An AI agent engages
It sees the campaign brief, but not the customer’s previous objections or promises.
THE CONTEXT LAYER FOR AI AGENTS
You gave your AI agent a task.
Did you give it the complete context?
AI agents fail in production when customer history, intent, business rules and previous actions remain scattered across disconnected systems.
CallsCRM
IntentPolicies
ActionsOutcomes
CONTEXT GRAPH ARCHITECT
Hi—I’m the Zipteams Context Graph Architect. I’ll understand the task you want AI agents to perform, estimate the revenue exposed by missing context, and build a practical first blueprint.
First, what kind of company are you building this for?
No customer data is requested or stored in this assessment.
SCROLL TO SEE THE MISSING LAYER ↓
01 / THE PROBLEM
Every decision without context
puts revenue at risk.
An AI agent can complete its assigned task—while still missing the customer story, buying intent or business constraint needed to make the right decision.
A human takes over
The customer waits while the salesperson reconstructs the story across CRM notes and recordings.
The next agent follows up
It repeats a question, misses the buying signal and sends a generic response at the wrong moment.
02 / THE MISSING LAYER
Data access is not
complete context.
A Context Graph turns fragmented events into operating memory—so an AI agent knows what happened, what matters and what to do. If a human takes over, the same context arrives instantly.
InteractionsCalls · Email · WhatsApp
Business stateCRM · Product · Payments
SHARED HUMAN + AI CONTEXTWho they are
What happened
What happens next
What happened
What happens next
SignalsIntent · Objections · Sentiment
RulesConsent · Policy · Eligibility
OutcomesResponded · Converted · Renewed
03 / THE SHIFT
One customer memory for every AI agent—
available at the moment of action.
WITHOUT SHARED CONTEXT
Repeated questions
Delayed human handovers
Conflicting actions
Missed commitments
Revenue leakage
→
WITH A CONTEXT GRAPH
Instant handover briefs
Relevant next-best actions
Coordinated humans and AI
Governed execution
Learning from outcomes
04 / WHERE TO START
Connect the conversations where
context changes the outcome.
Bring in real conversations
Connect calls, chats, meetings and CRM events to create a live customer journey.
Give context at every action
Surface the customer story, unresolved concerns and best next action to the AI agent—and to a human whenever they take over.
Measure revenue outcomes
Write outcomes back to learn where context is lost and which actions improve conversion.
Bring 20–30 customer journeys. We’ll show where context breaks, the revenue exposed and the first graph to build.
Connect conversations & book a demo ↗