Enterprise GTM AI
Your board mandated AI. But agents only reason as well as the model beneath them — and your revenue isn't a model yet. Epicbrief is the layer serious teams build on.
Customers
Business impact
Three enterprise GTM orgs. Three numbers that moved once the intelligence layer was in place.
“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.”
“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.”
“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.”
01 · The problem
Cost scales per query. Five reps, five answers about the same deal.
Six months of engineering. The warehouse isn't a model.
Generic outputs. Reps don't adopt — they bin the tool.
02 · The evidence
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
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.
CFO — regardless of industry"reduces close-of-books time"CXO referral within 30 days47-day median cycle · 4% discountWHY 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
Four architectural decisions sit under the layer. Two are table stakes — how data is stored, and when meaning is extracted. Two are the moat — which revenue concepts the model captures, and how they evolve as your business does. Get any one wrong and the whole thing falls apart.
Neo4j built their own GTM graph. Then they chose Epicbrief for the end-to-end intelligence layer.
LLM work — extraction, semantic interpretation, relationship inference — happens once, at ingest. The graph is the cache. Agents read structured edges, not raw text.
Agents every rep can use — without ballooning API cost.
GTM is shaped like a network. Champion isn't a record — it's who's pushing for whom. Pain isn't a field — it's what's affecting which people. Forecast isn't a number — it's evidence chained across deals. Warehouses force this into rows.
Agents that reason about your pipeline — not just look up records.
Ontology translates fragmented GTM data into real-world entities — Companies, Deals, People — and the meaning between them.
Agents that understand your business — not just integrate with your tools.
GTM evolves. New roles, categories, playbooks, product lines. Without a manager, every change is a re-architecture.
Agents that grow with your business — not ones you have to rebuild.
05 · Why ontology
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.
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 Healthcriteria_fulfilment(deal)Deal Healthbuying_committee_coverage(deal)BCorg_influence(person)BCdecision_velocity(deal)Forecastingstage_dwell_drift(deal)Forecastingforecast_confidence(deal)Cross-domainchampion_at_risk(deal)Cross-domain06 · The architecture
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.
Click any component to inspect →
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.
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.
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.
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.
07 · Get started
You've seen the layer. Here's how we build yours.
The engagement
Executive interviews capture the decisions leadership needs to make to move a KPI, and the questions that unlock each decision.
Deliverable
Decision Map
Inventory every system holding GTM signal. Ownership, access, shape, retention, identity keys, refresh cadence.
Deliverable
Source Inventory
Translate the Decision Map into entity types, relationships, and properties. Every question must be answerable by traversal.
Deliverable
Ontology Specification
Define the classification rubric for each GTM signal. Validate against real transcripts until outputs match expert judgment.
Deliverable
Calibrated Rubric Library
Pipelines turn live unstructured data into structured entities. Cross-source identity resolution unifies every system.
Deliverable
Live Intelligence Layer
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