Right Model, Right Task, Right Cost
Most companies default to one model for everything and overpay by 10x or more. We benchmark models against your actual use cases, engineer production-grade prompts, and build API architecture that routes each task to the best model at the lowest cost. $0.30 per audit run, not $3.00.
of enterprise AI spending is wasted on overprovisioned models and unoptimised inference. Organisations routinely use frontier models for simple tasks that smaller, cheaper models handle equally well. The gap between what companies spend and what they need to spend is significant.
Andreessen Horowitz, The State of AI Infrastructure (2024)
“We are paying thousands a month on API calls and have no idea which model we should actually be using for what.”
Teams build a prototype on GPT-4, it works, and they ship it. Now every task runs through the same expensive model whether it is summarising a document or extracting a phone number from an email. The API bill grows every month, the outputs are inconsistent, and nobody has visibility into what is working and what is wasting money.
Stop Overpaying
for AI.
There are dozens of production-ready models available today: Claude, GPT-4, Gemini, Llama, Mistral, and more. Each has different strengths, different pricing, and different performance characteristics. Defaulting to one model for every task is like hiring a senior engineer to do data entry.
We benchmark models against your actual workloads, not generic benchmarks. A model that scores highest on reasoning tests might be overkill for your invoice extraction pipeline. We find the model that delivers the quality you need at a fraction of the cost, then build routing logic so simple tasks go to cheap models and complex tasks go to capable ones.
Use-Case Benchmarking
We test models against your real data and real tasks. Accuracy, latency, cost per call, and output consistency, all measured on your workloads so you make decisions on evidence, not marketing.
Model Routing & Tiering
Intelligent routing that sends each request to the right model based on complexity, cost, and latency requirements. Simple extraction to a fast, cheap model. Complex reasoning to a capable one.
Cost Analysis & Tracking
Per-call cost tracking across every model and endpoint. You see exactly what each workflow costs to run, where the spend is concentrated, and where switching models saves money without losing quality.
Prompt Engineering & Structured Outputs
Production-grade prompts with chain-of-thought patterns, structured JSON outputs, and evaluation pipelines that catch quality regressions before they reach your users.
API Architecture & Fallbacks
Clean API layers with rate limiting, retry logic, provider failover, and circuit breakers. When one provider goes down, your system keeps running on an alternative.
Output Validation & Monitoring
Schema validation on every LLM response, automated quality scoring, and real-time dashboards showing cost, latency, and error rates across all your integrations.
From Prototype
to Production.
Your ChatGPT prototype works in a notebook. Making it work reliably at scale in production is a different problem entirely. Rate limits, provider outages, inconsistent outputs, malformed responses, cost overruns. These are engineering challenges, not prompting challenges.
We build production-grade LLM integrations with structured outputs, evaluation pipelines, fallback providers, and proper error handling. Every integration includes monitoring, cost tracking, and output validation. When a provider goes down, your system switches to a backup automatically. When an output does not match the expected schema, it retries or escalates. This is infrastructure, not experimentation.
What's Included
Industries We Serve
Healthcare
Integrate language models for clinical note summarisation, patient communication, and documentation with full privacy controls.
Financial Services
Route financial document analysis to the right model at the right cost while keeping sensitive data off third-party servers.
Legal
Integrate LLMs for contract analysis, brief drafting, and legal research with data privacy architecture built for privilege.
Frequently Asked Questions
Which models do you work with?
All major providers and open-source models, including Claude, GPT-4, Gemini, Llama, Mistral, and dozens of smaller specialised models. We benchmark multiple candidates against your specific workloads and recommend based on your use case, latency requirements, data sensitivity, and budget.
We already have LLM integrations running. Can you optimise what we have?
Yes. We audit your current usage, identify where you are overpaying or underperforming, and implement changes that reduce cost and improve reliability. Common wins include swapping overprovisioned models for cheaper alternatives, adding structured outputs, and implementing proper caching for repeated queries.
How do you handle data privacy with third-party model providers?
We architect integrations with data privacy as a first-class concern, including zero-retention API agreements, stripping sensitive fields before they reach the model, and using self-hosted models for workloads where data cannot leave your environment. We help you choose the right deployment model for each use case based on your compliance requirements.
What does a typical engagement look like?
Most engagements run three to six weeks, starting with use-case mapping and current-state analysis, then moving through model benchmarking, prompt engineering, and architecture design. You get working production code, monitoring dashboards, cost tracking, and documentation your team can maintain.