AI-native Revenue Operations for founder-led SMBs
One revenue system from first touch to renewal — built or configured for your niche, watched by AI agents that never forget to follow up.
For US founders of B2B services firms and agencies, $2M–$10M, 10–50 people — who don't trust their own pipeline number, and are done being the glue between marketing, sales, and support.
15 minutes. Fit or no fit. No deck, no discovery theater.
See exactly what the audit output looks like before you spend a dollar.
Live sample — sample data, real system. Yours would show your numbers. Click around.
Every stage has one definition, agreed in writing. That's why the number is trustable.
Built on or wired into your stack
- HubSpot
- Salesforce
- Shopify
- Stripe
- QuickBooks
- Supabase
- PostgreSQL
- Google Cloud
- Zapier
- Make
- Google Sheets
- Gmail
- Google Calendar
- Notion
- Airtable
- Intercom
- Zendesk
- Azure
- Slack
- Pipedrive
- Gorgias
You don't trust your pipeline number. You built a company anyway.
Every founder in the $2–10M bracket runs revenue on the same duct tape: disconnected tools, manual exports, and your own memory as the integration layer. The forecast is a guess wearing a dashboard. Here's what that sounds like:
“Leads sit for days.”
“Reporting takes my ops person two days.”
“We find out about churn when the cancellation email arrives.”
These aren't three problems. They're one problem: there is no system. There's a CRM nobody trusts, a support tool sales never sees, and a founder doing plumbing at the most expensive hourly rate in the company.
One system, three layers. Built in the order that stops the bleeding.
Everything starts with the Audit — two weeks, a scorecard, and a prioritized blueprint. It's the gate: the build-vs-configure decision gets made there, before a dollar goes into tools or code. Then the system goes in as a ladder:
- T1
Revenue Data Foundation
- Felt cost
- You have four tools and four versions of the truth.
- What goes in
- One system of record. Every contact, company, deal, order, and ticket — one place, one definition, clean data, tracking that actually attributes.
- The artifact
- A canonical data map and a funnel dashboard your whole team reads the same way.
- T2
Revenue Systems — build or configure
- Felt cost
- Leads leak in the handoffs — marketing to sales, sales to delivery, delivery to renewal.
- What goes in
- Pipelines, lifecycle stages, lead routing with SLAs, sequences, and the sales-to-success handoff — built custom or configured on your existing stack. (How we decide is the next section.)
- The artifact
- The routing and SLA rulebook. When a lead comes in at 9:04, you can point to what happens at 9:05.
- T3
Revenue Intelligence
- Felt cost
- You find out about problems after they've cost you money.
- What goes in
- An executive dashboard, automated weekly and monthly reporting, and AI agents watching for the things humans forget.
- The artifact
- The sample dashboard above — that one, wired to your data — plus the agent messages shown below.
The most expensive mistake isn't the tool. It's who you asked.
Ask a CRM partner agency what you need, and the answer is their platform — a HubSpot shop says HubSpot, a Salesforce shop says Salesforce. They can only configure, so they'll force-fit the tool. Ask a dev shop, and the answer is custom — they can only build, so they'll build. Both are selling their own labor.
We sell the decision. Every audit runs your business through this matrix, and either answer pays us the same — which is the only way the answer is honest.
This is the actual decision matrix we run in every audit. No thumb on the scale. Read your own row.
Most clients land in a mix — configure the funnel, build the data layer. The audit tells you which rows you're in, in writing, with the reasoning attached. And when the answer is build, the stack follows your world — Azure if you're a Microsoft shop, Google Cloud if you live in Workspace, a lean Postgres stack if you just want it to work. That's a line in the blueprint, not a default.
The follow-up you forgot at 11pm. The agent didn't.
Systems hold the data. Agents act on it. These run on triggers from your own stack and deliver into Slack, where your team already lives. The rule is simple: agents draft, humans send. No AI ever emails your customer.
- Triggeran event in your stack
- Agent draftsreply, follow-up, or brief
- Lands in Slackwhere your team lives
- Human sendsalways. no exceptions
Speed-to-lead drafter
The felt cost: “leads sit for days.”
New lead — 4 minutes ago
Dana Whitfield, VP Ops at Corvid Freight — filled out the pricing form, mentioned “consolidating our reporting across three tools.”
Draft reply is ready. It references her exact words and proposes two call times.
Stale-deal nudger
The felt cost: the deal that quietly died while everyone was busy.
Deal going cold: Harbor & Main — $14K proposal
No activity in 12 days. Last touch: you promised a revised scope “by Friday.” That was two Fridays ago.
Draft follow-up is ready — acknowledges the delay, attaches the scope.
