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Agent Intelligence - Giving AI an Expert Sales Brain
Agent Intelligence

Everyone Built Chatbots.
We Built Cognition.

While others build RAG systems that treat every problem like keyword search, Vivun has mastered the science of capturing and codifying human expertise. RAG agents are sophisticated parrots—they find and repeat chunks of text while missing the relationships that create real understanding. Vivun has solved the harder problem: transforming expert judgment into structured decision-making systems where every action is grounded in verifiable logic and explicit knowledge relationships. Our ontology-driven architecture captures the declarative, procedural, and tacit knowledge that separates true professionals from search engines—creating agents that think, not just talk. Ava's LLM serves as the mouth, not the brain—that's where ontologies, knowledge graphs, and multi-hop reasoning take over.

Expert Knowledge

Declarative, procedural & tacit

1
Capture expert decision patterns
2
Structure tacit knowledge
3
Generate contextual responses
Active Processing

Sales Reasoning Model

Ontologies & knowledge graphs

Query
Graph
Logic
1
Map entity relationships
2
Traverse knowledge paths
3
Generate grounded conclusions
Graph Traversal

Structured Memory

Multi-layered reasoning architecture

Store
Link
Recall
1
Hierarchical context storage
2
Cross-interaction learning
3
Adaptive memory optimization
Memory Layers

Multi-Hop Reasoning

Transparent, auditable logic

Chain
Trace
Proof
1
Chain logical inferences
2
Bridge knowledge domains
3
Provide reasoning audit trail
Chain Logic

We captured the brains of elite sellers and built your AI teammate.

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Vivun Expertise Mapping
Patented Innovation

Patented Expertise.
Engineered for Scale.

Turning Elite Technical Know-How into AI That Works for You

How Vivun Learned to Map Domain Expertise

At Vivun, we didn't just build AI for sales—we pioneered a new way to capture and replicate domain expertise at scale. We started by studying the world's best technical sellers—the people who make the impossible deals happen. We dissected their brainpower: the know-how, the tactics, the instincts they rely on every day.
Through thousands of hours of observation, analysis, and iteration, we uncovered the hidden patterns behind technical excellence—how top sellers qualify, handle objections, and connect product capabilities to real buyer pain. And we didn't stop there.
We built proprietary models to map this expertise into a structured intelligence system. Today, that system is known as the "Sales Reasoning Model' and it powers Vivun's AI Sales Aegnt, giving every rep access to the same elite thinking as the best technical sellers on the planet.
This isn't just innovation—it's invention. Our team holds patents on methods for mapping and operationalizing domain expertise, because no one else has done what we've done. We turned tribal knowledge into a transferable, scalable framework—making Vivun the true pioneer in the space.
Learn About the Sales Reasoning Model
Domain Mapping
Elite seller knowledge extraction and categorization
Pattern Recognition
Hidden tactics and decision frameworks
Expertise Transfer
Tribal knowledge into scalable AI models
Scalable Intelligence
Every rep gets elite-level thinking
Patent Badge Rectangles
US PATENT
Text Processing
11,853,698
Granted
US PATENT
Gap Clustering
11,354,505
Granted
US PATENT
Trial Management
11,861,330
Granted
Hero Section - AI Agents
Intelligence Comparision

Not all AI Agents are Created Equal

RAG agents help you find what happened.
Ava helps you decide what to do next.

Ava vs RAG Animation

Ava vs Traditional RAG

AVA
"How do we win this deal?"
Question
Multi-Framework Reasoning
Analysis
Knowledge Graph Integration
Context
Actionable Next Steps
Strategy
RAG
"How do we win this deal?"
Question
Database Search
Retrieval
Transcript Snippet
Output
Toggle Section - Actionable Outputs

Actionable Outputs, Not Search Results

RAG Output
Ava Output
"I found notes about winning deals..."
[Transcript snippets]
"Here's the actionable path to win: Demo with champion, resolve IT objection."
[Cited from Calls & CRM]
What You See vs What You Get Section

What You See VS What You Get

The real difference is beneath the surface—only one agent thinks like a seller.

