SentinelRun — The AI That Reads Your Automation Outputs and Flags the Runs That Should Have Failed
Your automation ran clean. Zero errors. It also just wrote the wrong customer name to 800 records. SentinelRun uses Claude to read each execution output, compare it to what you told it to expect in plain English, and fire an alert before anyone notices the successful disaster.
Difficulty
intermediate
Category
AI Agents & RAG
Market Demand
High
Revenue Score
8/10
Platform
AI Agent
Vibe Code Friendly
No
Hackathon Score
6/10
Validated by Real Pain
— sourced from real community discussions
The most dangerous no-code AI automation run is the one that completes successfully but produces semantically wrong output — existing monitoring tools only catch crashes, leaving silent data corruption completely invisible.
What is it?
Silent automation failures — where a workflow completes without errors but produces semantically wrong output — are invisible to every existing monitoring tool because they only watch for crashes, not meaning. SentinelRun lets automation builders define expected behavior in plain English ('customer email should never be empty, order total should be greater than zero'), then Claude reads each execution output and scores whether the behavior matches. This is the semantic monitoring layer that no-code tools have never shipped. Buildable with Claude claude-3-5-haiku for cost-efficient per-run analysis, Supabase for log ingestion, and a simple Next.js dashboard — no custom ML, no model training, ships in three weeks.
Why now?
Claude claude-3-5-haiku dropped to sub-cent per 1k token pricing in late 2024 making per-execution semantic scoring economically viable for the first time — previously this required expensive GPT-4 and the math never worked.
- ▸Plain-English behavior rule builder where user types expectations like a human, not a developer (Implementation note: Claude interprets and structures rules at analysis time)
- ▸Per-execution semantic scoring via Claude haiku comparing output JSON to behavior rules
- ▸Slack and SMS alert within 3 minutes when semantic score drops below user-defined threshold
- ▸Run history timeline showing score trends per workflow so builders spot degradation before catastrophe
Target Audience
Automation builders, indie hackers, and no-code agency owners running business-critical workflows on Make, Zapier, or n8n — approximately 150,000 globally.
Example Use Case
Jordan runs 40 client workflows. He defines 'invoice total must be numeric and above zero' in plain English for a billing scenario. SentinelRun catches three executions where the total field was null due to a upstream API change — before any client invoice was sent incorrectly.
User Stories
- ▸As an automation agency owner, I want Claude to read my workflow outputs and tell me when they look semantically wrong, so that I catch silent failures before clients notice.
- ▸As a no-code builder, I want to write my monitoring rules in plain English instead of code, so that I can define complex output expectations without developer help.
- ▸As a workflow owner, I want a score trend timeline per workflow, so that I can see gradual output degradation before it becomes a catastrophic failure.
Done When
- ✓Rule creation: done when user types a plain-English rule and sees it saved as a card under the workflow within 2 seconds.
- ✓Semantic scoring: done when a test payload is ingested and a 0-100 score with a one-line reason appears on the run timeline within 15 seconds.
- ✓Alert: done when a run scores below the user's threshold and a Slack message arrives with workflow name, score, and the violated rule.
- ✓Run timeline: done when the dashboard shows a sparkline of last 30 run scores per workflow with color coding green/yellow/red.
Is it worth building?
$59/month x 100 agency builders = $5,900 MRR at month 5. Math: 2,000 outreach targets in automation communities at 5% trial, 30% paid conversion.
Unit Economics
CAC: $50 via agency outreach at 5% trial conversion. LTV: $1,416 (24 months at $59/month). Payback: 1 month. Gross margin: 83%.
Business Model
SaaS subscription
Monetization Path
Free tier monitors 3 workflows with 50 runs/month. Paid $59/month for 25 workflows, unlimited runs.
Revenue Timeline
First dollar: week 4 via agency beta upgrade. $1k MRR: month 3. $5k MRR: month 7. $10k MRR: month 11.
Estimated Monthly Cost
Claude haiku API: $35, Supabase: $25, Vercel: $20, Twilio: $15, Stripe: ~$20. Total: ~$115/month at launch.
Profit Potential
$10k MRR within 9 months targeting automation agencies with 10+ active client workflows.
Scalability
High — can expand to GPT-4o fallback, custom rule libraries, team dashboards, and white-label for agencies.
Success Metrics
Week 3: 15 workflows monitored in beta. Month 2: 30 paid accounts. Month 5: 85% monthly retention.
Launch & Validation Plan
Write a 500-word LinkedIn post titled 'The automation that almost cost me a $12k client' and collect DMs from agency owners who relate.
Customer Acquisition Strategy
First customer: DM 20 Make agency owners who have publicly posted about client workflow incidents, offer 90-day free beta in exchange for weekly feedback calls. Ongoing: LinkedIn thought leadership on automation reliability, ProductHunt launch, Make and n8n community partnerships.
What's the competition?
Competition Level
Low
Similar Products
Healthchecks.io (cron ping only, no semantic analysis), Datadog (infrastructure monitoring, not no-code workflow output), Make native history (no alerting, no semantic rules).
Competitive Advantage
Only monitoring tool that reads semantic meaning of outputs in plain English — all competitors only catch HTTP errors and execution timeouts.
