Accelerate your business with AI.

Strategy. Capability. Execution.

3Pillars.ai builds value-creating AI inside mid-market companies. A senior operator who has run businesses like yours embeds in your organization and builds the capability with your team. Priced for the middle market.

Senior partners on every engagement
Fastest system to production: 3 weeks
First demo free for qualified opportunities
app.3pillars.ai/portfolio illustrative client workspace · full read access
Use caseNumber to hitStatusProgress
Contracting engineRevenue · 6 agents
$2.0M+
In production
Order intake triageCost · 3 agents
$640K
Pilot, on track
Management officeOps · 2 agents
1-3 FTE
In production
Quote & proposal deskRevenue · scoping
$410K
Leverage Map
gate 1: number attached · gate 2: pilot proof ROI reconciled monthly
Functions
All opportunities 14
Revenue & contracts 4
Operations 4
Service 3
Finance 3

Opportunity map

sorted by annual value · 9 of 14 shown
$2.0M+
Contracting engine
In production · 6 agents
$700K
Pricing integrity
Scoping · data ready
$640K
Order intake triage
Pilot · week 4 of 6
$520K
Service deflection
Queued · Q4
$450K
Bid response drafts
Queued · Q4
$410K
Quote & proposal desk
Selected · gate 1 passed
$380K
Invoice reconciliation
Mapped · needs owner
$290K
Document digitization
Mapped · data audit
$180K
Quality reporting
Mapped · low effort
Quote & proposal desk
Annual value $410K
Data readiness high
Time to pilot 6 weeks
Business owner VP, sales
Gate 1: number attached
Queue for pilot

Order intake triage · pilot

week 4 of 6 · gate 2 in progress
96.2% accuracy vs senior reviewer · target 95.0% on track to pass
accuracy by week · dashed line = target
34 min → 6 min
review time per order
71%
resolved with no human touch
3.1%
escalated for senior review
SecurityComplianceBusiness owner scale decision at week 6, with proof in hand

Benefits ledger · contracting engine

reconciled with Finance monthly
March
$86K
April
$142K
May
$210K
June
$265K
Credited year to date: $703K · plan $600K, running 17% ahead every dollar traceable to a case · methodology signed off by Finance
What each view shows
Value Portfolio every use case, ranked by dollar value
Leverage Map where the value sits across the business
Pilot scorecard the proof gate before anything scales
Benefits ledger realized savings, reconciled monthly
00 / Why now

You bought the tools. You ran the pilots. But the P&L sees no value.

The models work. What fails is organizational: wrong use case, no number, no owner. And in the mid-market, every stalled pilot is somebody’s real budget.

live reporting, 2026 · hover to pause, click for the source
Sound familiar?
Pilots that demo well, then stall in security review.
A copilot rollout with adoption charts but no dollars attached.
AI spend spread across vendors, with nobody accountable for the return.
An AI project in month nine that was scoped for month three.
01 / The three pillars

Strategy. Capability. Execution.
Three deliberate design decisions.

Most AI programs die in the seams between the strategy firm, the risk committee, and the build vendor. We closed the seams: deep strategic knowledge from our experts, capability built and embedded within your organization, and execution your P&L can carry.

Strategy where AI pays off Capability built from within Execution priced for the mid-market 3Pillars.ai one design

Strategy

A map of your operation with every AI use case valued for ROI and scored: moat vs commodity, complex vs simple. The view that helps us determine value-creation opportunities.

Capability

The organizational harness. A senior AI professional embedded in your team, accelerating AI. From governance to prototyping, this is where value compounds quickly.

Execution

Priced for the mid-market. Our own build team with agents underneath, working in your environment. You own the code, the playbooks, and the results.

02 / The operating model

The model: our operator inside, your people alongside, a build team behind.

The operatorOurs · senior · embedded

Works inside your business. Finds where the value lives, sizes it, and minds the risk from first map to production. Industry judgment, and the call on when AI is not the answer.

Your peopleYours · alongside

Build with us, run the pilots, and own the tools and playbooks at the end. The capability grows inside your organization and stays there.

The build teamOurs · agents underneath

Production engineering priced for the mid-market, with AI agents underneath. No pyramid of juniors on your invoice.

