Enterprise GTM AI

Revenue teams need more than AI agents. They need a GTM model agents can trust.

Epicbrief turns calls, emails, CRM records, and customer interactions into a governed intelligence layer — so leaders, workflows, and AI agents can reason from the same revenue truth.

See the platform → Get a demo

Customers

04 · Business impact

Revenue leaders, on the record.

Customer-reported outcomes from enterprise GTM teams using Epicbrief to improve growth, forecast quality, and enterprise execution.

20→60% YoY growth · 6 months

“Our YoY growth increased from 20 to 60% in 6 months because we were able to make GTM and product decisions based on objective data from Epicbrief.”

Amritpal Singh
Amritpal Singh President, Field Operations · Multiplier
98% Forecast accuracy · 2 quarters

“Creating more time for my reps to sell, removing friction in the sales process, delivering objectivity and consistency in the way in which we operate and report are all of critical importance for me.”

Mark Woodhams
Mark Woodhams Chief Revenue Officer · Neo4j
2.2× Enterprise win rate · 9 months

“We clearly see the sales execution and product gaps we need to fix to successfully transition from product-led / mid-market to enterprise sales motion.”

Martin Illman
Martin Illman Chief Revenue Officer · Supermetrics

Results reflect customer-reported business outcomes and use cases. Impact depends on data quality, workflow scope, and implementation.

01 · The problem

AI agents fail when revenue context is fragmented.

Calls live in Gong. Fields live in Salesforce. Customer risk sits in emails, support tickets, implementation notes, and product feedback. Agents can retrieve fragments, but they cannot reason reliably unless the business context is modeled first.

Step 01

Connect agents to raw systems

Cost scales per query. Five reps, five different answers about the same deal.

Step 02

Rebuild logic in the warehouse

Months of engineering. The warehouse stores rows, not meaning.

Step 03

Buy another cockpit

Generic outputs, no governed context. Reps don't adopt — they bin the tool.

AGENTS
Reps
Managers
Ops
Execs
The GTM intelligence layer.
GTM Ontology
GTM Graph
Integrations
DATA SOURCES

02 · From conversation to signal

A customer sentence becomes structured revenue context.

Epicbrief turns the messy language of customer conversations into governed signals your CRM, workflows, dashboards, and agents can use.

Customer conversation

“We like the product, but procurement needs security approval and the CFO wants to compare it against the incumbent before end of quarter.”

Epicbrief creates

  • Decision criteria: security approval, incumbent comparison
  • Economic buyer: CFO involved
  • Risk: procurement and security dependency
  • Next step: security review before end of quarter
  • Forecast implication: not commit-ready until approval path is confirmed
  • Evidence: source call, speaker, timestamp, CRM object
Activation Salesforce field update · Manager inspection · Forecast risk signal · Agent-ready context

02 · The evidence

Speed never moved revenue. Decisions did.

Agents save time. They don't make the revenue engine smarter — and the context behind every good decision is still fragmented across every tool your team uses.

03 · In practice

Every CMO has an ICP slide. Almost nobody knows if it's true.

Marketing builds the ICP from firmographics, intent data, and the GTM team's best read — before deals close. The CRO suspects it isn't the real pattern. The CFO can't prove it. None of them can ask the question that would settle it. Until the layer exists.

"Show me the deals we closed in 2025 that closed in under 60 days at under 5% discount. What pattern do they share — champion role, EB seniority, dominant pain, criteria that mattered, committee shape? And where are we still marketing to companies that don't fit the pattern?"

The use case: the CMO presents an ICP slide every quarter. Most of those slides are targeting hypotheses, not patterns from what closed. The gap between the two is where misallocated GTM spend lives — for one to ten million per year at enterprise scale.

Marketing's ICP slide

The hypothesis

  • $50M+ ARR
  • Retail · Fintech · Healthcare
  • 1,000+ employees
  • North America
  • Tech stack: Snowflake, Salesforce

Built from firmographic data + intent + analyst reports. Before any deals closed. Updated quarterly in a deck.

