Shashank Shekhar
Open to EU/Remote
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Independent2025 – Present

AI-Integrated Automation Services

Automation services for PDF/text extraction into structured JSON with validation, deterministic post-processing, and fallback paths.

Problem: Product teams needed dependable AI workflow automation without brittle prompts or costly retry loops.

Outcome: Implemented FastAPI-based automation services with deterministic validation, reduced extraction errors, and stable Docker deployment patterns.

AIWorkflowAutomation

Problem

Customers needed accurate data extraction from unstructured documents without fragile LLM pipelines or runaway costs.

Solution / Approach

Built a structured extraction pipeline using schema validation, deterministic post-processing, and fallback logic. Experimented with self-hosted Ollama to balance privacy, latency, and cost.

Architecture

  • Extraction services with per-schema validation rules.
  • Deterministic post-processing and error handling.
  • Observability and cost tracking to keep runs predictable.

Key Features

  • PDF/text to JSON structured extraction.
  • Validation pipelines with retries and deterministic fallbacks.
  • Cost/latency monitoring dashboards.

What I Learned

Reliable AI automation depends on deterministic steps around the model, not just prompt quality.

Tech stack

PythonFastAPIDockerOllama