Automated Workflow Discovery and AI Rating at Scale
Teams manually scouting automation templates faced high discovery time and inconsistent quality assessment. We built a durable pipeline that discovers workflows daily, scores them with AI, archives raw artifacts, and indexes everything for semantic search.
Business Impact
Replaces a full-time research analyst's daily scouting and scoring output
Business Impact
Executive Outcomes
Replaces hours of daily manual research and scouting
Hard budget cap prevents AI cost surprises
Scrape, score, archive, and search run end-to-end
Replaces a full-time research analyst's daily scouting output
The Challenge
“The team needed high-quality workflow intelligence but was spending too much manual time on discovery, evaluation, and technical retrieval. There was no structured way to assess workflow quality at scale or search historical patterns.”
Discovering and evaluating automation workflows required hours of manual scouting every day
No structured way to assess workflow quality at scale, leading to inconsistent recommendations
Historical workflows were not archived or searchable, making pattern discovery across projects impossible
LLM scoring costs were unpredictable with no per-run, daily, or monthly budget controls
External API failures during long-running batch jobs could lose hours of processing with no recovery path
The Transformation
What changed after we built the system
Discovering and evaluating automation workflows required hours of manual scouting every day
Automated three-phase pipeline scrapes 250 workflows daily on a predictable morning schedule
No structured way to assess workflow quality at scale, leading to inconsistent recommendations
AI-powered quality scoring with structured JSON validation produces consistent evaluations at scale
Historical workflows were not archived or searchable, making pattern discovery across projects impossible
Raw JSON artifacts archived in Google Drive and semantically indexed in Qdrant for instant search
LLM scoring costs were unpredictable with no per-run, daily, or monthly budget controls
Three-tier cost caps ($0.10 per workflow, $10 per day, $100 per month) enforce total budget predictability
External API failures during long-running batch jobs could lose hours of processing with no recovery path
Task composition architecture with metadata tracking and fault isolation enables clean recovery from failures
Why three-tier cost caps changed the economics
LLM-based evaluation is powerful but expensive at scale. Scoring 250 workflows daily with no guardrails could easily produce surprise bills if model pricing changes or output volume spikes.
The first cap is per-workflow: $0.10 maximum. If a single evaluation exceeds that, the run terminates and the workflow gets flagged for manual review. This prevents any one item from burning through budget.
The second and third caps are daily ($10) and monthly ($100). When either limit is reached, remaining workflows queue for the next period. This makes the pipeline's cost completely predictable. The team knows exactly what the maximum bill will be before the month starts.
How We Built It
Technical architecture for the curious
Scraping
Morning scrape pipeline with proxy rotation and explicit status tracking for quota exhaustion and missing resources.
AI Rating
Structured scoring bounded at $0.10 per workflow. Validation with repair handles inconsistent model output.
Archive
Every workflow's raw JSON is stored in Drive. Backfill pipeline processes up to 500 artifacts daily.
Search
Summary and technical vector collections enable semantic search. Full and incremental reindex keep the index current.
Operations
Sheets as operational record for non-engineering stakeholders. Trigger.dev middleware for singleton service initialization.
Engineering Decisions
Tradeoffs we made and why
Three-tier cost caps instead of uncapped LLM scoring
Benefit
Budget predictability at per-run ($0.10), daily ($10), and monthly ($100) levels with no surprises
Cost
Some high-value workflows may not get scored if the daily cap is reached early
Proxy rotation with direct-connection fallback
Benefit
Maintains scraping throughput when the primary proxy is rate-limited or down
Cost
Direct connections are more visible and more likely to be blocked by target sites
Google Sheets as the operational record instead of a database dashboard
Benefit
Non-engineering stakeholders can monitor pipeline health without any database access
Cost
Sheet size limits and slower performance compared to a dedicated monitoring tool at high volume
Scheduled three-phase pipeline instead of event-driven processing
Benefit
Predictable resource usage, clear phase boundaries, and independent failure domains
Cost
Fixed schedule cannot respond to real-time content spikes or priority changes during the day
Certain client names, proprietary workflows, screenshots, and internal assets referenced in this case study are protected under a non-disclosure agreement and have been anonymized or omitted to comply with our confidentiality obligations.
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