Enterprise AI Architect · Available for select engagements

I build AI agents that think before they act.

I architect and ship production-grade autonomous agents for enterprise operations across finance, legal, procurement, healthcare, and government — systems that reason openly, degrade gracefully, and survive contact with the real world.

8 live
Production products
3
Enterprise sectors
5-stage
Agent pipeline (OpenAgent)
~4kL
Open-source reference code
// signature architecture — typed, inspectable, recoverable PIPELINE ONLINE
01🧠Intentstr → schema
02Ambiguityknown unknowns
03🕸️Clarifierasks humans last
04🗺️Plannerverifiable DAG
05Executortrace to goal
Finance & Accounting AI
Voice AI Systems
Legal AI
Procurement Automation
Healthcare AI
RAG Architecture
Enterprise LLM Integration
Autonomous Agents
Finance & Accounting AI
Voice AI Systems
Legal AI
Procurement Automation
Healthcare AI
RAG Architecture
Enterprise LLM Integration
Autonomous Agents
Souvik Roy — Enterprise AI Architect Open to new engagements
The architect

Souvik Roy

Enterprise AI Architect · Creator of OpenAgent

I design and ship production-grade autonomous agents — and the open infrastructure beneath them. My work runs live across finance, accounting, payroll, lending, procurement, legal, healthcare, and D2C operations.

The throughline is a conviction the industry is only now catching up to: agents should reason in the open, fail where you can see it, and survive contact with the real world. That philosophy is codified in OpenAgent, the open reference pipeline I author under OpenGraph.tech.

Problems solved

Most enterprise agents die in one of five places. I build for all five.

Pilot AI looks magical in a demo and collapses in production. The failures are predictable — and each one has an engineering answer, not a prompt-tweak.

01 — INTENT
A blurry request becomes a confident wrong answer.
"Make this better" is a vibe, not a spec

I extract a typed goal with constraints, success criteria, and alternative interpretations before a single token of work is generated.

02 — AMBIGUITY
A polished artifact nobody actually asked for.
Under-specified scope, audience, format

An epistemic-humility layer audits each request along fixed dimensions and flags the known unknowns by severity — a decision gate, not a gut feel.

03 — CLARIFIER
The agent turns into an annoying questionnaire.
Seven questions, user abandons

The clarifier searches the web first, auto-resolves what it can, and spends user attention only on what's genuinely unknowable. One question, not seven.

04 — PLANNER
A brittle monolith you can't resume or verify.
Five paragraphs into the wrong answer

Intent becomes a dependency-aware DAG of numbered, independently verifiable steps — auditable and editable before anything runs.

05 — EXECUTOR
Execution loses the thread and can't prove it hit the goal.
Technically correct, goal-irrelevant

Steps run in dependency order with streamed output and a final trace-to-goal pass that maps the deliverable back to the original intent.

∞ — RESILIENCE
One missing dependency takes the whole system down.
No search? No cache? No RAG?

Systems degrade, never die: missing keys fall back to in-memory and local paths. Graceful degradation is a feature, not an error.

The flagship — OpenAgent

An open reference pipeline the whole industry can read in an afternoon.

I author and ship OpenAgent under OpenGraph.tech — the open standard for business agents that reason openly, not opaquely. Five typed specialists, one streaming pipeline, ~4,000 lines of code you can actually audit.

"Each stage has a typed input and a typed output. The schema between any two stages is your test surface — and your debug trail."
Python 3.10+ FastAPI · async Pydantic typed contracts Model-agnostic MIT licensed
01🧠
Intent
Turn fuzz into a typed goal
str → IntentSchema
02
Ambiguity
Flag the known unknowns
→ AmbiguityReport
03🕸️
Clarifier
Auto-resolve, ask only the rest
→ ClarifiedIntent
04🗺️
Planner
A DAG of verifiable steps
→ ExecutionPlan
05
Executor
Run, stream, trace to goal
→ ExecutionResult
Selected work

Shipped products, not slideware.

Live revenue products, enterprise demos, and open research — each one a different slice of the same discipline: agents that hold up under real operational load.

● LiveTax & Finance

PlanMyTax.ai

A production AI platform that turns tax planning and financial decisions into guided, agent-driven workflows — reasoning over real financial context in real time.

Visit product
● LiveConstruction & Procurement

livetenders.ai

An AI system for the tendering and procurement lifecycle — surfacing, parsing, and acting on live tender opportunities at the speed enterprise bid teams actually need.

Visit product
● LiveD2C · E-commerce Operations

Autonomous Operations for D2C

An end-to-end autonomous operations layer for direct-to-consumer brands — agents that run the day-to-day of the business, coordinating tasks across the stack so the team can stop firefighting and start scaling.

Watch it run
● LiveAccounting / Finance

AI Accountant

An autonomous accounting agent that books, categorizes, and reconciles with audit-ready, defensible output.

Watch demo
● LiveHR & Payroll

AI Payroll

A payroll agent that handles the full run — calculation, compliance, and edge cases — without the monthly scramble.

Watch demo
● LiveField & Home Services

AI Plumber

A vertical agent for the trades — taking inbound jobs from intent to booked, dispatched, and followed-up, fully autonomously.

Watch demo
● LiveFinance / Lending

Credit Analyst AI

An agent that performs structured credit analysis — assembling, reasoning, and defending a lending view.

Watch demo
● LiveFinance / Operations

AI Payment Reconciliation

Autonomous matching and reconciliation that collapses a manual finance-ops grind into a verifiable pass.

Watch demo
▸ DemoEnterprise Procurement

Voice-to-Procurement Stack

A voice-driven agent that takes spoken intent straight into structured procurement actions.

Watch demo
◆ ResearchBehavioural Modeling · ML

Large Behavioural Model

Open research applying large behavioural modeling to predict performance outcomes — the deeper ML foundation under the applied work.

View repository
◆ Open SourceAgent Infrastructure · OpenGraph.tech

OpenAgent — the open standard for business agents

The five-stage reference pipeline behind everything above: Intent → Ambiguity → Clarifier → Planner → Executor. Typed contracts at every boundary, graceful degradation by design, ~4k readable lines. The thing I run, ship against, and learn from.

Read the codebase
Capabilities

The full stack of an enterprise agent.

From the model boundary to the boardroom outcome — I own the whole reasoning chain end to end.

/01

Autonomous AI Agents

Multi-stage agents that plan, act, and verify — built to ship, not to demo.

/02

Voice AI Systems

Spoken-intent pipelines that turn natural language into structured enterprise actions.

/03

Enterprise LLM Integration

Model-agnostic integration into real operational systems, security, and workflows.

/04

RAG Architecture

Retrieval that fans out in parallel and grounds reasoning in your real knowledge base.

/05

Finance & Accounting AI

Reconciliation, credit, tax, and lending agents with auditable, defensible output.

/06

Healthcare & Legal AI

High-stakes domains where epistemic humility and traceability are non-negotiable.

/07

Procurement Automation

Voice-to-procurement and tendering agents across the full bid lifecycle.

/08

Agent Architecture & Strategy

Designing the typed, inspectable systems that make the rest of it possible.

Writing & thought leadership

Field notes from production.

The frameworks I publish are the same ones I ship. No theory I haven't run in anger.

Let's talk

Let's connect to solve your AI hustle.

Bring the messy version of the problem. In one working session we'll figure out whether an agent is the right answer — and exactly which of the five stages your last attempt died in.

cal.com/auto-d2c/1-1-with-souvik