AI Solutions
LLM evaluation, fine-tuning and cost optimization
Sound familiar?
Changes nobody can measure
Someone edits a prompt, it feels better on three examples, and it ships. Two weeks later support tickets rise and nobody can prove which change caused it or roll it back with confidence.
A model bill that keeps climbing
Every request goes to the biggest model with the longest prompt. Usage grows, the invoice grows faster, and finance is asking for a number nobody on the engineering team can defend.
Outputs that embarrass the brand
The assistant occasionally invents a policy, leaks a system prompt, or answers off-topic. Without guardrails and tests, every incident is discovered by a customer first.
What you get
- Evaluation set from real traffic
- Automated regression runs
- Accuracy and quality scorecards
- Input and output guardrails
- Model routing by task
- Prompt and response caching
- Token budgets and alerts
- Fine-tuning when it pays off
Stack and tools
- Claude API
- OpenAI API
- Promptfoo
- LangSmith
- Vercel AI SDK
- Redis
- PostgreSQL
- Grafana
- Python
- TypeScript
How we do it
- 1
Baseline
We sample real requests and outputs, label them with your team, and build an evaluation set that scores accuracy, format, safety and tone. This becomes the bar every change must pass.
- 2
Instrument
Every model call is logged with cost, latency, tokens and the prompt version. Within a week you can see which features, users and prompts drive spend and where quality dips.
- 3
Optimize
Cheaper models handle easy requests, caching removes repeated work, prompts shrink without losing accuracy, and budgets alert before overruns. Each change is proven on the eval set.
- 4
Guard and fine-tune
Guardrails catch injections, off-topic requests and policy violations. If format or tone still miss after prompting, we fine-tune a smaller model and compare it against the baseline.
Starting at
from $3,000
Typical timeline 2 weeks. Covers one AI feature. Fine-tuning runs are quoted separately.
Related work
LLM Evals & Optimization: questions people ask
Further reading
Let's scope your llm evals & optimization project.
Tell us what you are building or what is slowing you down. We reply within one business day.