Audit your Azure AI spend, implement multi-model routing and semantic caching — typically achieving 40–70% cost reduction without sacrificing agent capability or response quality.
Every token costs money. Most AI implementations have no token governance — and the bills reflect it. We audit, optimise, and govern AI spend systematically.
Routing every single agent query through your most expensive model — including simple FAQ lookups that a smaller, cheaper model handles perfectly — is the most common source of unnecessary spend.
Sending full system prompts and conversation history on every request, when most of it is identical across calls, multiplies token consumption without any quality benefit.
Without token-level dashboards and cost attribution per agent, workflow, or department, there's no way to know which workloads are driving spend — or what to optimise first.
Semantically similar queries — "what's my account balance" vs "show me my current balance" — hit the LLM independently when a semantic cache would serve the cached response at near-zero cost.
We instrument your AI workloads with Azure Monitor, map token consumption per agent, workflow, and query type, and identify the highest-impact optimisation opportunities before touching any code.
Multi-model routing (GPT-4o for complex tasks, GPT-4o mini or Phi for simple ones), prompt engineering and compression, semantic caching layer, and context window management — deployed against your live workloads.
Token budget alerts, cost-per-conversation dashboards, department-level attribution, and a FinOps framework so your team can maintain spend discipline as AI usage scales across the organisation.
Intelligent routing that sends complex reasoning tasks to GPT-4o, simple lookups and classification to GPT-4o mini or Phi-3 — matching model capability to query complexity and cutting spend by 30–60%.
Cache layer using vector similarity to serve cached responses for semantically equivalent queries — "what are your opening hours" and "when do you open" hit the same cached answer, not the LLM.
System prompt optimisation, context window management, and prompt compression techniques that reduce token consumption per call by 20–40% without affecting output quality.
Azure Monitor workbooks with per-agent, per-workflow, and per-department token consumption, cost attribution, and trend analysis — so optimisation is data-driven, not guesswork.
Analysis of your usage patterns to identify where Provisioned Throughput Units (PTUs) in Azure OpenAI deliver better cost predictability than pay-per-token for high-volume, predictable workloads.
Token budget alerts, spend approval workflows, cost tagging strategy, and a FinOps operating model for AI — so governance scales as you add more agents and workloads across the organisation.