Tune the AI you already shipped. Cut spend, hold quality.
Prompt caching, model tiering, streaming, evals. The levers that actually move cost and latency on a Claude or OpenAI bill.
For teams who already have an AI feature in production and the bill is growing faster than the usage. We do not replace your build — we tune it.
Stack review for a $30M company picking between OpenAI, Claude, and Bedrock. 1-hour call, 3-page memo, decision made.
The levers we actually pull
Named because the names are how you tell whether the engineer touching your AI bill knows what they are doing.
Prompt caching on Claude
Lift cacheable system prompts and few-shot examples into the cache control block. 90% off on cached input tokens. Often the single biggest cost cut on a Claude bill.
Model tiering
Cheap model first — Haiku, GPT-5 nano, Gemini Flash — with confidence-routing to Sonnet/Opus only when needed. Half the bill, same output quality.
Streaming + UX
Stream tokens to the UI so perceived latency drops 5-10x even when wall-clock is unchanged. Often cheaper than the engineering cost of speeding up the model itself.
Function calling, structured output
Stop parsing free-text JSON. JSON schema mode + Zod validation cuts retries to zero, drops token spend, and removes the ‘LLM returned malformed JSON’ class of bug.
Eval harness
Golden-set evals checked into git. Every model swap, prompt change, or temperature tweak runs against the set first. You ship on numbers, not vibes.
Retrieval cleanup
Most RAG setups retrieve too much, the wrong chunks, and at the wrong granularity. Hybrid search + rerank + chunk strategy review usually cuts token spend by half and lifts accuracy.
When to call
A few hours of tuning now is cheaper than a quarter of runaway spend.
Existing AI build, runaway costs
Your Claude/OpenAI bill is climbing faster than usage. We audit the prompt, the cache strategy, the model tiers, and the retrieval pipeline. Most builds we touch get 40-70% off the monthly bill in week one.
Latency complaints
Users say it is slow. We check whether it actually is, or whether the UX is just hiding the work. Streaming, model swaps, parallel tool calls, async tasks — whichever lever moves the meter without breaking the output.
Output quality regressions
Outputs got worse after a model swap, a prompt edit, or a vendor update. We bring the eval harness, find the regression, and ship the fix with numbers attached.
Vendor migration
Moving from OpenAI to Claude, Claude to Bedrock, or off a wrapper SDK to direct API. We do the migration with parity evals so you ship the change and the numbers in the same week.
Pricing
Build / Scale / Consult. Same tiers across every Wolrix service.
Audit of an existing AI build. Prompt-cache review, model-tiering review, eval-harness review. Written summary in 48 hours.
Implement the audit. Caching, tiering, structured output, eval harness checked into git. Cost cut + quality held, with numbers.
Full optimization sweep. Tiering, retrieval rebuild, vendor migration, observability stack. For builds at 6-7 figure annual AI spend.
Send the bill. We will find the lever.
A few hours of advisory now is cheaper than another quarter of runaway model spend. NDA before any details.