Human-approved AI workflow products

Make AI workflows reliable enough for real business operations.

Turn scattered revenue, customer, product, and operational evidence into structured, validated, reviewable outputs—before they shape delivery, engineering, product, or revenue decisions.

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Where evidence breaks down

Critical decisions often begin with scattered, conflicting inputs.

Revenue

Account context, buyer evidence, CRM fields, and commercial assumptions drift across tools.

Implementation

Commitments, requirements, risks, and owners remain unresolved when delivery begins.

Quality

Support signals and diagnostics arrive without enough evidence for accountable triage.

Product

Feedback, research, usage, and delivery context are hard to trace to a decision.

The control layer

AI prepares the work. Rules test it. A person decides.

Scattered inputsAI extraction and reconciliationDeterministic validationSource-linked reviewHuman approvalControlled output

Four workflow pillars

One reviewable operating pattern, applied to four teams.

Revenue owner review

Revenue Intelligence

Prepare source-backed account, buyer, CRM, and commercial evidence before records or decisions move forward.

Implementation review

Implementation Intelligence

Reconcile what was sold, required, and unresolved before delivery starts.

Quality decision

Quality Intelligence

Turn fragmented escalation evidence into a reviewable defect candidate.

Product decision

Product Evidence

Connect customer and product signals without automating prioritisation.

Use Cases

Inspect representative proof—not implied customer outcomes.

Services

Choose the smallest engagement that supports a sound decision.

Workflow Audit

Map the workflow, baseline, controls, and next experiment.

AI Workflow Prototype Sprint

Build and evaluate one bounded, reviewer-first workflow.

AI Workflow Advisory

Guide prioritisation, measurement, governance, and architecture.

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Relevant background

Product judgment and hands-on workflow building.

Mahadev’s verified background spans product, engineering, data platforms, merchant intelligence, onboarding, experimentation, QA, and independent AI workflow prototypes. He worked at PayPal from 2019 to 2024 and has built independently since 2024.

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Insights

Practical thinking on workflow reliability.

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Start with the operating problem

Find the smallest workflow worth making reviewable.

Bring the inputs, current rework, accountable owner, and decision that needs stronger evidence.

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