EB
erabot.ai
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Static analysis + agent-ready patches for AI cost

The first AI cost tool that writes the fix, not just the report.

Scan your repo. Get an agent-instructions.md Claude Code can apply in one command. Stop paying to re-read the report — and stop paying to send tokens you never needed to send.

Running $10K+/mo on LLM APIs? Talk to the founder →
Free to startNo credit cardPatches, not dashboards

No spam. Only launch updates and cost optimization tips.

Try it now — drop a file or paste a snippet. No signup needed for the first scan.

Live Demo

See erabot in action

Watch how we find per-request savings from 25 lines of code.

erabot scan
api_client.py×
config.yaml×
1import openai
2 
3client = openai.OpenAI()
4 
5def analyze_document(doc: str) → dict:
6 # Full document sent as context every call
7 response = client.chat.completions.create(
8 model="gpt-4o",
9 messages=[
10 {"role": "system", "content": SYSTEM_PROMPT},
11 {"role": "user", "content": doc} # 12K tokens avg
12 ],
13 temperature=0.7,
14 )
15 return response.choices[0].message.content
16 
17def summarize_batch(documents: list[str]):
18 results = []
19 for doc in documents:
20 # No caching — identical docs re-analyzed
21 results.append(analyze_document(doc))
22 return results
scan results

The Problem

Your LLM bill is growing

0%

Average token waste in LLM call sites

0%

Average context window underutilization

<0 min

Average time to first actionable insight

0+

Passing tests — production-hardened engine

The Platform

Every line of code. Every token spent. Fully understood.

Continuous cost monitoring

Deep Code Analysis

Tree-sitter powered AST parsing across Python, TypeScript, Go, Java, and Ruby. Detects every LLM call — even inside helper functions and async wrappers.

Real-Time Token Efficiency Dashboard

Visualize token flow by model, endpoint, and call site. Track context window utilization, spot efficiency trends, and catch token waste before it compounds.

Concrete Fixes

Get actionable diffs you can apply in one click. Claude Code-compatible markdown reports with exact code changes and estimated savings per fix.

Context Window Sentinel

Continuous monitoring via GitHub or proxy. Get context window alerts when utilization drops, and catch token waste in PRs before they merge.

How It Works

From codebase to savings in minutes

Four steps. Eight AI agents. Zero setup. Just connect and go.

Step 01

Connect

Paste code, upload a ZIP, connect GitHub, or point our proxy at your API calls. Detects 60 models across 10 providers. Zero config.

Learn more
Step 02

Analysis

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.

Learn more
Step 03

Optimize

Helioscan verifies every recommendation by applying changes in-memory and re-scanning. You get three output formats: a branded PDF, a Claude Code-ingestible markdown report with code diffs, and git-apply patches.

Learn more
Step 04

Monitor

Proxy monitors real API usage across 60 models. Budget caps with auto model fallback, email/webhook alerts on spend thresholds, and semantic caching to cut redundant calls.

Learn more

Output Formats

Three ways to ship fixes

Every scan produces three deliverables — one for each audience.

PDF Report

For stakeholders

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.

report.md

For humans

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.

agent-instructions.md

For AI coding agents

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.
Proprietary Engine

Verified by Helioscan

Every recommendation proven, not just predicted. Helioscan applies each optimization in-memory and measures the real cost delta before including it in your report.

01

8 AI agents analyze your code

Token Optimizer, Model Selector, Cache Analyzer, Architecture Reviewer, Cost Projector, Prompt Engineer, Batch Strategist, and Rate Limiter work in parallel across every file.

02

Atomic changes proposed one at a time

Helioscan isolates each optimization into a single, minimal code change. No compound edits. No guesswork about which change caused what.

03

Each change applied in-memory and re-scanned

The modified code is re-parsed, re-tokenized, and re-costed in a sandboxed environment. The before-and-after cost delta is measured, not estimated.

04

Only provably cost-reducing changes survive

If a change does not measurably reduce cost, it is discarded. Your final report contains only verified optimizations with real dollar figures.

Verification Engine

Each finding is applied, measured, and validated before it reaches your report

prompt_cache_miss$12.40/mo
model_downgrade$8.20/mo
batch_consolidationdiscarded
200iterations per scan
ASTvalidated code changes
100%verified savings
Try it free — 3 verified findings per scan

Built For

Who uses erabot.ai

Cut your bill

Solo Developers & Side Projects

Stop bleeding money on personal AI projects. See exactly where your tokens go and slash your API bill without sacrificing output quality.

Cut burn rate

AI-First Startups

Move fast without burning your runway on AI costs. Get the same output for a fraction of the spend.

Full cost governance

Enterprise Engineering Teams

Bring visibility and governance to distributed AI usage across hundreds of services and teams.

CI/CD native

Platform & DevOps Teams

Integrate directly into CI/CD. Scan every PR automatically and block regressions before they ship.

Strategic clarity

Founders & CTOs

Make data-driven decisions on AI provider selection, model choice, and optimization ROI.

Integrations

Works with the tools you already use

Drop erabot.ai into any AI stack — zero migration required.

OpenAIAnthropicGoogle CloudMistralCohereGroqOpenAIAnthropicGoogle CloudMistralCohereGroqOpenAIAnthropicGoogle CloudMistralCohereGroq
LangChainOllamaVercel AIHugging FaceTogetherCursorLangChainOllamaVercel AIHugging FaceTogetherCursorLangChainOllamaVercel AIHugging FaceTogetherCursor

Pricing

Start free. Scale as you save.

No hidden fees. Cancel anytime. Your savings pay for the plan.

Free

Explore what erabot.ai can find

$0/mo
  • 5 scans per month
  • 20 files per scan
  • Summary-level findings
  • Single file upload
  • Community support

Pro

For individual developers serious about costs

$49/mo
  • Unlimited scans
  • 50 files per scan
  • Full MD + PDF reports
  • GitHub repo scanning
  • One-click PR button
  • Email support
Most Popular

Team

For engineering teams optimizing at scale

$274/mo
Seats
5
  • Everything in Pro
  • 200 files per scan
  • Up to 15 team members
  • Team cost dashboard
  • Shared scan history
  • Priority support

Enterprise

For teams running $10K+/mo on LLM APIs

Custom
  • Founder-led 30-min cost audit
  • Everything in Team
  • 500+ files per scan
  • Unlimited members
  • SSO / SAML
  • Custom DPA
  • SLA guarantee
View full pricing comparison →

FAQ

Frequently asked questions

Your next LLM cost reduction starts today.

Start for free. No credit card. See your first savings in under 5 minutes.

EB
erabot.ai

Scan any codebase. See exactly what you spend on AI. Get one-click fixes.

No spam. Only launch updates and cost optimization tips.

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