Cut your LLM costs by up to 90% without sacrificing quality

Q: How do you achieve 90% cost reduction?

The biggest lever is model routing — most tasks don’t need the most expensive model. Combined with caching, prompt optimisation, and batch processing, the savings compound. We build evaluation frameworks first so every optimisation is validated.

Q: Will cheaper models reduce quality?

Not if you measure it and fine-tune a cheaper model. We build automated evaluation before changing anything, so every optimisation is validated against quality baselines.

Q: How quickly do we see savings?

Typically within 2–3 weeks of starting. The audit alone usually reveals quick wins that deliver immediate savings.

Q: Do you work with specific LLM providers?

We are provider-agnostic. We work with OpenAI, Anthropic, Google, Mistral, open-source models, and others. The right model depends on the task, not the vendor.

We audit and restructure LLM cost profiles through intelligent model routing, evaluation-driven optimisation, caching, and architecture decisions. Proven results, not theoretical savings.

LLM costs scale in ways traditional software doesn’t

Most companies have no visibility into what is driving their AI spend, no evaluation framework to know if cheaper models would perform just as well, and no architecture optimisations in place. The result is bills that grow linearly with usage and no way to know if you are overspending.

Using Claude Opus 4.6 for tasks that a smaller, cheaper, fine-tuned model handles equally well

No evaluation framework to measure quality, so nobody can prove a cheaper model works

Every prompt hitting the API fresh — no caching, no batching, no prompt optimisation

Finance asking engineering to justify LLM spend and engineering having no data to show

What we deliver

Cost audit

Full breakdown of LLM spend by use case, model, token volume, and cost per task.

Evaluation framework

Automated quality measurement so you can objectively compare model performance across tasks.

Model routing

Route tasks to the most cost-effective model that meets quality thresholds.

Caching architecture

Semantic and exact-match caching to eliminate redundant API calls.

Prompt optimisation

Reduce token usage through prompt engineering, structured outputs, and context management.

Batch processing

Move non-real-time tasks to batch APIs at significantly reduced cost.

Ongoing monitoring

Dashboards tracking cost per task, quality metrics, and spend forecasts.

How it works

Audit

1 week

Instrument your LLM usage, build cost attribution by use case, identify the top cost drivers.

Evaluation Build

1–2 weeks

Create automated evaluation for each use case so we can measure quality before and after changes.

Optimisation

2–4 weeks

Implement model routing, caching, prompt optimisation, and batch processing — validating quality at each step.

Monitoring & Handover

Cost dashboards, quality dashboards, documentation, team training.

Who this is for

Companies spending £5k+/month on LLM APIs who suspect they are overpaying

Engineering teams using a single model (usually an Opus version) for everything

Businesses scaling AI features where cost is becoming a blocker to wider deployment

CTOs who need to justify AI spend to the board with data, not hand-waving

Track Record

Relevant credentials

90%

LLM cost reduction achieved on client engagements

Decades

building production systems at scale

C-Suite

Senior leadership across multiple AI-native companies

Quality regressions from cost optimisation

Frequently asked questions

How do you achieve 90% cost reduction?+

Will cheaper models reduce quality?+

How quickly do we see savings?+

Do you work with specific LLM providers?+

Ready to get your LLM costs under control?

Most companies are spending 3–10x more than they need to on LLM APIs. Let’s find out where your money is going and fix it.