RAG Agent
Prompt:
"How do we win this deal?"
Output:
I found the following conversation on a call with [Sales Rep] about how they plan to win this deal this quarter.
Covers entire account, lasts 90 days

How RAG searches:

  • Uses LLM as a mouthpiece for answering, not as the reasoning engine
  • Finds text chunks from transcripts that match keywords from the prompt
  • Gathers transcript snippets discussing "winning the deal"
Ava
Prompt:
"How do we win this deal?"
Output:
You can win this deal by following these Actionable Next Steps…

Here's my current understanding of the deal…this is the Decision Criteria & what matters most…here are the key objections and how I would address them…
Citations from call transcripts, Slack, email, CRM

How Ava reasons:

  • Determines which concept and memory types the user needs
  • Prioritizes the most relevant memories and connects key concepts via knowledge graphs
  • Gathers context-rich memories and makes strategic decisions
Video Episodes Section

AI Deep Dive

Episodes

Your New Teammate is AI

Watch how AI reasoning transforms complex sales scenarios into winning strategies.

View More Episodes
High-Stakes Decisions - Redesigned
Because in sales, close enough isn't close enough

Sales Can't Afford AI That Guesses

Generic AI might work fine for writing poetry or summarizing documents. But sales happens in high-stakes environments where wrong moves kill deals, damage relationships, and cost revenue.

The Stakes

Misjudge stakeholder priorities

Deal stalls for months

Wrong timing on pricing

Competitor wins

Miss buyer signals

Opportunity dies

Poor follow-up execution

Relationship damaged

Why Language Models Break Down in Sales

Most AI sales tools are built on language models trained on everything from Reddit posts to academic papers. They know about sales, but they don't know how to sell.

Core Issue: No Sales Ontology

  • Confuses Champions, Economic Buyers, Decision Makers, and Influencers
  • Confuses org chart hierarchy with actual decision-making influence and power
  • No understanding of stage-appropriate actions vs. generic sales activities
  • Can't distinguish between real objections and negotiation tactics or stall behaviors
  • Mixes up symptoms, problems, and root causes
  • No grasp of basic sales stages and what actually moves deals forward

Additional Core Problems

Beyond ontology gaps, LLMs face other fundamental limitations that make them unsuitable for sales:

No Sales Methodology

No understanding of MEDDICC, Challenger, or proven frameworks

No Buyer Psychology

Can't read between the lines or interpret stakeholder dynamics

No Timing Context

Doesn't understand deal cycles, urgency, or competitive windows

No Risk Assessment

Unable to weigh competitive threats, deal blockers, or shifting probability factors

No Multi-Stakeholder Reasoning

Can't model complex organizational dynamics or influence networks

Compounding Errors

Auto-regressive reasoning leads to drift over complex decision chains

Why RAG Can't Fix This

Chunking sales documents doesn't create understanding—it just retrieves text. You can't solve an ontology problem with better search. The Sales Reasoning Model addresses this with structured definitions and explicit relationships, not more data.

The Solution

AI That Already Knows How to Sell

The Sales Reasoning Model doesn't learn sales on the job. Ava knows how to sell and how YOU sell. She can help you strategize your way to close and automate your way to total process compliance.

  • Learns your unique sales approach and methodology
  • Strategizes deal progression based on your process
  • Automates compliance with your sales framework
  • Adapts to your team's specific selling style
  • Transparent reasoning you can audit and trust
Expert Knowledge
Sales methodology encoding
Strategic Logic
Decision frameworks
Deal Memory
Context persistence
Transparent Reasoning
Auditable decisions

See Expert Reasoning in Action

Watch how Ava applies the Sales Reasoning Model to navigate a complex enterprise deal—from stakeholder analysis to competitive positioning to closing strategy.

Meet Ava
Four Tenets of True Enterprise AI
SECURE INTELLIGENCE

The Tenets of True Enterprise AI

Beyond Reactive AI — Ava Thinks and Works Ahead, and Securely

Expertise & Reasoning
True intelligence is more than pattern matching. Ava is modeled on expert workflows, delivering context-aware reasoning you can trust.
Memory
Ava remembers and builds context across interactions, making every output informed, accurate, and personal.
Proactivity
Stop waiting on prompts. Ava anticipates what's next and acts, delivering work that moves deals forward before you ask.
Data Security
Your data stays yours. We never train on client data or interactions—ensuring complete isolation and enterprise-grade security.
Enterprise Data Privacy Section
Enterprise Data Architecture

Your Data Builds Your Advantage,
Not Ours.

