Python backend & AI workflow services

Business workflows turned into owned backend systems.

I build remote-capable Python/backend automation, AI workflow, integration, and internal-tool systems for businesses that need a working system, not a demo shell, with Docker/CI handoff support when delivery needs it. The strongest fit is one workflow with a clear success condition, real system boundaries, and enough context to prove the first responsible slice.

Open LinkedIn Services Business scenario replay Message on LinkedIn Fixed-scope offers Decision-ready contact Inbound brief Start conversation DriveDesk work sample PDF after context

Best Immediate Starts

AI workflow automation, CRM/ERP/API adapter, backend automation and integration slice, Docker/CI recovery sprint, or DriveDesk AI Operator-style work sample.

Good Fit Filter

Best-fit request shape: backend-owned state, data sources, integration boundaries, approval flow, deployment/recovery path, or AI/RAG step that can be proven with logs, tests, docs, and a handoff.

First Reply Promise

Send one remote role or one workflow plus one success condition. I will answer with a fit read, risky assumptions, the smallest responsible first slice, the work sample to inspect before committing, and whether the next step is a role conversation, fixed-scope project, technical review, or no-fit.

LinkedIn Services Request Filter

If LinkedIn routes the request as Web Development, Application Development, or Custom Software Development, I still treat it as a fit only when the work has backend automation and integration, AI workflow, integration, Docker/CI handoff, or internal-operations ownership. A website refresh, a student assignment, a standalone game, or a generic mobile store is not the right request unless there is a real backend/integration system to own.

Category routing note: LinkedIn Services Fit explains the public fit filter for broad LinkedIn Services labels so first requests stay focused on backend automation and integration, AI workflow, integration, data, Docker/CI handoff, and internal-operations ownership.

Decision route for LinkedIn Services: LinkedIn Services Fit -> Decision-Ready Contact -> Inbound Brief -> review path -> PDF resume after first-contact context is clear.

Not my target right now: isolated brochure/static websites, student/course assignments, standalone game clones, or generic mobile/ecommerce apps without backend, integration, or operations ownership.

Offer Router

Choose the closest trigger, then open the package and work sample that matches it. The review link shows the implementation pattern before a paid scope starts.

Fast First Message

Send one remote role or one messy workflow with a concrete success condition. I can respond fastest when the first context names the business process, systems involved, what should improve first, and what must not break.

Remote role signal

  • Role title, remote setup, stack, team surface, and hiring timeline.
  • First-month ownership: the workflow, integration, internal tool, or Docker/CI handoff problem that should improve.
  • Best review path: DriveDesk AI Operator work sample.

Project signal

  • Business process, tools, data sources, APIs, documents, transcripts, or CRM objects involved.
  • One success condition, access constraints, deadline or timebox, and handoff depth.
  • Useful first outcome: working slice with tests, logs, docs, and handoff.

Proposal-Ready LinkedIn Services Request

If LinkedIn Services asks for a short project request, send the current workflow, systems involved, one observable success condition, access limits, deadline or budget range, and what must not break.

Fast proof to reference first: AI Ops Business Scenario Replay shows the shape of a backend-owned workflow before a paid scope starts.

What to include

  • Current workflow or system: what happens today and where it breaks.
  • Inputs and systems: documents, calls, transcripts, CRM/ERP/1C/banking/API, spreadsheets, chats, or code.
  • Success condition: one result that would make the first slice useful.
  • Constraints: access limits, hosting, language, deadline, budget range, and what must not break.

What I can send back

  • Fit read against backend automation and integration, AI workflow, integration, Docker/CI handoff, or internal-operations ownership.
  • Smallest responsible working slice.
  • Risky assumptions and missing information.
  • Decision-Ready Contact route, then review path: repo, demo, CI, runbook, or public artifact to inspect before deciding.
  • Scope boundary for a paid teardown, MVP, integration adapter, or recovery sprint.

DriveDesk AI Operator Pilot Slice

Sales/support workflow where documents, call audio, transcripts, or CRM leads become RAG-backed analysis, lead scoring, follow-up drafts, Telegram approval, and CRM-safe handoff.

AI Workflow / RAG MVP

Document, transcript, ticket, lead, order, support, knowledge-base, or approval workflow with retrieval, scoring, routing, operator review, tests, and runbook.

CRM / ERP / API Adapter

Explicit contracts, mapping, validation, retries, idempotency, logs, rollout notes, and smoke checks across CRM, ERP, 1C, banking, accounting, or custom APIs.

Internal Operations Platform

FastAPI/PostgreSQL backend, records, roles, audit trail, tasks, notifications, admin workflows, Docker deploy path, health checks, and handoff docs.

Docker/CI Recovery Sprint

Docker/CI gates, logs, health checks, backup/restore, smoke tests, rollback path, and operator runbook for self-hosted services that must stay operable.

Workflow Teardown + Working Slice

Risk map, data model, integration boundary, first working path, verification plan, and next implementation shape when requirements are still messy.

Reviewable Handoff

What I build toward

  • Code, tests, logs, smoke checks, docs, and runbook.
  • Backend-owned state, audit, retries, idempotency, and adapter contracts.
  • Clear boundary between AI output, human approval, and business-system action.
  • Enough evidence that another reviewer can inspect the work.

Useful first context

  • Business process, system links, data sources, and access constraints.
  • One practical success condition and what must not break.
  • Timeline, hosting, stack, language, compliance, and handoff depth.
  • Existing docs, exports, screenshots, API notes, or repository links if safe to share.

Review Routes