# Focus & Stack

Cost Optimisation  
Latency  
Caching  
Model Routing  
Redis  
Evaluation  
Monitoring  
Lite  
LLM

Making AI systems faster, cheaper, and better, simultaneously. The engineering that turns an expensive, slow prototype into something economically viable and performant enough for production. Cost, latency, and quality optimised together.

## What we optimise

### Cost Reduction

- Model routing (simple queries to cheaper models, complex to powerful ones, typically 30–50% savings).
- Prompt compression.
- Semantic caching.
- Batch processing for non-real-time workloads.

### Latency Reduction

- Streaming for perceived speed.
- Parallel execution of independent steps.
- Pre-computation for predictable queries.
- Retrieval optimisation.
- Infrastructure tuning and geographic routing.

### Quality Improvement

- Systematic prompt engineering with evaluation suites.
- Structured outputs to reduce errors.
- Dynamic few-shot example selection.
- Evaluation-driven iteration targeting specific failure modes.

## How it works

### Profile—Step 1

- Instrument the system. Token consumption per feature, latency per step, cache hit rates, error rates, quality scores.

### Identify—Step 2

- Find highest-impact opportunities. The feature that’s 60% of cost. The step that’s 70% of latency.

### Implement—Step 3

- Each optimisation validated against quality benchmarks. No cost reduction that degrades quality.

### Monitor—Step 4

- Ongoing tracking of cost, latency, and quality with alerts when metrics drift.

## Deliverables

### What you get

- Cost analysis with breakdown by feature, model, and request type
- Implemented optimisations with measured impact
- Quality evaluation proving no degradation
- Monitoring dashboards for cost, latency, and quality
- Optimisation playbook for ongoing improvement

## AI costs growing faster than the value it’s delivering?

We’ve optimised AI systems at production scale. The cost-quality-latency triangle has a sweet spot. We help you find it with data, not guesswork.