Privacy Architecture

Guaranteed Data Sovereignty

Unlike AI platforms that use your data to train shared models, Vivun ensures your competitive edge stays where it belongs: isolated and encrypted within your infrastructure.

Data Isolation
100% Private
Model Training
Zero Sharing
Competitive Intel
Stays Yours
Technical Implementation

Ava Space: Isolated AI Environment

Every interaction improves your own AI Agent inside your secure Ava Space. Your learnings never leave. Your competitive edge compounds exclusively for your organization.

  • Environment Dedicated, isolated AI workspace
  • Learning Model Private accumulation of insights
  • Data Flow Unidirectional, never external
  • Intelligence Compounds within your domain
Zero Third-Party Access Protocol

We do not allow your data to be accessed, transmitted, or received by any third party without your explicit consent.

No exceptions. This guarantee is built into our architecture, not just our policies.

Other AI Vendors
vs
Vivun Architecture

The real difference is beneath the surface—only one approach protects your competitive edge.

Shared Intelligence Model

Rely on your data to make their models smarter—for everyone. Your unique insights become someone else's advantage.

× Global training pools dilute competitive edge
× Your insights shared across all customers
× Data used to improve competitor advantages

Private Intelligence Model

Your data trains your AI exclusively. Competitive intelligence stays within your secure environment.

✓ Private Ava Space - isolated environment
✓ Zero data leakage - your edge stays yours
✓ Advantages compound over time, privately
The Minds Behind Ava's Brain
AI PIONEERS

The Minds Behind Ava's Brain

Ava's intelligence is built upon decades of groundbreaking research from the world's most visionary AI pioneers

