For teams on $10K+/mo LLM spend

Your Helicone dashboard shows what you’re spending.
We show why, and write the fix.

If your team is already running Helicone, Langfuse, or the raw OpenAI/Anthropic/Gemini APIs past $10K/mo, you’ve already solved observability. What you haven’t solved is what to do about it. erabot.ai scans your codebase, correlates call sites to your runtime cost data, and produces an agent-instructions.md that Claude Code or your coding agent can apply in one command.

What a 30-minute founder audit looks like

  • Share read-only Helicone / Langfuse access (or paste a 30-day export). We never touch production code — read only.
  • We run erabot against your top 20 hot-path files within the call. You see live which findings correlate to your real cost, not a hypothetical.
  • You walk away with a ranked list of the top 5 waste categories in your codebase, a projected monthly-savings range, and a draft agent-instructions.md you can hand to your engineers today.
  • No slide deck. No sales team follow-up. If you want the ongoing product, it’s self-serve. If you don’t, the findings are still yours.

Patterns we catch that dashboards don’t

  • Model-mismatch: GPT-4 calls whose prompt+context profile fits 3.5-turbo or Haiku without quality loss. Dashboards show you the bill; they don’t know the prompt structure.
  • Prompt waste: system prompts shipping redacted PII, unused few-shot examples, or full JSON schemas when a Pydantic-generated one would be 4x smaller.
  • Missing caching: deterministic lookups (classification, routing) firing the LLM on every request where a 24-hour TTL cache would eliminate 60%+ of spend.
  • Batch opportunities: N+1 LLM calls in loops that should be one multi-turn request or one batch API submission.

How we plug in

erabot does not replace Helicone — it consumes its data. As of this week, our Helicone runtime importer is live: paste a read-only Helicone API key at POST /api/scans/helicone and we project monthly savings from your real production traffic, no code upload required. Findings come back correlated to the actual models and volumes Helicone observed, not hypothetical call patterns in source.

Langfuse importer is also live (POST /api/scans/langfuse, HTTP Basic with your public+secret key pair, self-hosted host supported). If you’re on a direct provider SDK today with no runtime observability layer, we scan the repo alone and the numbers are static-analysis projections rather than runtime-measured — we label both clearly in the report.

Already have Helicone or Langfuse? Scan in 10 seconds.

If you run either observability platform, erabot pulls your last 30 days of logs and projects monthly savings against your actual traffic — no code upload, no tree-sitter, no waiting for a worker. Sign up free and paste a read-only API key in your dashboard.

# Helicone
curl -X POST https://api.erabot.ai/api/scans/helicone \
  -H "Authorization: Bearer {your_erabot_key}" \
  -F "helicone_api_key=sk-helicone-xxxxxxxx"

# Langfuse (HTTP Basic with public+secret key pair)
curl -X POST https://api.erabot.ai/api/scans/langfuse \
  -H "Authorization: Bearer {your_erabot_key}" \
  -F "langfuse_public_key=pk-lf-xxxxxxxx" \
  -F "langfuse_secret_key=sk-lf-xxxxxxxx"

Book the 30-minute audit

Direct to Rohan, founder. No SDR, no discovery-call deck. Bring your Helicone/Langfuse access or a 30-day cost export.

We don’t train on your code. Redacted before any LLM call. See /security for the full data-handling policy.