Enterprise AI Architecture

Enterprise Deep Research: The Next Killer App for AI

Ofer Mendelevitch from Vectara reveals how applying web research concepts to private enterprise data solves the #1 challenge in GenAI: factual accuracy.

73%: The Factual Accuracy Crisis

"73% of LM customers implementing use cases say that factual accuracy is their top challenge right now."

— Ofer Mendelevitch, VP of AI at Vectara

Timestamp: 02:00

The insight: This single statistic explains why hallucination mitigation has become table stakes for enterprise AI deployment. If you're not addressing factual accuracy, you're not solving the real problem.

The Factual Accuracy Crisis

The #1 Enterprise Challenge

73% of GenAI customers cite factual accuracy as their top challenge. This isn't a theoretical concern—hallucinations prevent enterprises from deploying AI for high-value tasks like RFP responses, investment research, and employee onboarding.

The implication: Current GenAI tools simply can't meet enterprise accuracy requirements. The breakthrough isn't making chatbots more accurate—it's building research systems that methodically mine enterprise data with multi-agent workflows.

20-30 Minutes: The Cost of Quality

Enterprise deep research typically takes 20-30 minutes to complete because it does substantial work underneath the covers—querying multiple data sources, cross-referencing information, validating facts, and building comprehensive responses.

"Usually something that takes about, you know, 20 30 minutes to complete because it does a lot of work underneath the covers."

The trade-off: For high-stakes enterprise decisions—investment recommendations, strategic RFP responses, complex compliance questions—accuracy matters more than speed. The time-to-answer is intentional, not a limitation.

Watch explanation (00:03:08)

What is Enterprise Deep Research?

Think about what happens when you ask a web research tool like Perplexity or ChatGPT Search a complex question. It doesn't just return a single answer—it deploys multiple agents in parallel, each researching different aspects, reflecting on findings, and synthesizing comprehensive results.

Enterprise deep research applies this proven pattern to private data.

"Think about it as exactly the same idea only now it goes to your private data."

— Ofer Mendelevitch, VP of AI at Vectara

Timestamp: 03:20

Multi-Agent Workflows

Multiple AI agents work in parallel, each researching different aspects of the query, reflecting on findings, and synthesizing comprehensive results.

Reflection Loops

Agents validate their own findings, cross-reference information, and refine responses through multiple iterations for maximum accuracy.

Parallel Execution

Multiple agents run simultaneously to query different data sources, dramatically reducing total processing time compared to sequential approaches.

Comprehensive Synthesis

Final responses combine insights from multiple agents with proper source attribution, confidence scoring, and fact validation.

Technical Architecture Quote

"Again the same process multi-agent with reflection with synthesis of the final results parallel execution of agents underneath and it queries your enterprise data of course using in this case Vectara agentic RAG capabilities."

Timestamp: 03:24 | Watch (00:03:24)

Trustworthy AI: The HHM Model

Vectara's approach to hallucination mitigation isn't theoretical—it's proven in production with massive adoption. Their Hallucination Detection Model (HHM) has achieved 5.5M+ downloads because it directly addresses the factual accuracy crisis.

5.5M+

HHM Model Downloads

"Our hallucination detection model, also called HHM, has just passed 5 mill downloads about couple months ago. I think it's at 5.5 right now or something like that."

— Ofer Mendelevitch, VP of AI at Vectara

Timestamp: 01:18

Hybrid Retrieval

Metadata filtering and reranking for precise data access. Ensures responses are based on the most relevant, accurate sources.

Why it matters: Simple vector search isn't enough for enterprise accuracy. Hybrid retrieval combines multiple approaches for precision.

Corpus Understanding

Systems understand what data is available before querying, enabling proper query planning—knowing which sources to query for which questions.

Why it matters: Prevents querying irrelevant sources and ensures comprehensive coverage of available knowledge.

Agentic RAG

Retrieval Augmented Generation with agent capabilities that understand context, user intent, and proper fact attribution.

Why it matters: Static RAG isn't enough. Agentic RAG adapts to query complexity and validates responses.

Multimodal Ingestion

Advanced ingestion makes images and tables searchable, not just text. Critical for enterprises with rich document formats.

