Work with me

Your inference bill drops at least 30%, or you don't pay. Complexity-based routing so easy queries stop hitting a frontier model, plus a two-tier semantic cache so paraphrased repeats never hit the API at all.

• Spend map: cost broken down by route, model and cache-miss reason
• Embedding + heuristic complexity router, A/B'd against your current path
• L1 in-process and L2 Redis cache with semantic similarity matching
• Dashboards for cost per request, hit rate, p95 latency and quality delta

Best if you're spending north of $8k/month on inference.

Your agent works in the demo and fails for real users. I pull your production traces and tell you exactly why, ranked by how often it happens.

• Failure taxonomy with measured frequencies, not guesses
• Golden eval set built from your real traffic, wired into CI
• Ranked fix list with effort estimates
• The two cheapest high-impact fixes shipped as PRs

Best if you have an LLM feature in production and no eval suite.

Users search for the exact thing and don't find it. Almost always pure vector recall with no keyword arm, so exact-match queries fall straight through.

• Judged eval set built first, so every change is measured
• Structure-aware chunking and ingestion fixes
• Hybrid BM25 + dense retrieval with fusion and metadata filters
• Latency-budgeted cross-encoder rerank

Recall@10 improves 25% on your own query set, or I keep working free.