erabot scans your codebase and shows exactly where the money leaks, analyzes for fixes and then proves they work by replaying your real traffic and testing that quality holds before you change a line. Runs in your own environment under your own keys, so your source never leaves.
From the erabot validation eval — every number traces to a signed proof artifact.
Live Demo
Watch how we find per-request savings from 25 lines of code.
How It Works
Four steps. Eight AI agents. Zero setup. Just connect and go.
Run the erabot CLI in your repo, or wire in the GitHub Action. Detects 60 models across 10 providers. Zero config.
Eight AI agents scan in parallel — Token Optimizer, Model Selector, Cache Analyzer, Architecture Reviewer, Cost Projector, Prompt Engineer, Batch Strategist, and Rate Limiter. AST parsing, token counting, and a RAG knowledge base surface every wasted token.
erabot shadow-verifies every fix — it applies the change in-memory and re-runs to confirm quality holds before you see it. You get three output formats: a branded PDF, a Claude Code-ingestible markdown report with code diffs, and git-apply patches.
Connect Helicone, Langfuse, or OpenTelemetry and estimates become measured spend. A CI cost-gate fails a PR when a diff would raise your bill, and realized savings are tracked as fixes land.
Open-weight migration
The biggest line on your bill is usually a frontier model doing work a cheaper open-weight model could do just as well. erabot replays your real traffic through open-weight candidates cheapest-first, verifies every call site objectively — executed code, structural checks, ground-truth accuracy — rewrites the prompt when a swap fails, and ships each migration as a signed, expiring proof. Nothing is downgraded on a guess.
Beyond single calls
Modern AI spend isn't one big call — it's agents looping, calling tools, and running skills. erabot finds the waste and the runaway risk hiding in that machinery.
Multi-step agents fan out call after call. erabot maps the orchestration and flags where an agent re-runs a frontier model on a step a cheaper rung would handle — so a single audit shrinks the whole chain, not just one prompt.
The pattern behind runaway bills and stuck agents is an LLM call inside an unbounded loop with no turn cap or spend ceiling. erabot detects those loops statically and proposes a bounded guard — shadow-verified so the cap never breaks the task.
Every tool or skill an agent can call is a cost surface. erabot surfaces oversized tool context, redundant skill invocations, and uncached repeat calls — the quiet waste that never shows up as a single expensive request.
Results
Detection precision, scored against independent labels on 520 repos
Full-stack recall — four detection layers, measured on 3,049 labeled call sites
LLM call sites audited across 15 real open-source AI products
Measured cost cut at 100% output quality (escalation routing, replayed traffic)
The biggest savings come from running a task on a cheaper model — but get it wrong and you ship a quality regression. erabot never recommends a downgrade on a guess. It runs a simulation first:
Only swaps that hold up are shipped. Ones that don’t are shown with their measured quality — never applied automatically.
The unsafe downgrade is blocked and downgraded to a routing fix that keeps quality at 100%.
Output Formats
Every scan produces three deliverables — one for each audience.
Branded executive report with optimization grade, cost breakdown, and per-finding savings. Share with your CTO or finance team.
Download and share — no technical setup needed.Human-readable markdown with rounded savings, per-finding "What to consider" analysis, and no code diffs. Built for skimming and sharing.
Share with your team — human-readable cost analysis.Structured handoff for Claude Code, Cursor, and Copilot. Each finding includes architectural context, fix locus, and anti-patterns to avoid.
Point Claude Code at agent-instructions.md — structured handoff.erabot runs where you already work. Drop a single command into Claude Code or Cursor and your coding agent can run the audit and apply the fixes for you — every finding comes as an apply-ready agent-instructions.md it can act on directly.
Integrations
Drop erabot.ai into any AI stack — zero migration required.
Pricing
No hidden fees. Cancel anytime. Your savings pay for the plan.
For individual developers serious about costs
For engineering teams optimizing at scale
For teams running $10K+/mo on LLM APIs
Not sure which tier? Book a demo
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