Snakepit Book audit

Local-first AI operations

Bring your AI stack under control.

Snakepit helps small and mid-market teams stop token burn, govern agent workflows, and deploy AI systems that are cheaper, safer, observable, and actually useful.

Run the audit preview View the stack
Token burn Shadow AI Broken workflows No observability

Generative UI demo

Show the problem live.

Select a company profile and the page generates an AI Ops canvas: current stack, likely leaks, recommendations, and the first deployment sprint.

Company profile

This is a controlled/declarative hybrid: fixed components, generated content model.

AI Ops Control Map

Live preview

Token Burn Cleanup

Estimate where spend leaks.

Illustrative model for showing prospects why routing, context discipline, caching, and local models matter. Replace the assumptions with real data during an audit.

Current monthly spend $14,230
After routing + local workloads $4,190
Estimated avoidable spend $10,040
  • Install gateway-level cost tracking.
  • Route repeatable tasks to cheaper/local models.
  • Cap context and remove stale workspace state.
  • Trace model calls by user, workflow, and outcome.

Lead capture mechanism

AI Maturity Assessment

Three questions produce a Snakepit Score and recommended first move.

Reference architecture

What Snakepit actually deploys.

Users / Developers / Operators
↓
AI Gateway: LiteLLM
↓
Frontier models
Claude / OpenAI / Gemini
Local models
Ollama / vLLM / OpenRouter
↓
Agent workflows
OpenCode / Codex / custom ops
Observability
Langfuse traces / evals / budgets
↓
Business workflow: ATS, support, CRM, reporting, internal knowledge

First productized offer

AI Ops Audit + Token Burn Cleanup

In 10 business days, Snakepit maps where AI is being used, what it costs, where workflows are leaking, and what to fix first.

Book AI Ops Audit