be trusted.
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Evaluation before expansion
Compare quality, failure cases, latency, and cost against an agreed baseline before widening deployment.
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Approved data and clear access
Define permitted data sources, retention rules, and access boundaries before connecting models to business information.
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Human review where it matters
Use review gates and fallback paths for uncertain outputs; high-stakes decisions stay with authorised people.
Our process
Define the task and limits
Agree the use case, success measures, prohibited actions, and points requiring human approval.
Approve data and access
Confirm rights to use data, minimise sensitive fields, and document retention and provider settings.
Build a measurable baseline
Compare a simple baseline with suitable models or retrieval approaches before adding complexity.
Evaluate quality and failure modes
Test representative examples, unsupported answers, misuse scenarios, latency, and cost against agreed criteria.
Integrate with human oversight
Add access controls, review steps, fallbacks, and release gates; do not delegate high-stakes decisions autonomously.
Document monitoring and change control
Hand over evaluation sets, model and prompt versions, monitoring guidance, and approval steps for updates.
Questions?
Not always. We compare rules, existing models, retrieval, and training options against task quality, cost, and data readiness. More complex modelling must justify its benefit.
Only with approved rights, access, and handling rules. We confirm provider settings, retention, sensitive-data minimisation, and deployment constraints before data is shared or processed.
We use representative evaluation cases, error analysis, and agreed quality thresholds, with human review and fallbacks. High-stakes decisions are not delegated to an autonomous model.
Data access, use-case complexity, integration needs, and evaluation readiness determine the plan. We agree a bounded experiment and a go/no-go review before expanding delivery.
