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.
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.
Four workflow pillars
One reviewable operating pattern, applied to four teams.
Revenue Intelligence
Prepare source-backed account, buyer, CRM, and commercial evidence before records or decisions move forward.
Implementation Intelligence
Reconcile what was sold, required, and unresolved before delivery starts.
Quality Intelligence
Turn fragmented escalation evidence into a reviewable defect candidate.
Product Evidence
Connect customer and product signals without automating prioritisation.
Use Cases
Inspect representative proof—not implied customer outcomes.
Revenue
Portfolio prototypes and architecture evidence.
Inspect evidenceImplementation
A guided synthetic handoff-validator demo.
Inspect evidenceQuality
A representative reviewer-first workflow brief.
Inspect evidenceProduct
A representative evidence-reconciliation workflow.
Inspect evidenceServices
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.
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.
Read the evidence-controlled backgroundInsights
Practical thinking on workflow reliability.
Workflow-first systems
Why bounded workflows matter more than broad autonomy.
Beyond AI accuracy
Measure whether outputs support real decisions.
Evidence and judgment
Keep accountable decisions grounded in context.
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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