AI workflow automation for complex B2B operations

Use Cases

Your team has the information. It is just scattered across the workflow.

I build human-approved AI workflow systems that turn fragmented evidence into validated, reviewable outputs—before it creates revenue leakage, delivery rework, engineering churn, or weak product decisions.

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Revenue Intelligence · Representative workflow proof

Make CRM and account context reliable before revenue teams act.

CRM records, calls, buyer research, and commercial context conflict or remain incomplete before qualification, forecasting, or outreach decisions.

Owning team: Revenue Operations

Inputs

Approved CRM fields, first-party call evidence, authoritative company sources, and verified buyer sources

AI-assisted work

Extract account facts, reconcile recent context, and prepare candidate CRM changes with source links

Validation

Required-field, freshness, allowed-value, duplicate, and cross-source conflict checks

Reviewer decision

Revenue Operations lead approves, returns, or rejects

Controlled output

Reviewed account brief and approval-ready CRM change set

Implementation Intelligence · Representative workflow proof

Catch delivery-critical ambiguity before customer kickoff.

Commitments, requirements, dependencies, security needs, owners, and open questions remain scattered when delivery begins.

Owning team: Implementation and Professional Services

Inputs

CRM commitments, executed agreements, discovery evidence, solution notes, and approved requirements

AI-assisted work

Extract commitments and dependencies, reconcile handoff evidence, and surface gaps or ambiguity

Validation

Required-input, ownership, conflict, dependency, and readiness checks

Reviewer decision

Implementation manager approves, returns, or rejects

Controlled output

Approved implementation-readiness baseline and controlled handoff package

Quality Intelligence · Representative workflow proof

Give engineering evidence—not another ticket history.

Support reports, diagnostics, duplicates, environment details, and release context are fragmented before engineering triage.

Owning team: Quality Engineering and Support

Inputs

Support tickets, system logs, diagnostics, environment details, and release records

AI-assisted work

Extract reproducible evidence, reconcile duplicates, and prepare defect candidates for inspection

Validation

Reproduction-field, environment, duplicate, release, and evidence-coverage checks

Reviewer decision

Quality Engineering lead approves, returns, or rejects

Controlled output

Approved engineering-ready defect candidate or documented return decision

Product Evidence · Representative workflow proof

Turn customer signals into product evidence your team can inspect.

Feedback, interviews, usage signals, commercial context, and delivery evidence are difficult to reconcile without losing provenance.

Owning team: Product and Product Operations

Inputs

Customer feedback, research, usage signals, support evidence, and delivery context

AI-assisted work

Extract themes, connect claims to sources, and reconcile supporting and conflicting signals

Validation

Source-coverage, freshness, segment, duplicate, and contradiction checks

Reviewer decision

Product manager approves, returns, or rejects

Controlled output

Source-linked discovery brief with an explicit reviewer decision

One shared control model

AI prepares the evidence. Rules and accountable people control the decision.

Scattered inputsAI extraction and reconciliationDeterministic validationSource-linked reviewHuman approvalControlled output

Pilot measurement

Every workflow starts with a measurable operating baseline.

Baseline → prototype → pilot measurement → scale, refine, or stop

Revenue

CRM completeness, review time, qualification speed, prevented incorrect writeback

Implementation

Late gaps, remediation effort, review time

Quality

Triage time, returned candidates, accepted findings

Product

Evidence coverage, review time, accepted themes

Selected portfolio builds

Inspect how the control patterns work in practice.

Independent portfolio build

MAWI

Approval-gated actions, policy checks, auditable decisions, and deterministic fallback.

Inspect MAWI

Portfolio prototype

CRM Hygiene

Source-backed CRM change packages prepared for Revenue Operations review.

Inspect CRM Hygiene

Representative demo

iQuote

AI-assisted extraction with deterministic commercial rules and human approval.

Inspect iQuote

Next step

Want to get started?

Book a Discovery Call to discuss one operational workflow, the friction it creates today, and the most useful next step.

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