AI system consulting for teams that care about reliability.
Helping organisations build and operate machine learning an AI systems that are measurable, maintainable, and ready for production — with specialisation in search, ranking, and recommendation.
Clarity
From problem framing to success metrics — early.
Reliability
Evaluation, supervision, and monitoring built into delivery.
Momentum
Fast progress on the decisions that unblock teams.
Services
Search, ranking & recommendation
Retrieval pipelines, ranking strategies, evaluation, relevance metrics, and deployment patterns that hold up in production.
Production ML/AI engineering
Data pipelines, training and evaluation, serving, monitoring, and operational practices focused on reliability and maintainability.
Language-model-based components
Prompt design, structured output patterns, supervision/evaluation workflows, and integration with guardrails and observability.
Approach
1) Frame the problem
Align on objectives, constraints, data reality, and what “good” means. Define success metrics early.
2) Make it measurable
Build evaluation and supervision into the system: test sets, offline metrics, human review points, monitoring.
3) Deliver & operationalise
Incremental delivery, documentation, and handover: interfaces, dashboards, and maintenance routines.
4) Improve continuously
Feedback loops from production signals; prioritised iteration based on impact.
Typical engagements
Architecture & evaluation review
Review an existing ML/AI system: data, metrics, modelling approach, failure modes, and a prioritised improvement plan.
Technical due diligence
Independent assessment of ML/AI capability and risk for leadership decisions, partnerships, procurement, or investment contexts.
Hands-on delivery support
Short, high-impact work alongside your team to unblock delivery and improve reliability, evaluation, and monitoring.
Regular check-ins and reviews to keep ML/AI efforts aligned, measurable, and production-ready.
About
mect IT Consulting Ltd is led by a senior applied ML/AI practitioner with a PhD background and extensive experience across research-to-production work. The approach is pragmatic and engineering-focused: define success metrics, deliver incrementally, and build systems that teams can operate long-term.
Contact
Ready to ship production AI? Let's discuss your challenges and build a concrete plan.