G2's #1 Rated Fractional RevOps Service

We help revenue teams put AI to work on data you can trust.

Trusted by

Pindrop Hex PowerSchool project44 Cursor

Deep Dive Upskill your teams with courses, certifications, and coaching. Terrain Deploy apps, agents, and skills at record speeds. BI & RevOps Foundations Adapt your RevOps to the age of AI. Less effort, better data where it matters.

Why Lean Layer

Why high-growth B2B teams partner with Lean Layer.

The strategic vision, technical horsepower, and data precision of an elite RevOps department. Embedded in your business.

Who we are We aren’t outside observers or high-level advisors. We are a team of RevOps architects, data engineers, and GTM specialists who plug directly into your workflow to build systems that scale.

No egos or red tape. Just seasoned operators
Integrated across your entire GTM stack
Fractional cost, full-department impact

Our Experience We’ve architected, repaired, and accelerated revenue engines for high-growth companies like Amplitude, Rho, ActionIQ, and OpenWeb.

Deep technical mastery in Salesforce, HubSpot, and 30+ tools
Complete coverage across the revenue lifecycle
#1 rated RevOps partner on G2

What makes us different We combine strategic design with hands-on technical execution and AI workflows to deliver results fast.

We ship code, automate systems, and clean data
A team of experts working as a single engine
Flexible model without locked-in 12-month overhead

BI & RevOps Foundations

Fractional revenue operations engagements give you access to an entire RevOps org at the cost of a single manager.

Scale up when a project lands, down when it doesn’t. You pay for the hours you use.

Process & Design

Data model design

Data capture

Business rules

Anomaly monitoring

Processes

Source Systems

CRM + Core Revenue TechOpportunities · Accounts · Pipeline Salesforce

Call RecordingsTranscripts · Sentiment · Diarization Sybill

Chorus

Data EnrichmentFirmographics · Intent · Company data · Hiring Clay

News

Product / AnalyticsUsage · Adoption · Health Amplitude

Slack / EmailChannels · Threads · Comms Slack

Docs / WikisPlaybooks · SOPs · Artifacts Notion

Orchestration Layer

Cleaned, matched & certified

GroomedDeduplicatedEntity-matchedAccuracy-checkedIntegrity-certified

Organizational Context Layer

Your unique business & org context

Best-practice-driven KPIs

Investor & board reporting preferences

AI-Ready Warehouse

AI-Ready WarehouseDecision-ready data, structured for the KPIs you care about

Lean Layer’s expertise & judgmentThe opinionated data model, metrics framework & process design underneath every layer

What we take on

Fractional RevOps TeamNo RevOps function today? We become one. Strategy through execution, without hiring for it.

Specific WorkstreamsAlready have a team? Hand us what’s stuck. CRM builds and overhauls, AI readiness, revenue analytics, lead scoring, attribution reporting, forecasting, annual planning, territory design.

Critical CoverageParental leave, a sudden resignation, the gap between hires. We step in and keep things running.

Explore BI & RevOps Foundations →

Build-to-own tools

Terrain

Built-to-own tools, designed for you: fast to deploy, easy to evolve. Terrain apps bend to your business' unique contours so you can tackle whatever comes your way at record speed, accuracy, and customization. In revenue operations, durability is not permanence; it is adaptability.

The ground you walk on is shifting faster than ever. Navigating the landscape with speed is your competitive advantage.

Planning Studio

Example Terrain app

Plan next fiscal year end-to-end — territory, headcount, and capacity, rolled up from the bottom.

FY2027 planDraft

Territory assignments142 reps

Headcount & hiring plan+38 phased

Capacity modeling$186M

Bottoms-up planning1,240 accts

Reshape the plan as the year moves — reassign territories, re-time hires, remodel capacity in minutes.

What we build

AppsPurpose-built tools for insights, planning and running your unique motion.

AgentsAutomated workflows that capture, enrich, and act on data without manual busywork.

SkillsReusable AI capabilities your team can plug into any workflow, on demand.

Explore Terrain →

Courses · certification · coaching

Deep Dive

Training and enablement that helps operations and revenue teams work smarter and deploy AI faster as new capabilities land.

Lean Layer customers get free, unlimited access to all courses and certification programs. Office hours, roadblock removal, and coaching are available as add-ons.

Deep Dive curriculum

AI foundations for RevOpsFoundational

Applying new model capabilitiesPractical

Deploying AI across the orgPractical

What's new in foundation modelsAlways current

Refreshed as new foundation models ship.

What we build

CoursesSelf-paced lessons on putting AI to work in revenue operations, from the fundamentals to the frontier.

CertificationsStructured programs that validate your team’s AI & RevOps skills, so capability is proven, not assumed.

