Root Partners

Your buyers are asking AI who to call. Find out what it says, and build the team that changes the answer.

Root Partners shows companies what AI platforms recommend about them to buyers, and who they recommend instead. For companies that need more than a report, we design and build the AI-native marketing organizations that compete in this environment.

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The Numbers

AI-driven buyer discovery is already happening at scale. Most businesses are not in the results.

1.2%
of businesses appear in AI recommendations. The other 98.8% are invisible to buyers who ask AI first.
45%
of buyers now use AI to find services, up from 6% one year ago
83%
of businesses never appear in AI-generated recommendations for their category
7
AI platforms tested in every audit: ChatGPT, Perplexity, Gemini, Claude, Copilot, Meta AI, Grok
The Structural Shift

Two problems arriving at the same time.

The External Problem

AI agents control the top of your funnel before a human is ever involved.

When a buyer wants a solution, they increasingly ask an AI. That AI builds a shortlist and surfaces a recommendation before your sales team enters the picture. For most companies, that shortlist doesn't include them. What AI tells your buyer in the first seconds of their query determines whether your brand is in the conversation at all — and most companies have never seen that picture clearly.

The Internal Problem

The marketing org built for the last decade can't compete at the speed this one demands.

The content velocity, research throughput, and marketing operations that agentic commerce demands cannot be sustained by a traditionally structured marketing team. The companies positioned to win in this environment will run their marketing function differently from the start. The structure, the roles, and the operating model all need to be built for what's coming, not what came before.

Root Partners addresses both. Our Intelligence practice shows you exactly where you stand with AI buyers today. Our Infrastructure practice designs and builds the marketing organization that competes at the speed and scale this environment demands.

The New Buyer Funnel

AI owns the top of the funnel. Your window to influence is earlier than you think.

AI handles awareness, discovery, and much of evaluation before a human is ever involved. Your team typically enters the picture at step three, after the shortlist is already set.

Stage What Happens Who's Driving
Awareness AI agent queries the category and surfaces relevant vendors AI
Discovery AI assembles a shortlist based on signals it can read and evaluate AI
Evaluation AI reviews evidence, scores options, surfaces a recommendation AI + Human
Selection Human validates and confirms the AI's recommendation Human
Purchase A human or autonomous agent completes the transaction Human

Most companies are optimized for steps 3–5. Root optimizes for steps 1–2, where the shortlist is made and your odds of appearing are determined — and builds the internal organizations capable of sustaining that position over time.

In Practice

"We assumed our SEO work was doing the job. The audit showed our primary competitor was appearing in 71% of AI buyer queries in our category. We weren't appearing in any of them. That was a six-month pipeline problem we didn't know we had."

B2B SaaS company — 60 employees — Pacific Northwest ← Replace with client data when available
4%
AI visibility score at baseline, before any changes
38%
Score after implementing the priority recommendations
7
Weeks from baseline audit to measurable score improvement
How We Work

Two practice areas. One thesis.

Most clients begin with the Competitive AI Audit to understand exactly where they stand. The path from there depends on how much ground needs to close and how fast it needs to close it.

Practice 01

AI Visibility Intelligence

What AI platforms tell your buyers about you, your competitors, and your category — measured, scored, and delivered as a competitive intelligence picture you can act on.

01
Competitive AI Audit

A single blind simulation run across all 7 AI platforms using real buyer personas built for your industry. You get your visibility score, the competitive matrix showing who AI recommends instead of you, and priority recommendations to close the gap. One-time, no commitment.

7 Platforms Tested Visibility Score Competitive Matrix Priority Recommendations
02
Active Optimization

Monthly re-runs of your simulation with automated improvement suggestions generated from score changes between cycles. You'll know whether your content, messaging, and positioning investments are actually moving the needle, and what to adjust next. Cancel anytime.

Monthly Re-Simulation Score Trend Tracking Automated Suggestions Competitor Monitoring
Practice 02

AI Marketing Infrastructure

The marketing organization designed for agentic commerce. We design the structure, build the agent infrastructure, and deliver a team model that lets one marketing leader operate at the output level of four or five traditional hires.

