AI Cost Reduction & LLM Optimisation

Every successful AI product hits the same wall. Usage scales, the API bill scales faster, and suddenly your AI feature is eating your margin. This is the Inference Crisis, and it is entirely solvable.

We implement systematic cost reduction across your AI stack — from prompt engineering to infrastructure architecture — delivering 40–80% savings while maintaining or improving output quality.

Where the Money Goes

In our audits, we consistently find three areas of financial leakage.

The Reasoning Overkill — Using GPT-4-class models for tasks that a model 10x cheaper handles equally well. Classification, extraction, formatting, and simple summarisation do not need frontier reasoning. Most production traffic can be handled by smaller, faster, cheaper models.

The Redundancy Loop — In typical RAG and chatbot systems, 20–30% of queries are semantically identical. Without caching, you pay for the same answer thousands of times. Semantic caching serves these at near-zero cost.

The Prompt Bloat — System prompts grow as teams paste in edge-case instructions. A 2,000-token system prompt sent with every 100-token query means 95% of your input tokens are overhead. Prompt compression and context pruning cut this dramatically.

Our Approach

Semantic Caching — A vector-based caching layer that identifies "close enough" queries and serves cached responses instantly. Typical hit rates of 20–40% on production traffic.

Dynamic Model Routing — A lightweight classifier that routes each request to the cheapest model capable of handling it well. Simple tasks go to small models. Complex tasks go to frontier models. Blended cost drops 50–70%.

Prompt Engineering — Systematic compression of system prompts, context pruning for RAG pipelines, and few-shot distillation. Less input means less cost and less latency.

Infrastructure Right-Sizing — Evaluating whether API, managed, or self-hosted inference makes sense for your volume and requirements.

Supporting Technical Guides