AI automation role fit

Backend-owned AI workflows, not loose automation glue.

My strongest fit is AI automation work where business data, documents, call audio, transcripts, tickets, or CRM leads need to become a reliable workflow: backend-owned state, RAG/context retrieval, structured analysis, approval, CRM/API handoff, tests, logs, docs, and a supportable runbook.

I am not positioning as a pure ML researcher or no-code-only builder. The useful signal is that I keep workflow state, RAG quality, privacy boundaries, adapter contracts, retries, audit, and verification in the backend, then use tools like n8n and Telegram for orchestration and operator control.

DriveDesk proof route AI Ops proof status Reviewer acceptance Skill evidence GitHub-readable version
AI Automation RAG FastAPI PostgreSQL/pgvector Structured JSON Telegram Approval CRM/API Handoff Docker/CI

Backend-Owned State

Documents, transcripts, leads, approvals, audit notes, retries, and handoff records live in backend-owned state instead of hidden workflow nodes.

RAG And Structured Output

Relevant context is retrieved with source discipline, then AI output becomes summaries, risks, next steps, lead score, missing info, and draft actions.

Human Approval

Risky actions stay reviewable through approval states, Telegram payloads, operator visibility, and an auditable accept/reject path.

CRM / API Integration

Approved results cross system boundaries through adapter contracts, field mapping, validation, idempotency, retries, logs, and dead-letter thinking.

Production Proof

Docker Compose, CI, deterministic tests, smoke checks, docs, cost notes, and runbooks make the workflow reviewable beyond screenshots.

AI-Assisted Discipline

AI accelerates research, implementation variants, docs, tests, and debugging. I still own architecture, verification, deployment, logs, and shipped quality.

Workflow Slice I Can Own

  1. Normalize documents, transcripts, tickets, leads, or webhook payloads into backend-owned records.
  2. Retrieve context through PostgreSQL/pgvector-backed storage or a compatible vector layer.
  3. Produce structured summaries, risk flags, scoring, routing reasons, and draft actions.
  4. Keep human review explicit through approval states, audit notes, Telegram payloads, and operator handoff.
  5. Push approved results into CRM/API/webhook boundaries with mapping, validation, retries, and logs.
  6. Add deterministic tests, smoke checks, Docker/CI paths, docs, and a short runbook for support.

Proof Review Order

  1. Open DriveDesk AI Operator proof route for the product-level workflow.
  2. Open AI Ops public proof status for current RAG, approval, CRM-safe handoff, and CI evidence.
  3. Open reviewer acceptance for exact technical acceptance coverage.
  4. Open Skill Evidence for the full skill-to-proof map.
  5. Open DriveDesk Core for backend/platform foundation.

Strong First Slice

Give me one real workflow with one input source, one context source, one useful AI analysis output, one approval or review point, one CRM/API/webhook handoff, and one success metric. That is enough to produce a working first version with tests, docs, and a clear next iteration path.

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