Advisory Intake
Start with the system constraint.
The best conversations are specific. Bring the architecture problem, the stack, the current failure mode, and the decision you need to make next.
Inference Budgeting
Estimate your token costs & architecture latency.
Operations pricing is determined by prompt context size and model decisions. Adjust the parameters below to project standard costs vs. optimized local hybrid routing paths.
AI Audit Estimator
Model cost, latency, cache savings, and review signal in one pass.
Primary Model
Monthly Token Budget
577.5M
75,000 monthly queries
Estimated Savings
$819
Semantic cache + context compaction
Sync P95
3.2s
Direct request-response path
Async Queue
5.4s
Queued worker path with retry control
Monthly Model API Budget
Baseline
$1,950
Optimized
$1,131
Architecture Readout
Review focus: cache, compaction, queues, reranking, and fallback routing.
Qualification
Good fit if the problem is real.
This intake intentionally filters for serious technical buyers and teams with a production path.
- AI product moving from prototype to production
- RAG system with quality, grounding, or latency issues
- Agent workflow that needs state, tools, and observability
- AI infra company needing credible DevRel engineering assets
- Founder or CTO seeking fractional architecture judgment
Send this context
- 01
What are you building, and who uses it?
- 02
Where is the system today: idea, prototype, pilot, or production?
- 03
Which constraint hurts most: retrieval quality, agent reliability, latency, cost, observability, security, or adoption?
- 04
What is the current stack: models, vector DB, backend, cloud, deployment, and eval tooling?
- 05
What decision do you need help making in the next 30 days?