The operator
inside your business: finds, values, minds
senior · embedded
Your people
own the workflows, keep the tools
capability grows here
The build team
production engineering, agents doing the work underneath
AI agents · always on
the capability stays when the engagement ends
See a demo
03 / How it works

Every engagement starts the same way: our operator, inside your business.

step / 01

We embed

A senior operator joins your team and spends weeks inside the actual workflows, learning how the work gets done: the processes, the data, and the people who own them.

week 1 inside the workflows, shadowing the work
week 3 data map drafted: systems, owners, gaps
✓ first prototype in the team’s hands
Why embed
step / 02

The AI Leverage Map

Built from inside the work: every use case mapped, valued for ROI, and scored on one grid. A number on every opportunity. Most don’t survive the scoring.

Quick wins
Winners play
Table stakes
Distractions
Contracting engine
Order triage
Digitization
Custom LLM · killed
moat or commodity, easy or hard: scored before anything is built
step / 03

Proof before production

We build the pilot against the number. For qualified opportunities, the first working demo is on us.

demo built on your use case, your data
pilot running vs target: 95.0%
✓ gate passed · 96.2% · approved to scale
step / 04

Scale what works

Only proven work reaches production. The build team builds it, the capability takes root with your people, and you own what we build.

value reconciled monthly, per system

Your risk ends at the demo

You see it working on your use case before you commit a dollar to the build.

The pilot gate protects your budget

If the pilot misses the number, it does not scale. You decide with proof in hand.

You keep everything

The system, the code, the playbooks. In your environment. No subscription to cancel.

04 / Why embedded

Distance kills AI projects. Proximity delivers them.

“The best AI opportunities are rarely visible from the outside. You discover them by sitting next to the people doing the work.”Praveen Neppalli Naga, CTO, Uber · on the Agentic Pods program · July 2026 ↗
05 / Who we serve

The playbook is industry neutral. The people are industry natives. Pick yours.

See how we serve each industry →

The thinking behind these pages, across every vertical, lives in Our insights →

06 / Our results

Results from production.

Result 01 · Revenue
$2 to 5M

A six-agent contracting engine, live in three weeks

Stood up, put into production, and processing more than a thousand contracts. Annualized upside from a standing start.

What it provesSenior-led delivery at a speed a staffed pursuit team cannot match.
1,000+ contracts in
6 agents, parallel
terms + flags out
week 0 standing start · week 3 in production
Result 02 · Revenue
$3 to 7M

A data model redesign that moved the top line

A core data model and its workflows, redesigned and run by seniors on the engine rather than an associate bench.

What it provesAnalysis to implementation, owned by one senior team.
Result 03 · Operating cost
$300K/yr

An AI-native management office replaced a traditional PMO

Two full-time coordinator roles of effort, run by agents instead. Roughly $300K a year in loaded cost, taken out of the back office.

What it provesThe back office runs with no coordinator in the middle.

Pick one, and we'll show you a working demo.

Pick one, get the demo
Revenue & contracts
Contract intelligence

1,000+ contracts processed; $7M identified.

Pricing & rate analysis

Terms, benchmarks, and leakage surfaced from your own paper.

Proposal & bid response

First drafts from your win history, on your templates.

Pipeline & account intelligence

Buyer briefs and account theses, assembled on demand.

Relationship resurfacing

Deals and contacts gone quiet, ranked and revived from your own CRM history.

Receivables follow-up

Aging invoices chased with drafted, personalized outreach and a memory of every promise.

Operations & service
Case processing at scale

Review time from 30 to 45 minutes down to 5 to 10.

Claims, appeals & casework

Document-heavy workflows with a number attached.

Call center & customer service

Summarization, routing, next-best-action.

Document digitization

Vision models extracting and validating records.

Document-room triage

Thousand-file rooms read in full, with the red flags surfaced weeks earlier.

Platform & infrastructure
AI infrastructure accelerators

Up to 40% of foundational architecture, pre-built.

Local model deployment

Open-source models inside your environment, safe for regulated data.

Executive copilots & briefings

Meeting capture to board materials, on cadence.

Governance & benefits ledger

Tracking that proves value and keeps it compounding.

Tribal knowledge capture

What your best salespeople and technicians know, captured and answering on live calls.

07 / Partner selection

Five questions to ask anyone pitching you AI.

Including us. The answers separate a system in production from a science project. We publish ours because we like them.

