Build a revenue system your team can trust.

Commercial impact

Reliable operations make revenue easier to run.

Revenue friction rarely lives inside one tool. It appears when routing waits for a manual decision, records mean different things across teams and leaders cannot trace the numbers behind the forecast.

Speed

Move high-value work faster

Important leads, accounts and exceptions reach the right owner without waiting for private fixes.

Trust

Trust the numbers teams use

Stages, ownership and forecast outputs remain connected to explicit source decisions.

Learning

Turn exceptions into learning

Edge cases become visible operating evidence instead of recurring Slack conversations.

An exception queue moving to a human owner, then a resolved rule and a feedback loop. — Exceptions stop becoming private heroics and start improving the system.

Revenue control layer

Make every revenue decision visible and owned.

I follow one live workflow across records, tools and teams, then expose every transformation, decision, route and fallback. Automation handles the repeatable path. People retain ownership of policy, uncertainty and high-impact overrides.

Trace the workflow

Trace the real workflow

Follow the record from its first event to its final owner and identify missing data, hidden transformations and manual repairs.

Define the rules

Turn judgment into operating rules

Define qualification, stage progression, ownership, routing and forecast logic in language the team can review.

Customer proof

Swan replaced manual revenue work with systems that keep running.

The same control-layer approach was applied across inbound routing, CRM coverage and forecasting. Each system made the rule visible, automated the repeatable work and left an owner for exceptions.

Inbound routing

Inbound routing that doesn’t need a BDR queue

Talk to an Expert meant a human read every submission. When that stopped working, first contact blew out to ~17 hours and AEs saw everything.

I built the qualify-and-route flow and put it in production: scoring, four buckets, Slack alerts, nurture, decline, plus a maintenance guide. When a few leads slipped, I added a light human check for non-TAP. TAP stays fully automatic.

Roughly five people’s triage work sits in that system. Top leads move in under 10 minutes. Manual BDR review is near zero. Post-launch, the under-2h SLA held ~99% of the time.

17h → under 10 minutes on hot inbound
SLA under 2h hit ~99% after launch
Sample: 240 in → 38 routed across four buckets

Four-bucket inbound routing

Own uncertainty

Give every exception an owner

Add review queues, fallbacks and service levels so uncertainty remains visible.

Improve the system

Measure the outcome, not the automation

Track time to action, routing quality, data completeness, conversion and forecast stability.

A revenue workflow moving through normalize, decide, route and measure, with feedback to the first stage. — Normalize, decide, route and measure inside one maintainable loop.

Your first live system

Prove the control layer on one live workflow.

The first cycle stays deliberately narrow. We choose a workflow that matters, make its decisions explicit, put the system into production and use real exceptions to improve it.

Trace
Follow the current reality
Follow the record, tools, owners and current failure points.
Define
Set the operating rules
Agree on required evidence, decisions, fallbacks and review boundaries.
Ship
Put the control layer live
Launch the automation, exception queue, monitoring and operating views.
Improve
Return evidence to the rules
Use outcomes and edge cases to update the rules.

RevOps / GTM: Owns the operating policy, expected outcomes and system priorities.
Workflow owners: Review exceptions, validate sensitive decisions and return field evidence.
Lever: Designs the control layer, operates the first cycle and documents the handover.

What you own

Leave with a revenue system your team can keep running.

This is not another automation diagram. I build the operating layer, prove it against live records and transfer a system your team can inspect, maintain and improve.

Design

Design the control layer

A documented data contract, decision rules, ownership model, source lineage, exception paths and success criteria.

Operate

Prove it live

A working workflow, review queue, monitoring views and operating evidence tested against real records.

Transfer

Transfer ownership

Architecture, field dictionary, rulebook, failure modes, QA checklist and maintenance guide owned by RevOps.