What the layer answers

The pattern

  • EB is the CFO — regardless of industry
  • Top criterion: "reduces close-of-books time"
  • Source: CXO referral within 30 days
  • Committee adds IT lead by week 2
  • → 47-day median cycle · 4% discount

Industry was the 8th strongest correlation. Marketing was targeting the wrong dimensions — and spending against them.

WHY THIS IS IMPOSSIBLE TODAY— click any row to expand

The pattern was always in your data.
Nothing could read it. Until now.

04 · Why Epicbrief

Most tools store your data. Epicbrief models what it means — and keeps it alive.

Epicbrief does not just sync GTM data into another database. It turns fragmented records and conversations into real-world revenue concepts — companies, people, deals, risks, pains, champions, products, buying committees, and next steps — connected to source evidence and updated as your business changes.

The proof
Neo4j built their own GTM graph. Then they chose Epicbrief for the end-to-end intelligence layer.
— Why Neo4j is a customer
01 Reuse

Extract once, reason many times.

Meaning is captured when data arrives, so every workflow and agent can reuse the same trusted interpretation instead of re-reading raw text from scratch.

Agents every rep can use — without ballooning API cost.

02 Shape

Relationships, not records.

A champion is not a field. A risk is not a note. A buying committee is not a table. Epicbrief stores how revenue concepts relate — across people, deals, pains, products, and timing.

Agents that reason about your pipeline — not just look up records.

03 Meaning

Your revenue concepts, not a generic schema.

Every company defines pain, champion, qualification, churn risk, product fit, and expansion differently. The ontology captures your definitions — not a vendor's.

Agents that understand your business — not just integrate with your tools.

04 Change

A living model, not a static schema.

Your segments, products, playbooks, and forecast process change. The model changes with them — versioned, governed, and kept aligned to how the business actually runs.

Agents that grow with your business — not ones you rebuild.

05 · Use cases

The first workflows where the layer moves the number.

Epicbrief does not replace your CRM, call recorder, warehouse, or BI stack. It gives them the missing context layer.

01 · Pipeline inspection

Identify which deals are real, which are stuck, and which are missing critical buying-process evidence.

SignalsChampion strength · Decision criteria · Economic buyer · Next step · Procurement risk

02 · CRM field automation

Keep opportunity fields updated from actual customer conversations instead of rep memory.

SignalsPain · Objections · Competitors · Product gaps · MEDDICC · Forecast risk

03 · ICP validation

Compare the accounts you target against the patterns found in deals that actually closed.

SignalsDominant pain · EB seniority · Committee shape · Cycle length · Discount · Source path

04 · Churn & expansion

Detect customer risk and expansion signals before they become lagging metrics.

SignalsUnresolved product gap · Adoption issue · Negative sentiment · Stakeholder change · Expansion trigger

05 · Agent readiness

Give AI agents governed GTM context instead of asking them to search raw transcripts and fragmented CRM records.

SignalsAccount context · Deal state · Source evidence · Business definitions · Allowed actions

05 · Why ontology

Every concept that decides a deal — modeled as a live signal.

Companies, Deals, and People become one entity each, unified across every system. The relationships between them — Champion, Pain, Urgency — carry severity, history, and evidence. Together they form a living model of your revenue that updates as deals move and your business changes.

Layer
5 nodes · canonical only
click pills to add layers · click any node to inspect
works at stakeholder on party to fulfilled by belongs to on involves addressed by evaluates achieved by step of affects owned by part of member of has stage committed as on channel on deal part of competing for reason for attributed to based on attached to of feature fulfills Company Person Deal hub Contract Activity Pain Criterion Metric Process step Org unit Department Stage Forecast cat. Channel Thread Competitor Win/loss reason Feature Usage event
Canonical Deal Health Buying committee Forecasting Engagement Competitive Expansion
Start with the 5 canonical entities — Company, Person, Deal, Contract, Activity.

Click any layer pill above to add it. The canvas pulls back to fit the model as the graph grows.