Regulatory Risks
GDPR risk if execution payloads sent to Claude API contain EU personal data — must offer payload field masking before analysis. Document in privacy policy.
What's the roadmap?
Feature Roadmap
V1 (launch): webhook ingestion, Claude semantic scoring, Slack alerts, rule builder. V2 (month 2-3): score trend timeline, email digest, payload field masking for GDPR. V3 (month 4+): white-label agency dashboard, n8n self-hosted support.
Milestone Plan
Phase 1 (Week 1-2): schema, webhook ingestion, Claude scoring endpoint. Phase 2 (Week 3-4): alert wiring, rule builder UI, Stripe billing. Phase 3 (Month 2): 30 paid accounts, GDPR payload masking shipped.
How do you build it?
Tech Stack
Next.js, Claude claude-3-5-haiku API, Supabase, Twilio, Stripe — build with Cursor for semantic analysis pipeline, v0 for alert dashboard
Suggested Frameworks
LangChain for prompt chaining, Claude claude-3-5-haiku for semantic scoring, Next.js App Router for API routes
Time to Ship
3 weeks
Required Skills
Claude API, Next.js, Supabase, webhook ingestion, prompt engineering.
Resources
Claude API docs, LangChain docs, Supabase Realtime, Twilio SMS docs.
MVP Scope
app/page.tsx (landing), app/dashboard/page.tsx (workflow list with scores), app/api/ingest/route.ts (webhook execution payload receiver), app/api/score/route.ts (Claude semantic scoring), lib/db/schema.ts (Drizzle schema), lib/claude.ts (scoring prompt), lib/alerts.ts (Slack and SMS), components/RunTimeline.tsx (score history), components/RuleBuilder.tsx (plain-English rule input), seed.ts (demo workflows and runs), .env.example.
Core User Journey
Sign up -> connect workflow via webhook -> write plain-English rules -> first run scored -> receive Slack alert on anomaly.
Architecture Pattern
Workflow fires webhook on execution -> Supabase ingests payload -> scoring job calls Claude with payload plus user-defined rules -> semantic score returned -> if below threshold Slack and SMS fires -> score stored -> dashboard timeline updates.
Data Model
User has many Workflows. Workflow has many BehaviorRules and ExecutionRuns. ExecutionRun has one SemanticScore. SemanticScore below threshold creates Alert.
Integration Points
Claude claude-3-5-haiku for semantic scoring, Stripe for payments, Twilio for SMS, Slack API for alerts, Supabase for storage, Vercel for hosting.
V1 Scope Boundaries
V1 excludes: GPT-4o integration, custom scoring model training, mobile app, team accounts, white-label, n8n self-hosted agent.
Success Definition
A paying agency owner catches a real silent failure via semantic alert, prevents a client escalation, and posts about it publicly crediting the product.
Challenges
The hardest problem is convincing builders to trust AI-scored monitoring — false positives erode trust fast, so the semantic scoring prompt must be extremely conservative with a high confidence threshold before firing alerts.
Avoid These Pitfalls
Do not build custom ML models — Claude haiku is cheap and accurate enough for structured output comparison. Do not allow real-time scoring on high-frequency workflows without rate limiting or costs will spike. First 10 paying customers need manual onboarding to write effective rules — budget those calls.
Security Requirements
Supabase Auth with magic link. RLS on all tables scoped to user ID. Payload field masking before Claude API call for PII. Rate limit scoring endpoint to 60 calls/minute per user.
Infrastructure Plan
Vercel for frontend and API. Supabase for Postgres and auth. Sentry for error tracking. GitHub Actions CI. Total ~$115/month.
Performance Targets
300 DAU at launch, 5,000 scoring calls/day. Claude API call under 3s. Dashboard load under 2s. Supabase Realtime for live score updates.
Go-Live Checklist
- ☐Security audit complete.
- ☐Payment flow tested end-to-end.
- ☐Sentry live and catching errors.
- ☐Claude scoring tested with real Make payloads.
- ☐Custom domain with SSL.
- ☐Privacy policy with payload handling terms published.
- ☐5 agency beta users signed off.
- ☐Rollback plan documented.
- ☐Launch post drafted for Make and LinkedIn communities.
First Run Experience
On first run: two demo workflows pre-seeded with 14 days of scored runs including one red-flagged anomaly with Claude's explanation visible. User can immediately read the anomaly reason and inspect the violated rule. No manual config required: demo scoring runs against pre-stored payloads without a real Make account.
How to build it, step by step
1. Define Drizzle schema: Workflow, BehaviorRule, ExecutionRun, SemanticScore, Alert tables. 2. Run npx create-next-app with Tailwind and App Router. 3. Set up Supabase with RLS per user and edge function for webhook ingestion. 4. Write Claude haiku scoring prompt in lib/claude.ts that receives output JSON plus rule strings and returns a 0-100 confidence score with reason. 5. Build /api/score route that calls Claude and stores result. 6. Wire Slack webhook and Twilio in lib/alerts.ts to fire when score drops below user threshold. 7. Build RuleBuilder component in plain text input using v0. 8. Build RunTimeline showing score trend as sparkline per workflow. 9. Add Stripe billing gating beyond 3 free workflows. 10. Verify: ingest a test payload, confirm Claude scores it, confirm Slack alert fires for a low-score run end-to-end.
Generated
June 3, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.