Start with the workflow, not the agent.
A useful automation begins with a repeated task, a measurable cost, and a clear definition of a correct result. I map the steps people follow today, the data they need, the tools they use, and what happens when something fails. Some steps need an LLM; others need an API integration, a scheduled job, or a better validation rule.
This is a good fit when your team copies information between systems, reviews documents manually, assembles recurring reports, or maintains a prototype that needs constant intervention. The aim is a dependable process with visible operating cost.
What I can automate.
I connect AI components to the application and operational systems around them. Deterministic code handles permissions, retries, validation, and irreversible actions. Model output is checked before it changes a record or becomes a customer-facing answer.
- Document and PDF extraction with field validation and source checks
- CRM, inbox, and internal API workflows with explicit access boundaries
- AI assistants and agents with human approval for expensive actions
- Scheduled collection, data preparation, and reporting pipelines
- Model routing, caching, and API-cost controls for existing AI workflows
What the engagement delivers.
We begin with a scoped audit: workflow map, feasibility assessment, integration boundaries, expected operating costs, and an implementation plan. Before building, we agree which inputs must work, which failures need a human, and what evidence will count as a successful handover.
Implementation includes the workflow, its application interfaces, tests for representative cases, and deployment documentation. I make failure states visible and document how to retry, stop, or roll back the process. Your team should understand what it owns after the engagement.
- A ranked opportunity map and a bounded first implementation
- Acceptance checks covering normal inputs and known failure cases
- Runtime cost and reliability considerations
- Documentation for operations, credentials, and handover
Evidence behind the service.
My public market-data automation tracks 365 listed companies through scheduled collection and versioned outputs. My document-intelligence case study describes extraction within a regulated onboarding workflow, where validation and deterministic boundaries matter as much as model capability.
Confidential client systems stay confidential. Case studies separate what can be inspected publicly from what cannot be shared, and explain the constraints around each result.
Hiring a freelance automation engineer.
I work from Lalitpur in the Kathmandu Valley and collaborate remotely with teams in Nepal and internationally. Send the workflow, current tools, approximate volume, deadline, and the cost of a wrong result. Include sample data only when it is safe to share; do not send credentials or customer records in the project brief.
Pricing and delivery depend on access, integrations, data quality, and acceptance requirements. A focused audit defines that scope before a build commitment. If your primary problem is answering questions over private documents, explore my RAG development service as well.