Introducing Enterprise Deep Research

Introduction

Deep Research is an advanced feature available in OpenAI’s ChatGPT, Anthropic’s Claude, Google's Gemini, or even as an open-source tool with Together.AI’s open-deep-research. Deep Research acts as an autonomous research assistant, planning and executing multi-step investigations by browsing, analyzing, and citing information from across the web to produce analyst-level insights.

Deep Research can explore hundreds of websites, review documents in multiple formats, and generate detailed, multi-page reports complete with source attributions - turning hours of manual research into minutes.

Here’s an example. Imagine you need to do research on the influence of AI-generated content on journalism and authorship. You can simply use a prompt similar to this one:

💡 Example DeepResearch Prompt

Conduct a comprehensive literature review on the influence of AI-generated content on journalism and authorship, summarizing the key theories, recent advancements, major debates, and identifying gaps in current research. Include references to at least 10 peer-reviewed sources.

In about 10-20 minutes, Deep Research comes back with a very detailed report on this topic.

While revolutionary, Deep Research tools are inherently limited to public information, leaving your most valuable asset - your internal data - untouched.

Vectara Enterprise Deep Research (EDR) overcomes this boundary. It applies the same rigorous, multi-step analysis to your private enterprise content, allowing you to generate analyst-grade insights from the information you already own.

Enterprise Deep Research

There are many use cases that are enabled by Enterprise Deep Research, such as compliance monitoring, preparing a legal or corporate strategy brief, sales enablement, or even real-world evidence synthesis for pharmaceutical companies.

In this blog post, we will focus on continuous compliance monitoring, which enables proactive and automated monitoring of an organization's adherence to regulatory requirements and internal policies. This methodology involves the constant collection and analysis of data from across the enterprise's IT systems to identify and address potential compliance issues in real time.

Continuous compliance monitoring provides a dynamic and real-time view of an organization's compliance posture, and enables immediate remediation of vulnerabilities and non-compliant activities, thereby minimizing the risk of penalties, reputational damage, and security breaches.

In essence the organization becomes "audit-ready" at all times.

This is an especially important use-case for companies in regulated industries like financial services, healthcare, or insurance, which are often required to comply with tens or even hundreds of disparate regulations.

Enterprise Deep Research is a foundational enabler of robust, continuous compliance monitoring. Your GRC (Governance, Risk, and Compliance) team can determine which data sources are required for continuous monitoring (e.g., email communications, shared file stores like Google Drive or S3, system access logs, or transaction records) and after ingesting this data into Vectara, getting a daily report on compliance posture becomes an easy task with Enterprise Deep Research.

Here is an example Enterprise Deep Research prompt you can use:

💡 Example Enterprise Deep Research Prompt

You are the Chief Compliance Officer for <Company> with a significant and growing customer base in the EU, particularly Germany and France. Our products process personal data of our clients' employees and customers, including names, email addresses, IP addresses, and usage analytics.

Please conduct a deep analysis of our GDPR compliance posture. Your research should focus on the following key areas of compliance monitoring:

Data Processing Agreements (DPAs): Analyze the latest requirements for DPAs with our sub-processors (e.g., cloud hosting providers, analytics services). Identify any recent changes stemming from court rulings like Schrems II and the new Standard Contractual Clauses (SCCs). What are the best practices for continuous monitoring of sub-processor compliance?
Data Subject Access Requests (DSARs): Detail a best-in-class workflow for handling DSARs, from initial request verification to data retrieval and delivery. The research should identify common pitfalls and provide metrics and KPIs we can use to monitor the efficiency and completeness of our DSAR fulfillment process.
Record of Processing Activities (ROPA): Outline a comprehensive and scalable methodology for maintaining our ROPA under Article 30. What automated tools are market leaders using to link data flows to specific processing activities and legal bases?
Data Protection Impact Assessments (DPIAs): Provide a detailed framework for conducting DPIAs for new product features that involve processing sensitive personal data or using novel technologies like AI/ML for user profiling. What are the specific triggers for a DPIA according to the latest guidance from the EDPB and French (CNIL) and German data protection authorities?
Breach Notification: Research the most recent enforcement actions related to data breach notifications under GDPR. What constitutes an 'undue delay' in reporting? Provide a checklist for our incident response team to use for evaluating a security incident and determining if it meets the threshold for notification to a supervisory authority and affected data subjects.

The final output should be a comprehensive report with actionable recommendations, checklists for each area, and examples of best-in-class documentation and procedures from comparable companies in the B2B SaaS industry.

With access to all your product and engineering data, as well as contracts and agreements, the report comes back with a very detailed analysis of the compliance posture on GDPR. The compliance officer can then review the generated report, make any necessary changes, and submit it for review to the auditing body.

How does Vectara EDR work?

The general flow of web-based Deep Research is well documented in the industry (e.g. here) and works as follows:

A "Research Head” analyzes the user’s query and creates a plan for how to obtain all the necessary information to build the desired report to answer the user's question.
The “Research Head” then spawns a few “Research Assistant” agents to execute this plan in parallel
Once all the information is collected, the "Research Head" agent compiles this information into a final report in the style and tone requested.

All of these steps are often performed using a web search tool to collect information and an LLM to drive the Agentic behavior of the research head and its assistants.

Applying this process to enterprise data, however, presents a new challenge.

To build a research plan, the "Research Head" agent must formulate key topics to pursue, while its "Research Assistants" must use specific questions to obtain information. The problem is, with no prior exposure to the enterprise data they're working against, these agents lack the foundational knowledge to define those topics or formulate the right questions, hindering their ability to compile a final report.