Amir Bakarov

Amir Bakarov

Sr. Machine Learning Engineer

Craig Baker

Craig Baker

Lead Site Reliability Engineer

Mark Baltzegar

Mark Baltzegar

Director, Engineering

Brad Bouldin

Brad Bouldin

Principle Engineer

John Bruce

John Bruce

CTO

Chris Bruner

Chris Bruner

VP of Product

Sergey Buciuscan

Sergey Buciuscan

Lead Software Development Engineer

Jon Call

Jon Call

Sr. Engineering Manager

Shine Chaudhuri

Shine Chaudhuri

Lead Product Designer

Phil Chwistek

Phil Chwistek

Sr. Product Manager

Ryan Conklin

Ryan Conklin

Lead Software Development Engineer

Rachel Duchesneau

Rachel Duchesneau

Sr. Software Engineer

Iyobo Eki

Iyobo Eki

Lead Software Engineer

Melodie Hauck

Melodie Hauck

Customer Support Engineer

Danielle Heffernan

Danielle Heffernan

Customer Experience Manager

Rayne Hernandez

Rayne Hernandez

Sr. Machine Learning Engineer

Brian Johncox

Brian Johncox

Software Engineer

Kate Kravchyshyn

Kate Kravchyshyn

Sr. Software Engineer

Dawid Kulig

Dawid Kulig

Staff Software Engineer

Chen Liang

Chen Liang

Sr. Machine Learning Engineer

Dennis Lin

Dennis Lin

Lead Machine Learning Engineer

Brian Mckean

Brian Mckean

Lead Software Engineer

Leonel Mena

Leonel Mena

Software Developer

Joe Miller

Joe Miller

Chief AI Officer

Kevin Perrine

Kevin Perrine

Staff Software Engineer

Alanna Regan

Alanna Regan

Sr. Product Designer

John Salvatore

John Salvatore

Principle Engineer Architect

Shrey Shah

Shrey Shah

Sr. Software Engineer

Volodymyr Sokol

Volodymyr Sokol

Lead Software Engineer

Dan Stone

Dan Stone

Lead Software Engineer

Vitaliy Tomkiv

Vitaliy Tomkiv

Sr. Software Engineer

Mark Ward

Mark Ward

Lead Machine Learning Engineer

Russell Witham

Russell Witham

Director, Product Management

Ryan Woodcox

Ryan Woodcox

Staff Software Engineer

Vivun AI Experts
AI EXPERTISE

Learn from Vivun's AI Experts

Deep insights from the minds shaping sales intelligence

Why RAG Isn't Enough: The Power of Ontologies & Knowledge Graphs in AI

Episode 05

Multi-Modal AI: The Next Leap in Human-Agent Collaboration

Latest Episode

Understanding Structured Knowledge

Episode 04

Watch All Videos
Sales Reasoning Model

AI Automates.
Ava Collaborates & Executes.

Unlike typical AI tools that guess from patterns, Ava reasons through structured knowledge to deliver transparent, reliable, and autonomous sales intelligence.

Inside Ava's Reasoning Engine
STAGE 01

Question Processing

Ava identifies this as a deal strategy question requiring competitive analysis, extracts context like deal stage and stakeholder roles, activates relevant sales methodologies (MEDDIC, Challenger Sale), and determines which knowledge domains to engage.

VS TRADITIONAL RAG:
Traditional RAG just extracts keywords for search terms.

Persistent
Memory
Raw Data Chunks
Text A
Text B
Text C
Text D
Structured Ontology
Champion
Economic
Buyer
Technical
Influencer
MEDDIC
Process
STAGE 02

Analysis Engine

Ava's multi-framework reasoning analyzes competitor strengths/weaknesses, maps stakeholders and their concerns, aligns value propositions to prospect pain points, and evaluates risks and timing.

VS TRADITIONAL RAG:
Traditional RAG just searches for documents containing similar keywords without understanding sales context.

STAGE 03

Context Integration

Ava's knowledge graph integration connects prospect industry trends to solution benefits, references historical patterns from similar deals, understands stakeholder influence networks, and anticipates objections based on deal characteristics.

VS TRADITIONAL RAG:
Traditional RAG just assembles text snippets without strategic insight.

Semantic
Memory
Episodic
Memory
Procedural
Memory
Working
Memory
94%
87%
96%
91%
Input
Analysis
Pattern
Recognition
Multi-Hop
Reasoning
Risk
Assessment
Decision
Synthesis
Action
Generation
Email Thread
CRM Data
Call Notes
STAGE 04

Output Generation

Ava delivers a prioritized action plan with stakeholder-specific messaging, competitive differentiation tactics, timeline recommendations based on buying cycle, and measurable success metrics.

VS TRADITIONAL RAG:
Traditional RAG provides generic advice without prioritization or customization to deal specifics.

STAGE 05

Consultant-Level Recommendations

Sophisticated reasoning culminates in a personalized strategic playbook. The system delivers specific plays, competitive positioning advice, and tactical recommendations calibrated to your unique situation.

Competitive Response
Updated with Gong call
Leverage technical differentiation in security architecture. Position against Competitor X's recent vulnerability disclosure.
Pricing Strategy
CRM sync complete
Recommend value-based pricing model. ROI projections support 15% premium over standard enterprise rates.
Stakeholder Map
Email analysis fresh
Engage CFO early with TCO analysis. Security team shows highest influence on final decision.
Optimal Timing
Live calendar data
Q4 budget cycle creates urgency. Align proposal delivery with their quarterly planning meeting.
Risk Mitigation
Recent call notes
Address integration concerns with proof-of-concept. Highlight similar successful deployments.
Success Metrics
Updated with Gong call
Track engagement velocity, stakeholder sentiment, and competitive displacement indicators.
Learn About Sales Reasoning Model
DEEP DIVE

Sales Reasoning Model:
Learn How Ava Thinks

See how Ava's Sales Reasoning Model transcends the constraints of traditional AI through structured intelligence. Learn about the advanced architecture behind Ava's intelligence: ontologies, knowledge graphs, transparent reasoning, and the next generation of autonomous sales execution.

Learn More
Structured Intelligence
Beyond pattern matching to true reasoning with sales-specific ontologies
Transparent Logic
Every decision traced back to sources with auditable reasoning chains
Autonomous Action
Proactive work generation that moves deals forward without prompts
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Coach
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