100x ConsultingBook 15 min

01Fast vs right

Fast is easy now. Right is rare.

Anyone can draft outreach, replies and proposals with AI now. Trusting what reaches your buyers is the hard part. For founder-led B2B services firms, 100x is forward deployed engineering for the revenue engine, with an on-site reviewer who makes what the automation produces right.

Book a free 15-min mini-auditGet a 2-page sample teardown
EVERY CORRECTION BECOMES A RULEAI DRAFTSCHECK AGENTREVIEWERSENT
83%

of US small businesses that use AI use it for writing or marketing.

Federal Reserve Banks, March 3, 2026 · US small employer firms: 6,525 responses, September–November 2025; the AI questions were optional

58%

of US employees surveyed have relied on AI output at work without evaluating its accuracy.

The University of Melbourne & KPMG, 2025 · 1,019 US respondents in a 47-country study, fielded November 2024 – January 2025

30%

of respondents at organizations using AI say theirs experienced a consequence of AI inaccuracy in the past year.

McKinsey & Company, November 5, 2025 · 1,753 respondents at organizations that regularly use AI, mid-2025, mostly large companies

02Why now

AI got fast. The checking didn't.

AI is now part of everyday work. The checking didn't keep up, and the cost shows up downstream: errors, rework, and work that sounds like everyone else's.

The speed

It runs inside the everyday work now, mostly unchecked.

In the New York Fed's region, most service firms surveyed now run AI inside their business processes, up from a minority a year earlier. Many people rely on what it writes without checking it.

Service firms that used AI in their business processes in the past six monthsTwo survey waves
0%25%50%75%AUGUST 2025 SURVEYAUGUST 2026 SURVEY40%61%NEW YORK–NORTHERN NEW JERSEY REGION · FIRMS USING AI ONLY TO SEARCH NOT COUNTED

Federal Reserve Bank of New York, September 1, 2026 · Service firms in the New York Fed's Business Leaders Survey, August 2025 and August 2026

What US employees surveyed said about AI at workSelf-reported
0%50%100%Relied on AI output without evaluating its accuracy58%Made mistakes in their work due to AI57%

The University of Melbourne & KPMG, 2025 · 1,019 US respondents in a 47-country study, fielded November 2024 – January 2025

The errors

So mistakes ship, and much of the time AI saved goes to fixing them.

Inaccuracy is the negative consequence of AI that organizations report most. And much of the time AI saves goes to correcting, rewriting and verifying what it produced.

Negative consequences of AI experienced in the past year
51% SAID THEIR ORGANIZATION HAD EXPERIENCED AT LEAST ONE NEGATIVE CONSEQUENCE0%25%50%Inaccuracy30%Explainability14%Personal privacy11%Cybersecurity10%RESPONDENTS AT ORGANIZATIONS THAT REGULARLY USE AI · 2025

McKinsey & Company, November 5, 2025 · 1,753 respondents at organizations that regularly use AI, mid-2025, mostly large companies

The time AI saves, and where it goesVendor research
Time saved by AINEARLY 40% LOST TO REWORKsuch as correcting errors,rewriting content and verifying outputs

Workday, January 14, 2026 · 3,200 full-time employees who use AI, at organizations with $100M+ revenue, November 2025 · vendor research

40%

of US desk workers surveyed believe they received AI-generated “workslop” in the past month. They reported spending an average of 1 hour and 51 minutes dealing with each one.

BetterUp Labs & Stanford Social Media Lab, September 2025 · 1,004 full-time US desk workers, September 2025 · vendor research

The sameness

And it sounds like everyone else's.

In controlled studies, ideas produced with ChatGPT were less varied than ideas people came up with alone.

Share of ideas that counted as unique, pooled across participantsExperiment re-analysis
0%50%100%Repurpose a tennis racket and a garden hoseNo tool92%Instructed to use ChatGPT70%Invent a toyNo tool100%Instructed to use ChatGPT41%ONE IDEA PER PERSON · GPT-3.5-ERA CHATGPT · SHORT IDEA TASKS

Nature Human Behaviour, May 14, 2025 · Re-analysis of two online idea experiments, about 200 participants each; short idea tasks, not business writing

03The stakes

In revenue work, slop lands in front of buyers.

Most companies surveyed aren't seeing a return from AI yet. The firms getting value did something most skipped.

