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Founder
Rohit Wakode — Founder & Director

B.Tech IIT Bombay · LLB GLC Mumbai. Building intelligent enterprise systems in India since 2014.

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Solution · 03

AI Workflow Automation

Replace repetitive operational overhead with deterministic rails plus AI judgment.

Temporaln8nSagaAudit

Pure-AI automation is unpredictable; pure-rules automation is brittle. The right answer is a hybrid: deterministic workflow engines for the parts that must be exact, AI for the judgement calls — with both fully observable.

We build idempotent, replayable workflows with saga-style compensation, so a failure never leaves your data half-changed. Every step is logged with lineage a regulator can follow.

WhatDeterministic workflow rails with AI judgement where rules run out.
Best forHigh-volume, audited back-office processes.
RunsYour cloud or on-prem; integrates with existing systems.
Time to valuePilot in 6 weeks.
01 — Capabilities

What we build.

Specific, production-grade capability — not a feature checklist.

/ 01

Hybrid deterministic + AI

Rules and code where correctness is non-negotiable; models only where judgement adds value.

/ 02

Idempotent & replayable

Every step is safe to retry; whole workflows can be replayed from history.

/ 03

Saga compensation

Multi-system transactions roll back cleanly on failure — no half-finished state.

/ 04

Document & data ingest

OCR, extraction, and validation pipelines that turn messy inputs into structured records.

/ 05

Full lineage

Per-step audit trail and dead-letter handling for exceptions.

/ 06

Human exceptions

Edge cases route to a person with full context, then feed back into the rules.

02 — How it works

From your problem to production.

01

Model the process

02

Build the rails

03

Insert judgement

04

Operate & audit

STEP 01

Model the process

Document every step, system, and exception path; agree what “done” and “failed” mean.

STEP 02

Build the rails

Implement the workflow on a durable engine (Temporal/n8n/custom) with idempotent, compensable steps.

STEP 03

Insert judgement

Add AI only at the decision points that need it, each behind validation and evals.

STEP 04

Operate & audit

Ship with dashboards, replay, and dead-letter queues; exceptions become new rules over time.

03 — Where it pays

Use cases.

KYC / onboarding pipelinesInvoice & PO matchingDocument ingestion & extractionContract review routingCustomer-ops triageCompliance reporting
04 — Engineering

Stack & standards.

Orchestration
Temporal
n8n
Custom workflow engine
Event-driven
Ingest
OCR / document AI
Schema validation
Extraction models
Reliability
Idempotency
Saga / compensation
Dead-letter queues
Replay
05 — Outcomes

What good looks like.

Exact
Where it must be
Deterministic rails for the parts that can’t be wrong.
Replayable
Nothing half-done
Idempotent steps and compensation on failure.
Traceable
Regulator-ready
Per-step lineage for every run.
06 — Questions

Answers, before you ask.

Why not just use an LLM for everything?
Because some steps must be exact and auditable. We use deterministic code there and reserve AI for genuine judgement calls, each behind validation.
What happens when a step fails midway?
Saga-style compensation rolls back completed steps so your systems never end up in an inconsistent state.
Can it handle our messy documents?
Yes — OCR plus extraction and validation turns unstructured inputs into structured, checked records, with exceptions routed to a human.
Ready when you are

Put AI Workflow Automation into production.

Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.

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