Services
AI Cost Optimization
Seeing where AI spend goes and bringing it down without losing quality.
How we see the problem
Finance can name the vendor on the invoice, and cannot name the workflow that spent it.
Token and API fees are only part of the bill. Evaluation, tracing, idle infrastructure and tools bought separately sit on it too. A cut that is not checked against real failures labeled by systems or experts can drop accuracy, and your team will not see that until a customer does.
- The strongest model for every task
Your team has not measured where a smaller model still meets the quality bar.
- Context re-sent on every call
Long instructions and stale documents in every request.
- Five calls where one would do
Agents looping, or AI used where a rule or a search would answer.
- Usage with no owner
Teams adopt tools on their own and the bill arrives centrally.
What we do
Token Ledger records every call before we change anything.
It records every call by team, model and workflow. Then four levers, each run against real failures labeled by systems or experts, so a saving that drops accuracy does not ship. Then budgets in the runtime, checked before each model call.
Spend by team, model and workflow, tracked over time. The baseline comes first.
The cheapest model that meets the quality bar for each step.
Shorter prompts, cached context, repeated work paid for once, rules and search where they do the job.
Monthly budgets enforced before every call, cost recorded per step, reporting per user and agent.
How the work runs
One workflow, instrumented, then tuned.
- 01Instrument
Token Ledger on one high-usage workflow; baseline cost, latency and quality.
- 02Find the levers
Rank by the spend behind each one.
- 03Apply with evals
One change at a time, each checked against real failures labeled by systems or experts.
- 04Hand over
The ledger, the budgets and a review cadence, to the owner of AI economics.
Case studies
Where we’ve put it to work.
Other services
Seven more ways we put AI to work.
- AI TransformationWe write your AI roadmap, build it, and hand it to your team.
- AI SDLCOne agent-ready flow from idea to merged code, scaled team by team.
- AI for GTM & FinanceA data foundation for go-to-market and finance, then the agents that run on it.
- Fine-tuning and EvalsModels tuned to your data, and the evaluations that prove they work.
- ML and Enterprise ForecastingForecasting at enterprise scale, like forecasting Black Friday demand to plan staffing.
- Custom Agent and App DevelopmentAgents and AI applications built for one workflow and run in production.
- Context and Knowledge GraphsTurning what your company knows into context your AI systems can use.