Your revenue engine

Where slop meets your buyers.

In a small B2B services firm, AI drafts the work closest to revenue: the first reply, the follow-up, the proposal, the renewal note. That's the work your buyers read.

Where AI drafts in revenue workIllustration
FIRST REPLYa generic openerFOLLOW-UPthe same templateas everyoneDISCOVERY NOTESPROPOSALa claim nobody approvedQUOTERENEWALa detail that's wrong
An illustration of where drafted work reaches a buyer, not survey data.
Companies and the value they get from AI1 square = 1% of companies
60%minimal revenue and cost gains despite substantial investment35%scaling, seeing some returns5%achieving AI value at scale

Boston Consulting Group, September 2025 · 1,250 CxOs and senior executives who make AI decisions, 68 countries, mostly large companies

73%

of B2B buyers surveyed actively avoid suppliers who send irrelevant outreach.

Gartner, June 25, 2025 · 632 B2B buyers, August–September 2024

What works

A person at the end isn't enough on its own.

In a meta-analysis of more than a hundred experiments, mostly decision tasks, people paired with AI did worse on average than the better of the two alone, and better where the person was stronger at the task than the AI. The companies getting value redesign the work itself.

When a person plus AI beats either one aloneMeta-analysis · 106 experiments
WORSE THAN THE BETTER ONE ALONEBETTER THAN EITHER ALONE−0.50+0.5All experiments, on averageg = −0.23Where the person alone was better at the task than the AIg = +0.46Where the AI alone was better at the task than the persong = −0.54EFFECT SIZE (HEDGES’ G) · CONTROLLED EXPERIMENTS PUBLISHED 2020–2023

Nature Human Behaviour, October 28, 2024 · 106 controlled experiments published 2020–2023, about 85% of effects from decision tasks

Fundamentally redesigned workflows because of AI
0%50%100%AI high performers (about 6% of respondents)NEARLY THREE-QUARTERSEveryone elseONE-QUARTERSHARES AS REPORTED BY MCKINSEY, 1,719 RESPONDENTS, 2026

McKinsey & Company, August 25, 2026 · 1,719 survey participants, May–June 2026; high performers are about 6% of respondents

That is why 100x doesn't stop at a person at the end. The automation carries the volume, a check agent tests every draft against written rules, the reviewer corrects what gets through, and every correction goes back into the system as a rule.

04The fix

An engineer builds it. A reviewer makes it right.

For founder-led B2B services firms that want AI's speed without the slop, 100x is forward deployed engineering for the revenue engine, with an on-site reviewer who closes the loop.

  • The forward deployed engineer

    Maps how the work runs today, then builds the automation, the check agent and its rules inside your process and your stack.

  • The on-site reviewer

    Checks what the automation produces, corrects it, and feeds each correction back as a rule, so the automation gets more accurate.

How every agent workflow is builtThe 100x method
THE FORWARD DEPLOYED ENGINEERTHE ON-SITE REVIEWER01MAP THEPROCESSfix the workflow first02AI DRAFTSAI carriesthe volume03CHECK AGENTtests draftsagainst your rules04REVIEWERCORRECTSfixes it andadds a rule05MEASUREagainst the baselineRULES, FOR EXAMPLEvoice and tonerepeated wordsunapproved claimswhat must never be sentSENTEVERY CORRECTION BECOMES A RULE
Two layers of review sit between AI and anything that goes out: an automated check, then the on-site reviewer.

Unlike AI tools that hand over drafts and leave the checking to you, every agent workflow 100x builds for a client carries both layers of review.

  • Start from the workflow.

  • Never automate a broken process.

  • Check every draft against written rules.

  • Put a person at the end of every agent process.

  • Turn every correction into a rule.

  • Measure against a baseline recorded before the build.

Forward deployed engineering puts engineers inside a business to build production systems beside the people who use them. 100x works with firms of $1M–$10M and 5–30 people, in agency operations, publishing and education services, and staffing and recruiting. A deployment opens with a structured diagnosis of how revenue runs today and produces a written report: a map of the path, right-sized prescriptions, and a four-day plan. The baseline is recorded before the build, then measured again at launch, at 30 days, and at 60–90 days.