To address this “cold-start” problem, we added specific capabilities for Vectara Enterprise Deep Research to enable “Deep Research Corpora Introspection” - the ability to quickly inspect the contents of Vectara corpora and understand the data contained within them. This is critical for developing a research plan that is not only relevant to the user query, but also achievable based on the information available in your company's internal documents.

Thus, the Enterprise Deep Research flow looks as follows:

Image 1: Enterprise Deep Research Flow

Understand Corpora: a specialized introspection process designed to provide a good summary of the information in the corpora, available metadata fields, and other related information.
Prepare: perform initial research based on the introspection phase, and come up with clarification questions; collect user responses and finalize the research plan.
Research: perform the research using multiple agents in parallel.

The introspection step, performed during the “Understand Corpora” stage, ensures that the research plan is grounded in the internal (private) data, and that the head researcher and research assistants are provided with the proper context to return trusted results that can ultimately be compiled into the overall report requested by the user.

While standard Deep Research uses the public web only, Enterprise Deep Research can be used exclusively with private data or combine the power of external web search with the rich context of your own internal data, surfacing insights you simply couldn’t find otherwise.

Real-World Example: Email Compliance Monitoring

To demonstrate how Enterprise Deep Research using Vectara can help compliance and in-house legal staff, we’ve ingested the complete Enron emails dataset into Vectara (about 500K emails). Each email includes the date, sender, receiver and of course, the text of the email itself.

We then generated a full report using the following prompt:

💡 Enron DeepResearch Prompt

Write a comprehensive investigative report analyzing potential SEC violations committed by Ken Lay and Enron Corporation in the year 2000. The report should identify and cite specific clauses or statements from SEC regulations that may have been violated. For each potential violation, provide concrete evidence from Enron's internal email communications, including the sender, recipient, and timestamp of each email, along with a summary or quotation and an explanation of how the communication relates to the specific regulatory clause. The report should also include a detailed timeline of key events involving Ken Lay, highlighting collaborators who were involved in or aware of the violations, describing their roles, actions, or decisions, and assessing the overall impact of these activities on the company's operations, reputation, and legal standing.

The result is a detailed report based on a deep analysis of all emails included, highlighting the main compliance violations by Ken Lay, as well as providing specific evidence from the emails and a timeline of key events.

The full report is quite long, so we won’t post it here. Instead let’s look at a few key snippets.

First, the research report includes the following violation types:

Off-Balance Sheet Special Purpose Entities and Financial Reporting Violations
Securities Fraud and Material Misstatements Under Rule 10b-5
Periodic Reporting Violations and Disclosure Failures
Market Manipulation and Trading Violations
Insider Trading and Executive Stock Transactions
Political Influence and Disclosure Violations
Corporate Governance Failures and Executive Responsibility

Arguably one of the most egregious violations of law by Ken Lay was Securities Fraud, and in fact, he was convicted on three counts of securities fraud, which involved making false and misleading statements about Enron's financial condition to manipulate stock prices and encourage investment, even as the company's financial situation was deteriorating.

Here’s what the Vectara Enterprise Deep Research report had to say about securities fraud:

💡 Enron Deep Research Results (sample)

Regulatory Framework

Rule 10b-5 (17 CFR §240.10b-5): Prohibits any device, scheme, or artifice to defraud; making untrue statements or omissions of material fact; and any act operating as fraud in connection with securities transactions.
Rule 10b5-1: Defines insider trading as trading “on the basis of” material nonpublic information.

Evidence and Analysis

Enron’s 2000 financial disclosures violated Rule 10b-5 by:

Using SPEs and off-balance-sheet entities to hide debt and inflate earnings.
Omitting material facts about declining demand in key business units and conflicts of interest involving CFO Andrew Fastow’s partnerships.
Engaging in fraudulent business practices, including delayed asset write-downs and misleading investor communications.
Executives, including Ken Lay, sold stock while in possession of material nonpublic information.

Internal emails from late 2001 and early 2002, including whistleblower warnings and legal memos, confirm senior management’s awareness of these fraudulent practices.

The report also included a detailed breakdown of specific emails related to violations of Rule 10b-5 in the following table:

Date	Sender	Recipient	Summary	Relation to Rule 10b-5
2001-08-15	Sherron Watkins	Ken Lay	Warned of inflated profits and accounting abuses involving SPEs and partnerships.	Clause (a), (b), (c) violations
2001-10-23	Frank Hayden	Internal Distribution	Discussed class action suit alleging material misstatements inflating stock price.	Clause (b) misstatements and omissions
2001-10-25	Julie Armstrong	Danny McCarty	Detailed concerns about overstated earnings and failure to timely write down impaired assets.	Clause (b) false financial reporting
2002-01-02	Sarah Palmer	Internal Distribution	Summarized legal and reputational fallout from misstatements.	Violations of all three Rule 10b-5 clauses

The Power of Enterprise Deep Research Agents

Enterprise Deep Research transforms how you explore complex topics, combining web data with the critical context of your private company data. It represents a new kind of AI agent that is generic enough to address many enterprise use-cases and uses the full power of agentic RAG.

Deep Research for the enterprise opens up a massive opportunity for companies to explore complex topics in depth, grounded with their own internal documents and knowledge bases, elevating decision-making and democratizing this kind of in-depth intelligence once reserved for expert analysts to any employee in the company.

Many activities that require deep analysis using foundational material for decision making, such as putting a market analysis report, creating an investment memo, preparing for a sales presentation, or responding to an RFP, go from hours or days with multiple sparse and expensive experts involved, to a matter of minutes.

We are super excited about the power that Vectara Enterprise Deep Research brings and will share additional use-cases soon. If you’d like to learn more about specific use cases for your company, please contact our team for a demo.