Churn sentinel
The felt cost: “we find out about churn when the cancellation email arrives.”
Churn signal: Beacon Supply Co.
Three support tickets in 10 days, sentiment trending negative, and your champion hasn't opened the last two emails. Renewal is in 47 days.
Draft check-in email and a call agenda are ready.
Agents are only as good as what your company knows. The pricing logic in a veteran's head, the ops quirks buried in old email threads, the reason deal #40 closed — none of it lives in your CRM fields. We encode it into a context layer you own: entity-tagged, model-agnostic, portable. The model is rented. The context is yours. That's the Context Layer, priced below.
Every agent message ends the same way: a draft and a human decision. Agents draft, humans send — that's a design principle, not a disclaimer.
We sell agent pipelines. The one selling to you is ours.
We automated our own sales and marketing with the same agent pipeline we sell. Leave your email — our agents research your company, design the automations, build a working prototype, and email it to you before we've ever spoken. No call, no deck: the first thing you get from us is the work.
What happens when you press send
- 01
Research
Agents read your site and search your market — what you sell, what your stack looks like, where revenue likely leaks.
- 02
Design
They draft the automations that fit, mapped to the same ladder you've been reading: data, systems, agents.
- 03
Build
They build a working prototype of your revenue system — your company, your funnel, not a template.
- 04
Verify
Agents check their own work — schema gate, production build, end-to-end tests, live URL — before anything ships. Nothing hand-waved.
- 05
Email
The prototype lands in your inbox — usually within 24–48 hours of you pressing the button. And a human reads every reply.
Agent-built, machine-checked. The prototype, nothing else — no sequence, no newsletter.
Want to see one before you hand over an email? See a sample prototype — the one our agents built for Northlight Coffee Roasters, a fictional company we use as the public sample.
Fixed fees, scoped to your business. The first step is an audit — and you get it back.
Revenue Systems Audit
Funnel, stack, data — and context: what your company knows that your tools don't. A scorecard, a prioritized blueprint with ROI estimates, and the build-vs-configure call for your business — the matrix above, scored, in writing.
The credit mechanic, explicitly: 100% of the audit fee is credited toward any build started within 60 days. If we build, the audit was free. If we don't, you keep a blueprint you can execute with anyone — including the vendor the matrix says you actually need.
Revenue Foundation
CRM built or configured, integrations live, lifecycle stages, lead routing and SLAs, clean data, core funnel report. Tiers 1 and 2, done.
Context Layer
Owned context layer, rented intelligence. Your docs, email threads, CRM history, pricing logic, and ops quirks — encoded into one retrieval brain your agents and your team query, entity- and relationship-tagged from day one. Model-agnostic and portable: the model is rented, the asset is yours.
Graph semantic layer on the same asset, added when your data justifies it. Zero re-ingestion.
Revenue Engine
Everything in Foundation, plus the executive dashboard, automated reporting, and 3–5 production AI agents. Team training and runbooks included. The full ladder.
Retainers
- Monitor:
- dashboards maintained, monthly review, minor fixes.
- Operate:
- weekly cadence, agent tuning, context freshness, automation backlog.
- Scale:
- fractional RevOps lead — roadmap ownership, new builds, hiring support.
Deployment add-on
Run it in infrastructure you control. Your cloud account, your VPS, your keys — agents and context layer deployed where you can see them, calling rented models through your own API keys. A one-time setup fee plus a managed monthly, stacks on any retainer. Regulated industry or heavy volume? Self-hosted inference is quoted case-by-case — the first answer is VPC, a BAA, and open-weight models, not a rack in your closet.
Fixed fee, never hourly. Every engagement is scoped and quoted up front, so the number you sign is the number you pay — scope changes go by change order, nothing by surprise.
years building revenue & data systems
tools integrated across client stacks
industries served — e-commerce, publishing, education, services
weekly reporting after automation — was 2 days
No logos. No testimonials. Here's the actual work.
Client names stay anonymous until clients authorize them — and nothing on this page is invented. What follows is real, described plainly:
A DTC marine-parts retailer
Shopify + GorgiasBuilt a central revenue hub on Supabase joining order, support, and product data into one operating dashboard, and systematized their customer-support operations. The dashboard pattern you saw at the top of this page comes from this build.
A B2B publishing-services firm
HubSpotCRM work — pipelines, lifecycle stages, reporting.
Education-services clients
Reporting automationAutomated end-of-week and end-of-month reporting pipelines, running in production today.
SMS bot infrastructure
Trigger + draft patternBuilt for appointment-style workflows — the same trigger-and-draft agent pattern behind the Slack mocks above.
