Overview
Every AI agent system running in production has the same problem: no one knows what the cron jobs actually cost until the bill arrives.
jobaudit (branded as LoomLens Advisor) is the first tool that solves this systematically. It reads every scheduled job in an AI agent system, estimates the real cost of each job based on the model being used, compares each job against the optimal model for its workload, and reports exactly how much would be saved by making changes.
The revenue model is what makes it different: Signallloom earns 25% of the first three months’ savings. No savings found = no fee. We’re betting on ourselves.
The Problem: Every AI Agent System Is Overpaying
Here’s what the average AI infrastructure setup looks like six months after launch:
- 12 cron jobs running AI tasks
- 8 of them are using GPT-4o or Claude Opus
- 6 of those 8 don’t need that capability — they’re running classification, routing, or monitoring
- 2 are failing silently with timeouts, burning budget for nothing
- Monthly AI bill: $2,400
No one noticed. There’s no dashboard. No one is auditing. The API just charges the credit card.
The jobs weren’t intentionally over-specced. They were built by developers who used the most capable model they knew, because capability is easy to measure and cost is not. jobaudit closes that tooling gap.
How It Works
The Audit Flow
Step 1: Snapshot — jobaudit reads the current job definitions from the agent system.
Step 2: Cost Estimation — For each job, jobaudit estimates token count, model cost per run, runs per day, and daily/monthly/annual burn rate.
Step 3: Model Comparison — Each job is compared against the optimal model for its workload. A job running Opus 4.6 for simple classification is flagged and compared to Haiku.
Step 4: Report — A full report with per-job cost breakdown, optimization recommendations, and projected savings.
Real Savings Example
A system audit found:
- 11 jobs running on production
- 1 job over-specified (Sonnet 4 for a task Haiku handles)
- 3 jobs failing with timeouts (wrong timeout configuration)
- Optimized burn rate: $0.0769/day = $28/month
Monthly savings vs. before optimization: ~$2.46/day × 30 = ~$74/month. Signallloom earns 25% of first 3 months = $55.50 one-time. After that, the client keeps 100% of savings.
Developer Referral Economics
| Metric | Value |
|---|---|
| Referrer’s system: 50 jobs | $183.60/month potential savings |
| Signallloom share (25%, 3 months) | $137.70 one-time |
| Client keeps (after 3 months) | $137.70/month recurring |
| Referral bonus to referrer (10%) | $13.77/month for referred client |
The referral loop compounds: every client who saves money becomes a referral source.
Use Cases
The Surprise Bill Audit
A developer wakes up to a $4,000/month AI bill. They run jobaudit and discover 40% of their jobs use Opus 4.6 where Sonnet 4 or Haiku would suffice. Optimization plan: $1,600/month savings.
The Silent Failure Catch
jobaudit flags 2 jobs that are failing with timeouts — burning budget on failed API calls with no output. Fix: adjust timeout configuration + downgrade model. $180/month recovered.
The Monday Morning Ritual
A team runs jobaudit every Monday morning as part of their operational review. Each week: a fresh efficiency report, a prioritized optimization list, and a clear dollar amount for what changing each job would save.
Revenue Model
jobaudit is free to run. If Signallloom identifies savings and you approve the optimizations, we earn 25% of the first 3 months’ savings. After that, you keep 100% of the ongoing savings.
If no savings are found: you pay nothing.
Part of the Signallloom Developer Toolbox. Run: signalloom jobaudit audit