AI solution
LLM
Language model engineering: selection, integration, tuning and evaluation.
Provider selection and benchmarkingStructured output and function callingFine-tuning and distillation where justified
Capabilities
What this solution does.
Provider selection and benchmarking
Structured output and function calling
Fine-tuning and distillation where justified
Outcomes
What changes for the business
- Right model for each task, not one model for all
- Lower cost per resolved task
- Measured accuracy before release
Architecture
How it is built
- 01Abstraction layer over providers
- 02Evaluation harness in CI
- 03Cost and latency telemetry per route
Related services
AI StrategyA board-ready plan linking AI investment to specific processes, numbers and timelines.AI Readiness AssessmentA structured audit of data, systems, skills and risk posture before you commit budget.Private AIModels hosted inside your cloud or data centre so sensitive data never leaves your boundary.Generative AIApplied generation for drafting, summarising, extraction and content operations.LLM IntegrationEmbedding language models into existing applications, APIs and business workflows.RAG SystemsRetrieval-augmented generation grounded in your authoritative documents with citations.
Industries applying this
HealthcareClinical throughput, administrative relief and defensible data handling.ManufacturingConnected plants, predictable output, lower unplanned downtime.EducationAcademic operations, student experience and institutional reporting.RetailDemand accuracy, margin protection and connected commerce.GovernmentCitizen services, digitised process and auditable delivery.MiningSafety, asset uptime and site-to-corporate visibility.
Downloadable guide
LLM implementation guide
Reference architecture, governance controls, evaluation approach and a phased rollout plan.
Move from AI pilot to
production system.
Bring a use case and we will map data readiness, guardrails, evaluation and the route to a governed production release.