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Case study · Applied AI
Continuous, evidenced insight into what SMEs and the self-employed discuss online: taxes, social security, e-invoicing, hiring, cash flow. Manual browsing did not scale and could not answer with sources. We built a pipeline where nothing reaches the knowledge base unreviewed and every answer is cited.
The challenge
The venture needed continuous, evidenced insight into the problems SMEs and self-employed professionals discuss online. Manual browsing did not scale, left no record, and could not answer a question with sources. Any system had to be trusted by an operator before its output was trusted by a client.
Our approach
Collect, score, de-duplicate, review, then answer. Seven adapters pull posts per topic every twelve hours. One router turns each post into a structured signal scored 0 to 15 across five components and meters token cost per call. Identity, text hash and vector similarity are checked before storage, with a dead-letter queue if a check cannot run. An operator approves or rejects every signal; nothing is stored unreviewed.
The orchestration was designed around n8n Cloud's 300-second execution ceiling with chunked, paced dispatch and self-continuation, Basic Auth on every webhook, HMAC sessions, encrypted settings, and one error-handler workflow every other workflow reports to.
The solution
Results
A twelve-hour intelligence cycle across seven platforms with a human approval gate, and a chat that answers in Spanish with citations or declines. The client's operators run it themselves.
Stack
Talk to us
A senior engineer plus the relevant department lead joins the first call. No discovery gauntlet, no junior reps.