Before
Traditional Marketing Team
4–5 specialist hires: Marketing Automation, Email, Research, Content, Ops
$260K–$400K in annual salary cost
3–4 week content production cycles
Capacity capped at headcount
Institutional knowledge exits with the employee
After
AI-Native Marketing Organization
1 Marketing Leader managing a configured set of AI agents
40–60% reduction in annual marketing team cost
24–48 hour content and research cycles
Output scales with demand, not headcount
Institutional knowledge embedded in the system
Most clients project full cost recovery within the first year. The steady-state economics of an AI-native marketing organization represent a significant reduction in team cost at equivalent or greater output volume. The primary value is in the capability and speed — the cost savings are the byproduct.
03
AI-Native Marketing Architecture

A structured engagement to design the agent-driven team model for your organization. We map the roles, design the agent architecture, specify the technology stack, and build the management system your team will run on. The deliverable is a complete organizational blueprint with implementation specifications — built for a specific company, not a generic framework.

Org Design Agent Architecture Workflow Design Tech Stack Spec Management System
04
Build and Implementation

We implement the architecture. Agent configuration, systems integration, content and research workflow build-out, and the operating model your team inherits. This is where the design becomes a running system. Engagement scope and timeline are calibrated to the complexity of the organization.

Agent Configuration Systems Integration Content Workflows Operating Model
05
Ongoing Management

A monthly retainer to manage, optimize, and evolve the system as AI capabilities advance and competitive conditions shift. Includes performance reporting, capability updates, and ongoing strategic guidance. Most clients move here after implementation — the system compounds over time when it's actively managed.

Monthly Optimization Performance Reporting Capability Updates Strategic Guidance

PE Portfolio & Enterprise Engagements Working with a portfolio of companies or an organization that needs a bespoke engagement? Root has structured programs for PE operating partners and enterprise marketing leaders.

Talk to us →
The Audit Methodology

Why our simulation approach sees what other audits miss

01

Structural Intelligence Mapping

Most audits start with a search. Ours starts with the information layer AI draws from. Before a single query runs, we build a complete picture of how your brand exists in the content, schema, and review ecosystems that AI platforms use to form recommendations. Or how it doesn't.

Foundation
02

Behavioral Persona Modeling

Generic queries produce generic results. We construct buyer personas with real intent, context, and purchase motivation. How a buyer frames a question to AI determines everything about which brands get recommended in response. The personas drive the simulation, not the other way around.

Personas
03

Blind Multi-Platform Simulation

The simulation runs across seven AI platforms simultaneously: ChatGPT, Perplexity, Gemini, Claude, Copilot, Meta AI, and Grok. Critically, the AI doesn't know which brand is being evaluated. No thumb on the scale. The responses generated are the same ones your real buyers are seeing right now.

Simulation
04

Quality-Weighted Visibility Scoring

Being mentioned sixth in a long list is not visibility. We score every result by recommendation quality. First position with substantive coverage outweighs a passing mention by a factor of four. The score reflects what actually drives buyer decisions, not just whether your name appeared somewhere in a response.

Scoring
05

Competitive Signal Analysis

Every simulation surfaces which competitors the AI recommends instead. We map those results into a full competitive visibility matrix that shows who owns which platforms, which brands are gaining ground, and the specific structural advantages driving their AI dominance. You see the battlefield clearly for the first time.

Competitive Intel
06

Impact-Ranked Action System

The output isn't a report. Every visibility gap is mapped to a specific, executable fix, ranked by the size of the improvement it produces. Technical changes, content investments, and distribution moves are sequenced across 90 days so the highest-impact work happens first and results compound over time.

Action Plan
About

Built on the conviction that most companies don't know what AI says about them — or how to compete in a world where it matters

Root Partners was founded on a simple observation: when buyers ask AI for a vendor recommendation, the result is determined before anyone picks up a phone. Most businesses have no idea what that result looks like. Many don't know they're losing to competitors they've never benchmarked against.

The Intelligence practice measures that gap. Using blind simulations across every major AI platform, we show companies their real visibility score, the competitive picture AI is presenting to their buyers, and the specific changes that move the needle. Most clients see their results and understand, for the first time, exactly why certain deals weren't converting.