How a deployment runs
01EMBEDsit inside the workflow02BUILDsmallest thing that works03PROVEmeasured against a baseline04HAND OVERsystem, runbooks, trainingPATTERNS BECOME THE NEXT DEPLOYMENT
Four steps, drawn as a loop because the fourth returns to the first.

What gets built

What a deployment installs.

A data layer that makes one system the record of truth. A workflow layer that moves a lead along the path — routing, lifecycle, SLAs, handoffs. An intelligence layer on top: dashboard, reporting, and agents whose drafts are checked against your rules and corrected by the reviewer before a person sends them.

One system, three layers
CRMSTORESUPPORTBILLINGINBOXT3INTELLIGENCEdashboard · reporting · agentsT2SYSTEMSrouting · lifecycle · SLAs · handoffsT1DATAone system of recordCHECK AGENT · REVIEWER · A PERSON SENDS
The data layer reads from the tools below it; the dashboard and the agents sit on top.

05Try it

See it on your own company.

Leave a work email and 100x's own outbound pipeline goes to work on your company: agents research it, design the automations, build a working prototype, and draft the email. Automated checks test the build. A person reviews both, then sends it.

Our own agent pipelineRuns in production
RESEARCHsite & market✓DESIGNautomations that fit✓BUILDa working prototype✓VERIFYschema · build · e2e✓REVIEWa person sends it✓24–48 H END TO END
Each agent stage writes its output to the next; the last stage is a person.
Revenue overview
Sample data
Weighted pipeline
$486,000
+12% vs last month
New leads · this wk
47
31 from inbound
Median speed-to-lead
11 min
target: < 15 min
Forecast · this Q
$291,000
vs $275K target
Leads
412100%
Qualified
16841%
Proposal
5413%
Won
215%

Every stage has one definition, agreed in writing.

Leads touched < 15 min94%
SLA breaches this wk3
Agent drafts awaiting review5

Sample data.

Agent pipeline — intake

See a sample prototype — built for a fictional company, kept as the public sample.

Work

The work.

Deployments by architecture: who it was built for, what it ran on, what was built.

  • B2B publishing-services firm

    HubSpot

    Pipelines, lifecycle stages, reporting.

  • Education-services clients

    Reporting automation

    End-of-week and end-of-month pipelines in production.

  • DTC marine-parts retailer

    Shopify · Gorgias · Supabase

    Central revenue hub joining order, support and product data into one operating dashboard.

  • SMS bot infrastructure

    Trigger + draft pattern

    Appointment-style workflows.

100x delivers as a team: founder Alexis Vega with experts across marketing, sales and technology. The practice is pointed at the revenue function. Findings outside it go to partner referrals.

Built on and wired into

  • HubSpot
  • Salesforce
  • Shopify
  • Stripe
  • QuickBooks
  • Supabase
  • PostgreSQL
  • Google Cloud
  • Zapier
  • Make
  • Google Sheets
  • Gmail
  • Google Calendar
  • Notion
  • Airtable
  • Intercom
  • Zendesk
  • Azure
  • Slack
  • Pipedrive
  • Gorgias

06Talk

Before we talk.

What the reviewer does, how an engagement starts, what the work runs on, and what the agents do.

What does the on-site reviewer do?

Works inside your process next to the automation. The reviewer checks what it produces, corrects what the check agent missed, and turns each correction into a rule the automation follows from then on, so the next draft starts from the fix.

How does an engagement start?

A fifteen-minute conversation, then a structured diagnosis of how revenue runs today: lead flow, follow-up, the pipeline, quoting, onboarding, renewal. The diagnosis produces a written report, and a walkthrough follows it.

What stack do you build on?

Yours. A Microsoft shop gets Azure, a Google shop gets Google Cloud, and with no allegiance the default is a lean Postgres stack.

What do the agents do?

They watch triggers in live data (a new lead, a deal gone quiet, a churn signal) and draft the next action. A check agent tests the draft against your rules and delivers it into Slack, where a person reviews it, corrects it if needed, and sends it.

Make it right.

Fifteen minutes on one workflow where AI already drafts: what gets through, what a check would catch, and what a reviewer would correct first. If there is nothing worth fixing, we will say so.

Book a free 15-min mini-audit

Build · Check · Correct

Prefer email? alexis@100xconsulting.co

Sample teardown

The written output of a diagnosis, on an anonymised stack.