CoachingOffice hours and 1:1 support to unblock your team as they apply what they’ve learned.

Explore Deep Dive →

Revenue leaders move faster with us.

Trusted by 100+ revenue teams

G2 Satisfaction Score leaderboard — Lean Layer ranked #1 with a score of 96, ahead of every other RevOps and sales operations service

Read our G2 reviews

Recognized on G2: a Leader four quarters running

Built on six straight quarters as a High Performer

Wall of love

Revenue teams love working with us.

How we work

A fractional extension of your team.

For BI & RevOps Foundations, we plug in as a fractional extension of your team: strategic oversight, a dedicated point of contact, and specialists in every function. Scale up or down without the cost, or the commitment, of a full-time hire.

Month-to-month ~10-month avg. tenure G2-recognized

See how our fractional team works

Strategic oversight

GTM Advisor

your RevOps mastermind

Your point of contact

Engagement Manager

Your RevOps expert & point of contact

Technical specialists

Marketing Sales CS Ops BI AI

Plus an in-house cybersecurity expert and AI & full-stack engineers keeping your data safe.

PE & VC partnerships

We run GTM analytics for top PE firms across their portfolios. Private Equity and Venture Capital investors refer us into the companies they’ve backed, the strongest endorsement there is, alongside the clients who send us their peers. In fact, more than 70% of our revenue comes from these referrals.

Insight Partners Andreessen Horowitz Equal Ventures

The brains behind the top management consulting firms

When the most recognized names in management consulting need analytics and board reporting, they bring us in, not their internal teams. We combine revenue operations and analytics expertise with AI most firms only talk about, which makes the work sharper, faster, and far less expensive.

Put AI to work, as an extension of your team.

See how BI & RevOps Foundations, Terrain, and Deep Dive come together for your revenue org. Tell us about your team and we'll set up time with a Lean Layer expert.

Lean Layer’s POV

AI made your revenue data impossible to hide.

Every ops team has a spreadsheet it fixes by hand before the numbers go out. AI just made that secret impossible to keep, and impossible to ignore.

AI can answer instantly, run real operational work, and collapse the cost of software: real leverage for revenue leaders. But point it at a messy CRM and it produces answers that are polished, confident, and wrong. Most RevOps teams are squeezed between a CEO who wants AI now and a foundation that isn’t ready. We think that’s a false choice: real value in the first weeks, an honest foundation running in parallel, and a clear path to more.

1
Start with the problem, not the tool. Use cases come from the problem you’re solving, not from “we want AI.”
2
Own your data, and your applications. The context that makes AI useful should belong to you.
3
Data integrity is the baseline. Fix what matters at the source, then keep it honest as an operating metric.
4
Put AI to work in the workflows you already run. Many small, governed agents beat one giant one.
5
Keep a human in the loop. Judgment doesn’t disappear; the job changes.

Read the full case

For the last few decades, running a revenue organization meant managing through lag. You asked a question, someone found the data, likely cleaned it in Excel, and came back days later with an answer you could not fully check. Planning happened once a year, in a spreadsheet, and was out of date by February. Coaching ran on anecdote and gut, because the evidence was too expensive to assemble. Million-dollar decisions were made on numbers you privately doubted.

A decade of tools promised to fix this. Forecasting platforms, conversation intelligence, BI dashboards. They helped. But ask anyone who has run revenue operations what still sat behind the polished tools: the spreadsheets never went away.

We believe AI changes the equation in three ways.

It answers instantly. Any question about the business can now be answered in the time it takes to ask it. A plan can be a living tool you reshape as the year moves, not a file you open once a quarter. Rep performance, pipeline quality, and where wins actually come from can sit in front of you continuously, with proactive insights and recommended actions, not in a deck that took an analyst a week.

It runs work. Agents can now do real operational work: update a field from a rep's Slack message, enrich an account the moment it enters pipeline, put the report your CRO needs in their inbox before they wake up, rebuild in minutes the workflow changes that used to eat someone's week. The daily operations of a revenue team can be automated, monitored, and improved continuously.

It collapses the cost of software. Until now a revenue leader had basically two options: buy rigid tools built for the average company and bend your process around them, or pay enterprise prices for custom builds that work half the time. Purpose-built software, shaped to your motion and your definitions, can now be built in days and changed as fast as your business changes.

That is real leverage, and boards know it. It is why every revenue leader is being asked what their AI plan is.

What AI changes behind the scenes

Here is what the pressure for AI runs into: the dirty secret of revenue operations.

The secret was always the spreadsheet. Anything the CRM could not answer got exported into Excel and fixed by hand, usually the night before the board meeting. The report looked clean. The data underneath it was not. Every ops team knows this. It was survivable because a person stood between the messy data and the audience, quietly patching it.

