AI Process Automation
Turn your most expensive manual processes into production AI systems. Agentic AI for document processing, customer onboarding, operational reporting, and any judgment-driven workflow where a competent employee would mostly agree on the right answer.
What this service covers
- Process audit — identify and rank top 5 automation opportunities by ROI
- Architecture design — multi-agent systems, LangGraph orchestration, tool design, state management
- Production build — AI agent code, system integrations, observability layer
- Compliance and audit — full decision trail, consent records, defensible outputs
- Knowledge transfer — runbooks, monitoring dashboards, on-call documentation
- Continuous tuning — performance monitoring, prompt refinement, edge case handling
Where AI process automation pays back fastest
- Document processing at scale — KYC, contract review, claims processing, invoice extraction. 70%+ time savings on first-pass review.
- Customer onboarding workflows — multi-step processes spanning CRM, identity verification, contract generation, and initial setup.
- Operational reporting — pulling data from 5+ systems, reconciling, flagging anomalies, drafting the report. The kind of work analysts hate and get wrong when tired.
- Sales operations — lead enrichment, account research, proposal drafting, response-aware follow-up sequences.
- Compliance monitoring — flagging regulatory exceptions, audit trail generation, defensible decisions.
- Customer support deflection — Tier-1 queries that follow patterns. 60–70% handled without escalation.
Why this works (when most AI projects don't)
Most AI projects fail because nobody agreed what "working" meant before the work started. Engagements here use the ARIA Framework — Assess, Roadmap, Implement, Accelerate — with scope and milestones agreed before any line of code is written. ROI is ranked before commitment. Production is the goal, not the demo.
Read the full ARIA Framework explainer for how the four stages work and what you get at each gate.
What makes this different
24 years of architecting production systems for banking, aviation, and enterprise SaaS goes into every engagement. The hard parts of AI process automation aren't the model calls — they're the integration boundaries, the failure modes, the audit trails, the escalation logic, and the observability that separates a demo from a system that survives 2 a.m. on a Tuesday.
Past production work includes 4 days → 6 hours on banking document processing, 70% query deflection on enterprise support, and zero failed enterprise deployments on record. The work is unglamorous and structurally sound.
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Engagement structure
- Weeks 1–2 — Assess. Process inventory, data readiness, ROI ranking, risk scoring. Two-page output: top 5 opportunities prioritized.
- Week 3 — Roadmap. Scope freeze, technology selection, architecture design, milestone schedule, success criteria.
- Weeks 4–11 — Implement. Working prototype, integration with one system at a time, end-to-end pilot in shadow mode, graduated rollout.
- Week 12+ — Accelerate. Performance monitoring, continuous tuning, knowledge transfer, next opportunity selection.
Frequently asked questions
What is AI process automation, and how is it different from RPA?
AI process automation uses Agentic AI — reasoning agents that pursue goals across multiple steps, handle ambiguity, and use tools — to automate processes that traditional RPA breaks on. RPA simulates a human at a screen and snaps when interfaces change. Agentic AI uses APIs and reasoning, making it far more durable and capable of judgment-driven tasks.
Which processes benefit most from this?
Five high-ROI patterns: document processing at scale (KYC, contracts, claims), customer onboarding workflows, operational reporting that synthesizes across systems, sales operations (lead enrichment, proposal drafting, follow-up sequences), and compliance monitoring. The pattern is high-volume processes where humans currently read structured or unstructured data and make predictable decisions.
How long does an automation engagement take?
A focused, well-scoped automation of a single process typically reaches production in 4–8 weeks. A larger multi-process portfolio takes longer — but the second and third automations ship much faster than the first because shared infrastructure and patterns accumulate.
What does it cost?
Engagements vary. Indicative ranges: single-process automation from ₹3L+ build. Multi-process portfolio (3–5 processes) from ₹10L+ over 3–6 months. Enterprise scope with governance, audit, and integration across many systems from ₹25L+. Monthly run cost (AI APIs, hosting, monitoring) is typically a small fraction of build cost.
Will this work for regulated industries?
Yes — and audit trails, compliance, and explainability are built in from day one rather than bolted on. Past production experience includes banking (KYC, document review), aviation (operational workflows), and financial services. ARIA Framework explicitly accommodates regulated environments where output must be defensible.
How does the ARIA Framework apply to process automation specifically?
Assess identifies and ranks your top 5 candidate processes by ROI. Roadmap locks scope and success criteria for the chosen process before building. Implement ships the first working agent in weeks, not quarters, with observability built in. Accelerate scales what works and identifies the next opportunity. The full framework is explained in the ARIA blog post.