The Infrastructure practice addresses what comes next. Once a company understands the external picture, the question becomes whether their marketing organization is built to close the gap and sustain a competitive position in a buying environment that moves faster than any traditional team can track. We design and build the AI-native marketing organizations that operate at the speed and output level this environment demands — one marketing leader, a team of configured AI agents, and the management system to run it.

Both practices are grounded in the same operating experience: six successful exits and $1.4B+ in enterprise value generated across EdTech and SaaS markets. That background shapes how we work. We produce findings you can act on and systems you can actually run, not frameworks that gather dust on a shared drive.

FAQ

Questions we hear from every client

The shift to agentic commerce raises questions most organizations haven't had to answer before. Here's how we think about the ones that come up most.

Q
What does an AI-native marketing team actually look like?

The most common structure Root designs is a single marketing leader overseeing a set of AI agents that handle work traditionally done by a team of specialists. Research, content production, demand generation, competitive monitoring, and marketing operations all run through configured agents rather than individual hires. The human leader's role shifts from managing people to managing systems: setting strategy, reviewing outputs, making judgment calls, and running the optimization loop. For most clients, this represents the capability equivalent of a four to five person marketing team running at the cost structure of one.

Q
How does the audit connect to the consulting?

The audit shows you the external problem: what AI is currently recommending to your buyers and where the competitive gaps are. The consulting addresses the internal problem: whether your marketing organization is built to close those gaps and sustain a competitive position in an AI-driven buying environment. Most consulting engagements begin with the audit, because the findings define exactly what the marketing organization needs to produce. The two practices are designed to work together, and most clients move from one to the other naturally once they've seen their results.

Q
Is this an AI tools purchase or an organizational change?

Organizational change, with AI tools as the enabling layer. What Root designs and builds is a new operating model for your marketing function: the structure, the workflows, the management system, and the technology stack required to run it. The AI agents are the mechanism. The deliverable is a marketing organization that can produce at a level the previous headcount structure couldn't sustain. Most engagements surface significant cost savings relative to the traditional team model, but the primary value is in the capability and output, not the cost reduction alone.

Q
Isn't this just AEO or GEO?

AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are content strategies. They tell you how to optimize what you publish so AI platforms are more likely to cite you. Root does something different: we measure what AI is actually recommending to your buyers right now, before any optimization happens. That's a commercial intelligence question, not a content production question. The output is a competitive picture and a prioritized action plan, not a content brief. Most clients use our findings to inform their AEO or GEO work. We're the diagnostic that precedes the optimization.

Q
Why isn't my business showing up in ChatGPT or Perplexity recommendations?

AI platforms build recommendations from structured data, authoritative citations, schema markup, and content architecture — not from paid placement or traditional SEO rankings. Most companies are invisible to AI buyers because their digital infrastructure was built for human search engines, not AI reasoning systems. The fix requires understanding which signals AI platforms use to evaluate your category, then restructuring your content, metadata, and authority footprint accordingly. The report tells you exactly where the gaps are.

Q
How long does it take to improve AI visibility?

Meaningful improvement is achievable within 90 days when changes are prioritized correctly. Structured data and schema implementation typically produces results within weeks. Content and authority-building compounds over months. Root's roadmap sequences actions by impact and effort so the fastest, highest-return changes happen first, giving you measurable improvement quickly while building durable visibility over time.

Q
How do AI platforms decide which brands to recommend?

AI platforms draw recommendations from training data, retrieval-augmented generation (real-time web search), structured schema markup, and authority indicators like citations, reviews, and third-party coverage. Brands that appear consistently, accurately, and authoritatively across these sources get recommended. Brands that rely on paid advertising, keyword density, or outdated SEO signals are largely invisible to AI reasoning systems. The underlying logic is different enough from traditional search that most GTM playbooks don't transfer.

Get Started

Find out where you stand. Build to compete.

Start with the Competitive AI Audit to see your visibility score, your competitive position, and exactly what to change. If you need help closing the gap — or redesigning the team that closes it — we are a conversation away.