ONLINEsamwarren.devv0.1.0
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Most ops leaders optimize existing systems.
I design new ones.

Revenue Operations Leader, Revenue Architect, and GTM Engineer. Nearly two decades running ops across startups, unicorns, hyperscalers, and billion-dollar brands — where the job was never just managing the stack, it was building the motions that win real revenue. What this portfolio shows is what that looks like when the execution layer finally becomes AI-native.

browse_projects()read_notes.md
▸ system.statslive
225
skills
142
agents
8+
mcp sources
95%
attr. accuracy
ACCURACY · LAST 10 BATCHES
50%95%

projects.list

FIRING · PROD · DAILY
ORCHESTRATION LAYER
01 / 06

Cascade

An AI-native operations layer for revenue teams.

A conversational layer across the entire GTM stack. Classifies intent in plain English, dispatches to the right specialist, pulls live context from every connected system, and learns from every correction. Replaces the operational glue work that used to eat a full day every week.

225
SKILLS
142
AGENTS
8+
MCP SOURCES
~90 min/day
TIME RECLAIMED
▸ recent.activitylive
2h agofeatattribution pipeline — added churn winback recency rule
yesterdayfeatintel hub — shipped conditional CRM integration slide
2 days agov2news agent — scale-safe redesign, unification kickoff
4 days agofeatcascade — native SQLite FTS5 observation layer live
claude codenodepythontf-idfsqlite fts5mcp
read case study
FIRING · SHIPPED · USED WEEKLY
MCP · DECK GENERATOR
02 / 06

Customer Intelligence Hub

Quarterly Business Reviews, generated on demand.

An MCP server any CSM can talk to. Pulls live data from the customer intelligence stack, resolves the brand system, and hands back a near-complete QBR deck in under two minutes. Replaces a three-hour manual build that used to run every quarter for every enterprise customer.

3h → 90s
QBR DELIVERY
90% complete
STARTS AT
19
SLIDE TYPES
141
LOGOS MATCHED
typescriptcloudflare workerssnowflakepptxgenjsmcp
read case study
POTENTIATING · V2 · UNIFICATION IN FLIGHT
SIGNAL CHAIN · TWO SURFACES
03 / 06

Sales Intelligence Hub

Weekly digest + pre-call brief, one signal chain.

HomeDrop tells a rep what changed across their book this week. Intel Chat tells them what to do about one account right now. The unified product closes the loop between them — every digest card is one click from a seeded chat, every chat reshapes next Monday's digest.

7
DATA SOURCES
2
SURFACES UNIFIED
15 min → 90s
PRE-CALL PREP
targeting >25%
RELEVANCE LIFT
COMPONENTS
·HomeDropWeekly digest — what changed this week across your book
·Intel ChatPre-call prep — conversational brief in under two minutes
next.js 15supabasecloudflare d1pythongithub actions
read case study
FIRING · PROD · DAILY
SKILL BUNDLE · REPLACES A ROLE
04 / 06

The Marketing Ops Agent

Five skills, one operating capability.

Attribution, hygiene, campaign ops, reporting ops, and a weekly data refresh — each running on command or on a schedule, all learning from corrections. The kind of work that used to require a dedicated Marketing Operations hire, running inside Cascade as a single operating capability.

95%
ATTR. ACCURACY
20+
LEARNED RULES
54%
HYGIENE AUTO-FIX
~8 hrs
WEEKLY RECLAIM
COMPONENTS
sf-attribution
Opportunity attribution with persistent learning loop
hubspot-ops
Workflow management, UTM hygiene, MQL backfill
sf-campaign-cloner
Bulk quarterly campaign operations
sf-reports
Bulk report, dashboard, and list view updates
data-refresh
Sunday-night refresh pipeline across every tracked source
salesforce apihubspot v4claude reasoningpython
read case study
POTENTIATING · V2 · SCALE-SAFE REDESIGN
WEEKLY DIGEST · V1 → V2
05 / 06

Target Account News Agent

A per-rep weekly digest, rebuilt for scale.

Every Monday, every rep wakes up to a per-account briefing on what changed in their book the week before. v1 proved the value. v2 answered everything v1 got wrong about scale — deterministic signal extraction, per-rep filtering, Slack-native delivery, observable pipelines. The upstream half of the Sales Intelligence Hub.

per-rep
DIGESTS/WEEK
7
SIGNAL SOURCES
~5 min
MONDAY READ
all of them
V1 LESSONS ANSWERED
pythongithub actionsslackcloudflare d1openai
read case study
RELAY · OPEN · PORTABLE
PORTABLE TOOLING
06 / 06

Claude Dotfiles

The brain, in a repo.

Clone on any machine, run bootstrap.sh, and restore the full Cascade setup in under a minute. 142 agents, 41 custom skills, hook runtime, every MCP connector wired up. The foundation every other project in this portfolio runs on.

47s
BOOTSTRAP
142
AGENTS
41
CUSTOM SKILLS
bashpythonportable
read case study
▸ latest note — playbook·
Most teams start with the showy loop. That's backwards. The loops that compound are the ones that capture institutional memory first — which is almost always data hygiene and attribution.
Which Loops to Close First.
The interesting question isn't whether to build AI-native infrastructure into your revenue stack. It's where to start. Here's the prioritization I run.
read the note →
recent.activity
2h agofeatattribution pipeline — added churn winback recency rule
yesterdayfeatintel hub — shipped conditional CRM integration slide
2 days agov2news agent — scale-safe redesign, unification kickoff
4 days agofeatcascade — native SQLite FTS5 observation layer live
1 week agofeatmops agent — 20th learned attribution rule committed