Click any node for its properties, edges, and which domains share it.
Computed signals — functions over the data model ▾

Not stored fields. Each one is a query over the existing data. Cross-domain signals are where the unified backbone earns its keep.

champion_strength(deal, person)Deal Health
Recency, frequency, response latency, sentiment trend, who initiated. Reads from activity sub-graph.
criteria_fulfilment(deal)Deal Health
Percent of must-have Criteria met. With product Capability modelled, identifies the specific gap blocking the deal.
buying_committee_coverage(deal)BC
Which stakeholder roles are filled vs missing. Also returns org_blast_radius — how many departments the deal touches.
org_influence(person)BC
PageRank-style score over REPORTS_TO and INFLUENCES edges. Surfaces the quiet decision-shaper your rep didn't know about.
decision_velocity(deal)Forecasting
Time per ProcessStep vs the historical average for similar deals. Flags stalls before they show up in stage data.
stage_dwell_drift(deal)Forecasting
Days in current Stage vs cohort median. Combined with decision_velocity, gives a sharper signal than CRM stage age alone.
forecast_confidence(deal)Cross-domain
A composite — criteria_fulfilment × buying_committee_coverage × decision_velocity × account_temperature. Independent of what the rep entered.
champion_at_risk(deal)Cross-domain
champion_strength dropping × org_influence not declining = the champion is fading on YOUR deal, not in their job. Action: re-engage. (Deal Health × BC × Engagement.)

06 · The architecture

The structural layer your GTM stack is missing.

Not another copilot, cockpit, or workflow wrapper — the one underneath all of them. Here's how the other three decisions compose around the ontology: meaning extracted once, on arrival; stored as relationships, not rows; kept current as your business changes.

Ontology Manager Defines meaning · data model · agent skills DECISION 04 · CHANGE Source systems CRM · Gong · email activity · usage EXTERNAL Ontology Raw data → meaning → GTM signals DECISION 03 Graph DB Connected facts Multi-hop traversal DECISION 02 Agents Autonomous + human-in-the-loop DECISION 01 Execution Salesforce · Slack rep deliverables EXTERNAL FEEDBACK · activity loops back as new source data

Click any component to inspect →

Why composed

The 4 decisions compose.

Ingestion-time semantics needs the ontology. The ontology needs the graph. The graph needs agents to be useful. The agents need the manager to stay current. Decouple any of them and the whole chain leaks value.

Why the boundary

Sources and execution stay external.

Epicbrief doesn't replace your CRM, your call recorder, your email, or your Slack. It sits between them — turning raw events into a structured model agents reason from. Autonomous agents read the layer directly. Human-in-the-loop agents project from it — into existing tools when they fit, into a purpose-built view when they don't.

Why the feedback loop

Activity flows back as signal.

What happens in execution — meetings booked, emails sent, deals closed — flows back into the source layer, enriching the ontology over time. The graph compounds. The agents get smarter. The manager governs what they can mean.

The Ontology Manager

Where meaning stays governable — and yours.

Browse node types. Add new concepts (Renewal Risk, Expansion Signal, Pricing Trigger). Version semantic vocabularies. Watch which agents depend on which concept.

This is the difference between a vendor's idea of GTM and yours.

ontology · manager
Node types
  • Company
  • Person
  • Deal
  • → Pain
  • Metric
  • Criterion
  • ProcessStep
  • Activity
severityint · 1–5
scopeenum · org / team / individual
categorystring
urgencyenum · low / med / high / critical
↳ ROLLS_UP_TOPain
+ Add property or edge

06 · The proof

Neo4j built their own GTM graph. Then they chose Epicbrief for the intelligence layer.

A graph database can store relationships. Epicbrief adds the GTM ontology, extraction logic, signal definitions, source evidence, and activation workflows needed to make the graph operational for revenue teams.

01

They already understood graph architecture.

That made the missing layer clearer, not less necessary.

02

The hard part was not storing connected data.

The hard part was deciding which revenue concepts matter, extracting them consistently, and keeping them current.

03

Epicbrief turns the graph into an operating layer.

Revenue leaders, RevOps, workflows, and agents can use the same modeled context.

07 · Get started

Find out if your GTM data is ready for enterprise AI.

We'll map where your customer context lives today, which revenue workflows depend on it, and what it would take to turn it into structured signals your teams and agents can use.

Start the conversation

Get a demo →

Epicbrief works with your existing CRM, call recorder, warehouse, and workflow tools. It does not replace them.