Strategy consultancyadvises 3Pillars.aidesigned for all three AI build shopbuilds
Who finds the use case? Analyst benchmarks and stakeholder interviews. An operator embedded in your business maps the work from the inside and attaches a number to each opportunity. You bring it. They build what you ask for.
What number is it accountable to? A business case inside the deck. A number to hit, set before anything is built. Reconciled monthly after it ships. Time and materials. Effort, not outcome.
Will it survive security, compliance, and audit? Recommended in a governance workstream. Someone else implements. Built in from day one. Security, risk, and compliance are part of the design, because we work where review is unforgiving. Where pilots go to die.
Who owns the system? There is no system. There is a roadmap. You do. Your environment, your code, your playbooks, and your people trained to run it. No subscription. Their platform. You rent the outcome.
What happens when AI is the wrong answer? Another phase to study it. We say no, with the analysis that shows why. Knowing when AI is the wrong answer is most of the judgment you’re hiring. AI is always the answer. They sell AI.

Bring these questions to every pitch you sit through, including ours. Our answer is usually a working demo.

"Our competitors are AI companies, applied to your industry. We are your industry insiders, applying AI."
3Pillars.ai
08 / The team

Operators and advisors first. AI builders second.

Kevin Mehta

Strategy
Enterprise StrategyOperating ModelsValue Quantification
Formerly Senior Partner, HealthScape Advisors / Chartis · Altarum Institute · KPMG
LinkedIn ↗

Sonesh Shah

Strategy
Enterprise P&LDigital TransformationConsumer & Industrial
Formerly Global President, Dremel · Bosch
LinkedIn ↗

Eric Patzelt

Capability
Growth StrategyProgram GovernanceMarket Expansion
Formerly HealthScape Advisors · SCAN Group
LinkedIn ↗

Surya Kotha

AI Execution
Enterprise AIProduction DeploymentOpen-Source Models
Formerly SAP · Johns Hopkins Medicine · KPMG
LinkedIn ↗
More firm behind the faces: operators in every industry we serve, Senior Advisors across AI research, market strategy, data, and public policy, and a dedicated build team. Schedule a call and we'll bring the right people.
09 / FAQ

The questions buyers actually ask.

Straight answers. If yours isn't here, let's talk.

How fast do we see something real?
Weeks, not quarters. The first working demo is built on your use case before you commit to a build, and our fastest production system went live in three weeks.
How are you different from an AI agent firm?
They bring agents and ask you where to point them. We put an operator inside your operation first, find where the payoff lives, attach a number, and then deploy the agents. And when AI is the wrong answer, we say so. An agent firm never will.
Are we big enough for this?
The model was designed for the mid-market. A credible AI hire runs $600K before anything ships, and the big firms send you their bench. Senior operators plus agents deliver the scope a large team staffs, at a cost your P&L can carry. The firm is designed around that math.
Our data is a mess. Do we need to fix that first?
No. Drawing the data map is part of the operator’s first weeks: what exists, what condition it’s in, and what it blocks. Some of the highest-value use cases in our library ran on messy data. What matters is knowing which gaps are worth closing, and that takes someone inside the work.
We already have an AI leader. Where do you fit?
Embedded beside them. The operator works under their mandate, builds the map and the prototypes from inside the business, and the management office turns it into a sequenced, measured portfolio. The seat and the credit stay theirs; we bring the machinery underneath.
We don't have an AI leader yet. Can you start anyway?
Yes. That is the default shape of the engagement: our operator holds the Chief AI Officer seat, owns the agenda, and shows your executive team measurable ROI, until you hire the permanent seat. Then we hand it over, working.
Who owns what you build?
You do. The systems run in your environment, the code and playbooks are yours, and your people are trained to run them. We are not building you into a subscription.
What if AI isn't the right answer for our problem?
Then we say so, with the analysis that shows why. Knowing when AI is not the answer is part of the judgment you're paying for. Every recommendation carries a number either way.
Why embedded versus advising? How's it different?
Advisors read your process diagrams from a conference room. Embedded operators live inside the work, learn how it actually gets done, and find the wins nobody else sees. See why this matters and where it's worked.

Put an operator on your AI agenda.

Every engagement starts with the same step: a senior operator inside your business, building the AI Leverage Map from the inside. It ends with a ranked map and real numbers, whether or not we go further together.

Talk to us