Every claim on this page maps to an artifact you can see — the dashboard, the matrix, the agent mocks. That's the standard we hold ourselves to, because it's the standard we'll hold your revenue data to.
The questions you're already asking.
Why not just hire a HubSpot or Salesforce partner agency?
Because they can only configure. Whatever your problem is, their answer is their platform, arranged differently — and if your workflow doesn't fit the tool, you'll spend a year force-fitting it. Dev shops have the mirror problem: they can only build, with no revenue-domain judgment about whether they should. We do both, so we can sell you the decision instead of a predetermined answer. The matrix above is that decision, and it's a deliverable in every audit.
Why not hire a RevOps person full-time?
At $2–10M, a full-time RevOps hire is premature: a six-figure loaded cost and a single point of failure who can quit. The Revenue Engine costs a fraction of one year of that hire, goes live in 8–12 weeks, and the system outlives any employee. When you're big enough to need the FTE, they'll inherit working infrastructure and runbooks instead of a graveyard — and we'll help you hire them on the Scale retainer.
We already have a CRM. Do we start over?
Almost never — HubSpot, Salesforce, Pipedrive, we work with what you run. But having a CRM doesn't mean it's configured as a system: lifecycle stages, routing, SLAs, and a data model your team actually trusts. If the tool fits your workflow, the audit says “configure” and we fix what you have rather than replatform. Often the answer is configure-plus-build — configure the funnel, build the data layer. That's the point of asking before spending, and you'll see the reasoning row by row.
We don't want to rip out our tools or retrain the team.
Good — that's not the job. The system goes in as a layer, not a replacement: your CRM and your tools stay, and we join them underneath — one warehouse, one context layer — with the dashboard and agents on top. Your team keeps working in the tools they already know, plus Slack. Replacement only happens when the matrix shows a tool actively costing you money, and that's a call you make from the audit blueprint — never a precondition of working with us.
What stack do you build on?
Yours. A Microsoft shop gets Azure, a Google shop gets Google Cloud, and if you have no allegiance we default to a lean Postgres stack — because it's the cheapest thing to own and the easiest to walk away from. The architecture is the constant — one warehouse, one context layer, agents on top; the vendor underneath is a fitting decision, and the audit puts it in writing. We sell the decision, not a platform — that applies to the build side too.
Who actually does the work?
A senior team, not a rotating bench: experts across marketing, sales, and technology, working through an AI-augmented delivery pipeline that compresses build hours without cutting scope. Specialist subcontractors handle platform-configuration overflow under direct supervision; judgment calls, architecture, and everything client-facing stay with the core team. You will never be handed to an account manager.
What do the agents actually do?
They watch triggers in your live data — a new lead, a deal gone quiet, a churn signal — and deliver a drafted action into Slack or email: the reply, the follow-up, the check-in agenda. A human reviews and sends. Agents draft, humans send — no exceptions, because your customer relationships are not a place to find out an AI was wrong.
What happens when I switch models or tools?
Nothing breaks — that's the design constraint. The context layer is a model-agnostic asset: your knowledge, entity- and relationship-tagged, stored outside any vendor's walled garden, readable by whatever model you rent next. Models get better and cheaper every quarter; locking your company's knowledge inside one vendor's tool is how you pay for the same migration twice. You own the context. You rent the intelligence. Swapping the intelligence is a config change, not a rebuild.
What happens after the build?
You get trained, and you get runbooks — the system is documented well enough to run without us. Most clients keep a retainer (Monitor, Operate, or Scale) for maintenance, agent tuning, and the automation backlog. But that's a choice, not a lock-in: you own everything we build, and the documentation is a deliverable, not a hostage.
What if the audit says all I need is a cheap configuration — or nothing at all?
Then that's what the blueprint says, with the matrix scores to back it up. You paid for the truth, and the blueprint is yours to execute with anyone — us, your team, or another shop; the credit applies if you build with us within 60 days. The audit is a fixed fee precisely so the recommendation isn't bent toward the bigger sale. We'd rather lose a build than force one. Paying for an honest answer is the product.
Systems rigor, founder speed.
100x Consulting is a senior team: founder Alexis Vega — ten years of cross-functional systems delivery, including SAP-scale enterprise environments — alongside experts across marketing, sales, and technology. The discipline of data models, handoffs, and reporting that has to be right was learned where a broken handoff between systems costs real money and “we'll fix it later” isn't an answer. That rigor now runs at SMB speed through an AI-native delivery pipeline: reusable skills, agent templates, and automation that compress every build. It's the same approach being sold to you, applied to the business itself — which is the fastest way to know it works.