Why it matters: Most enterprise knowledge lives in visuals (charts, diagrams, tables). Systems that can't process multimodal content miss massive value.

Real-World Use Cases That Matter

Enterprise deep research shines for complex, multi-source questions that span fragmented data ecosystems. Three killer use cases demonstrate the value.

RFP Response Generation

Responding to a 150-question RFP requires mining data from Jira tickets, Notion docs, Google Drive, SharePoint, and internal wikis.

Challenge

Scattered data sources

Solution

Parallel querying + synthesis

Value

Hours → Minutes

How deep research helps: Parallel querying across all data sources, synthesizing consistent, accurate responses, validating answers against source material, and maintaining proper attribution for fact-checking.

Employee Onboarding at Scale

New employees need answers that span HR policies, technical documentation, tribal knowledge in Slack, and project-specific context.

Challenge

Outdated documentation

Solution

Unified knowledge access

Value

Context-aware answers

Data sources: Jira, Notion, Google Drive, SharePoint, Slack, internal wikis. Deep research provides unified access, context-aware answers based on role/team, continuous updates as documentation changes, and proper redaction for sensitive information.

Investment Memo Generation

Investment firms need to synthesize information from analyst reports, news sources, financial databases, and internal research.

Challenge

Information overload

Solution

Cross-referenced synthesis

Value

Risk-flagged insights

Financial services applications: Comprehensive company overviews, cross-referenced data points, proper source attribution, and risk-flagged content with confidence scores. Similar patterns apply to healthcare and insurance industries.

Enterprise-Grade Platform Requirements

Deep research systems for enterprises can't be SaaS-only. The deployment model matters as much as the technology.

Deployment Flexibility

"It's a SAS platform, but also runs on your own VPC or on premise in your own data center."

Timestamp: 00:32 | Watch (00:00:32)

SaaS Deployment

Fastest time to value. Ideal for organizations without strict data sovereignty requirements.

VPC Deployment

Private cloud deployment. Required for enterprises with data security policies.

On-Premise Deployment

Full data control. Mandatory for regulated industries (healthcare, finance, government).

BYOM Capability

Bring Your Own Model. Flexibility to choose the best LLM for your use case and cost structure.

Enterprise Buying Requirements

Platform flexibility is mandatory, not optional. Enterprise buyers have legitimate constraints:

Role-based access controls (RBAC) for proper data governance
Observability and monitoring for production reliability
Audit logging for compliance and debugging
Data sovereignty requirements for regulated industries

Key Insights for Builders

Accuracy-First Architectures Win

If you're building enterprise AI tools, optimize for multi-agent workflows with reflection loops, comprehensive data retrieval, hallucination detection with confidence scoring, and proper attribution for fact-checking.

The takeaway: Don't optimize for chat speed or clever conversation flows. The next killer app isn't a better chatbot—it's a research engine that transforms scattered enterprise data into trustworthy insights.

Apply Proven Patterns to New Problems

The breakthrough isn't new AI technology—it's applying proven web research patterns (multi-agent workflows, reflection, synthesis) to private enterprise data instead of public web content.

The takeaway: This reframes the problem from "inventing new AI" to "adapting proven patterns"—a much more actionable insight for builders.

Hallucination Mitigation is Essential Infrastructure

HHM's 5.5M+ downloads prove that hallucination mitigation isn't a nice-to-have feature—it's essential infrastructure for enterprise AI adoption.

The takeaway: When a technical component achieves 5.5M+ downloads, it's become a standard. Builders who ignore HHM-style detection are building on foundations that enterprises won't trust.

Design UX Around Async Workflows

Deep research takes 20-30 minutes because it does comprehensive work. Don't sell this as "faster chat"—sell it as "comprehensive analysis that saves human hours."

The takeaway: The value proposition isn't speed—it's quality at scale. Design UX that communicates progress, manages expectations, and delivers comprehensive results worth waiting for.

Plan for Multi-Model Deployment from Day One

Enterprise buyers will veto any solution that doesn't offer VPC/on-premise options. Platform flexibility is a buying requirement, not a feature.

The takeaway: The technical debt of retrofitting on-premise deployment later is massive. If you're building enterprise AI, plan for SaaS, VPC, and on-prem deployment from the start.