AI should remove that person from the middle. But point an agent at a messy CRM and it still produces an answer: polished, confident, precise-looking, and wrong. The first seller whose number is off will find the flaw in seconds, and trust in the whole system starts to die. Bad data used to mean bad reports. Now it means convincing, unreliable answers at scale, available to anyone in the company who asks.

To be clear: the goal is not to eliminate every spreadsheet on day one. Some of that layer holds real judgment, and it can even become something AI consumes. The goal is knowing exactly what your AI references when it answers, so the answer can be trusted. Crawl, walk, run.

And none of the familiar work goes away. Someone still has to build the CRM properly, keep the pipeline honest, make the dashboards agree, run the outbound engine, score and route the leads, and keep a dozen tools talking to each other.

AI does not replace that work. It changes how it is done, and it raises the bar for how well it has to be done.

So AI changes the RevOps job itself, and for the better. The old job was producing reports and patching data downstream, while keeping the lights on: the territory changes, the forecast asks, the daily requests that never stop. The new job is running the revenue system: connecting data sources, fixing capture at the source, measuring trust as an operating metric, deploying automation and agents with proper governance, and operating the applications built on top. Less report factory, more infrastructure. It is a better job, and a more valuable one.

And here is the honest part: nobody in seat can make that shift alone. Your ops team knows your data and your systems better than anyone. What they cannot do is keep the lights on, upskill on a technology that changes monthly, and rebuild how the organization operates, all at the same time. That is not a talent problem. It is a capacity problem.

The squeeze, and the false choice

Inside most revenue organizations right now, these truths collide.

The CEO wants AI in the business today. The ops team, the people closest to the data, know the foundation is not ready, and they are still expected to deliver everything they delivered before.

They are not being difficult. They are the ones who will be blamed when an impressive demo produces a number a seller tears apart in the next QBR.

Companies resolve this squeeze in one of two bad ways. They ship something flashy on data nobody trusts and burn credibility they cannot easily get back. Or they disappear into a six-month data project with nothing to show along the way, and the business loses patience before the foundation is finished.

We think that is a false choice, and our entire offering is built to break it: real value in the first weeks, honest about what it stands on, with the foundation work running on its own track and a clear path to more.

AI gives revenue teams leverage. We make it leverage you can trust.

The Beliefs

Our beliefs are built on years of experience running revenue operations for high-growth companies. And here's the thing: most of these beliefs have always existed. AI has just brought them front and center.

Start with the problem, not the tool.

“We want to use AI” is not a problem statement. What matters is what you are trying to solve: are we going to hit the number, is our move upmarket working, who should carry the enterprise motion. The use cases come from the problem, and the data requirements come from the use cases. Most teams cannot name the ten or twenty data points that matter most to the decisions they are making right now. We can, because we have done this across hundreds of revenue organizations. That judgment is the starting point of everything else.

Own your data, and your applications.

The context that makes AI useful should belong to you: your CRM data, your call recordings and transcripts, your definitions, and the applications built on all of it. Most companies do not actually own their richest data, and summaries built for a mass audience miss what matters to your business. Owning and processing your own context makes AI more accurate and dramatically cheaper to run. And ownership is protection: if a vendor disappears or you part ways with a partner, the system keeps working. The more you own, the better positioned you are for whatever comes next.

Data integrity is the baseline: fix what matters, at the source.

AI cannot do real revenue work, forecasting, planning, scoring, pipeline inspection, without trustworthy data. But perfect data everywhere is neither realistic nor necessary: there is a hierarchy. Start with the data behind the decisions leadership is making now, and fix it where it is born: capture rules, validation, enrichment, review where judgment is required. Then keep it honest, because data quality is not something a company achieves once: integrity runs like an operating metric, clean record rate, anomalies, time to resolve, with an AI rules layer that catches what falls through and learns. Data integrity is not a cleanup project that follows the AI strategy. It is part of the AI strategy.

Put AI to work in the workflows your team already runs.

Not one giant agent, but many small, governed ones with specific jobs: capture, enrichment, reporting, alerting, analysis. The work your team spends hours on today, updating flows, pulling reports, chasing reps for fields, is exactly where AI pays for itself first, and much of it delivers value even while the data foundation is still being built. The choice of what to automate, with an agent, a point tool you already pay for, or a process, is a design decision. Intentional beats impressive.

Keep a human in the loop.

AI does not remove the need for revenue operations judgment. Someone still has to decide what matters, catch what the rules miss, and own the system. The skills that job requires are changing fast, and your team cannot keep the lights on, upskill, and rebuild the operating model all at once. That is where we come in: we do the work with you, we train the people who own the system, and the capability and context stay inside your business, not with a vendor.

How we help