Process Automation with Generative AI

The Problem

Many organizations know they want to use AI, but struggle to bridge the gap between business intent and operational reality. They often fail to answer:

  • Which parts of the process actually require GenAI versus deterministic algorithms?
  • How do we monitor quality in production?
  • Is the architecture scalable, cost-effective, and testable?

I turn ambiguous requirements into measurable, testable, and reliable workflows.

Core Services

1. Problem Modeling, Solution & Architecture Design

We build hybrid systems, not just prompt chains.

  • Decomposition & Suitability: Deconstructing complex workflows into atomic steps to strictly separate Generative AI (probabilistic reasoning) from Deterministic Logic (algorithms/calculations/rules).
  • Hybrid Architecture: Designing end-to-end solutions that integrate LLMs with traditional software components. This includes defining agentic workflows, RAG strategies, vector storage, and async orchestration patterns.
  • Containerized Service Design: Defining the technical specifications for deployment—including API contracts, microservice boundaries, and Docker/Kubernetes manifests—ensuring a seamless handover to your infrastructure/DevOps teams.

Outcome: A clear, documented technical blueprint where AI is used only where it adds value, not risk.

2. Evaluation, Metrics & Regression Testing

Move from "it vibes good" to "it passes the test suite."

  • Metric Definition: Translating business goals into observable signals: correctness, faithfulness, latency, and cost.
  • Test Harnesses: Constructing golden datasets and automated evaluation pipelines (using LLM-as-a-judge or heuristic rubrics) to benchmark performance before launch.
  • Regression & Safety: Implementing rigorous testing for prompt drift and model updates, ensuring that new improvements don’t break existing functionality.
  • Synthetic Dataset generation: develop synthetic datasets carefully to test the efficacy within novel problems.

Outcome: Objective confidence in the system’s reliability before it touches production data.

3. Production Readiness & Technical Supervision

Ensure the build matches the blueprint and evolves safely.

  • Implementation Guidance: Supervising engineering teams to prevent common failure modes like brittle chaining, silent hallucinations, or unnecessary token usage.
  • Reliability Patterns: Designing application-level guardrails, retry logic, and fallback strategies (e.g., reverting to rule-based systems when confidence is low).
  • Monitoring Strategy: Defining the "signals" that matter in production—drift detection, user feedback loops, and anomaly alerting—to support long-term maintainability.

Outcome: A deployed system that is safe, observable, and capable of evolving without breaking.

Typical Deliverables

  • Architecture & Data Flow Diagrams: End-to-end design with explicit "AI vs. Non-AI" boundaries.
  • Deployment Specifications: Docker Compose or K8s manifest files for application services.
  • Evaluation Suite: Golden datasets, scoring rubrics, and automated regression reports.
  • Risk & Fallback Strategy: Documentation on guardrails, confidence thresholds, and escalation paths.

Who This Is For

  • Teams moving from LLM prototypes to production.
  • Organizations that value predictability, trust, and engineering rigour.
  • Technical leaders who need a clear architecture to hand over to internal development/DevOps teams.

Why This Is Different

This is not "prompt engineering" or generic strategy consulting.

This is end-to-end process automation engineering, treating business modeling, evaluation, and system reliability as a single, coherent engineering discipline.


Created Dec 2025 — Updated Jan 2026