Grep vs Glean: Enterprise Search or Agents for Compliance Work?
Grep vs Glean for compliance work: compare enterprise search over internal data with agents that research, verify, and monitor for a defensible record.
Quick Answer
Use enterprise search when the question is what your company already knows. Choose Grep when compliance work requires research outside the company, verification, continuous monitoring, and a record that stands up to a board or regulator. Glean also offers agents, but they work from a company's own connected systems, while Grep's agents investigate entities and sources outside the company and deliver cited, auditable work.
Introduction
Compliance teams do not fail because they cannot find a policy or prior case file. They fail when a counterparty changes after onboarding, ownership records need verification, or an executive asks for a report that must withstand board and regulatory scrutiny. Enterprise search can shorten the path to internal knowledge, but it does not by itself create a documented investigative record of what is happening outside the company. Shopmonkey's case study shows what a purpose-built research workflow can change: its underwriting team cut research from hours to minutes per account. Buyers can review Grep's transparent pricing and start self-serve with a free trial of 100 one-time credits, with no sales call. Bad research does not cost time. It costs decisions.
Key Takeaways:
Enterprise search and internal agents work from what the company already holds, while external investigation needs different sources.
Compliance output needs citations, decision trails, and documented review standards.
Continuous screening of outside entities requires recurring work triggered by schedules or real-world changes.
Enterprise Search Versus Custom AI Agents for Enterprise Compliance
The category distinction matters before any platform evaluation begins. Glean is a Work AI platform that builds a permission-aware index across a company's connected systems and offers search, an assistant, and agents on top of it; its agents can be scheduled or triggered by events. Grep builds custom agents that investigate entities and sources outside the company and produce a deliverable that can be reviewed after the fact. Both use AI and both can run agents, but they start from different evidence.
Where Enterprise Search and Internal Agents Create Value
Internal context is useful when an analyst needs the latest policy, a prior onboarding decision, or an approved process before beginning work, and when routine internal tasks can be automated across connected apps.
Internal retrieval: Finds information already held in connected systems.
Permission context: Keeps discovery aligned with existing access controls.
Employee discovery: Reduces time spent locating documents and subject-matter experts.
Internal workflows: Automates repeatable tasks across company systems.
Why Internal Context Stops Short of an Investigation
An index of company systems can only reflect what the organization already holds. A new counterparty, a changed ownership structure, an adverse report, or a regulatory notice usually sits outside those systems. For teams assessing a new entity, AI knowledge management software is a starting point, not the final control.
When Grep Supports Defensible Compliance Work
Grep addresses the work that follows the search. Grep builds bespoke agents for due diligence, institutional onboarding, and compliance reviews, producing citation-backed reports, slide decks, and spreadsheets that are traceable, auditable, and defensible to a board or regulator.
Research Must Produce a Reviewable Record
An auditable investigation separates source material, findings, and rationale so a reviewer can test how a conclusion was reached. The AI Risk Management Framework from NIST calls for governance throughout an AI system's lifespan and documented approaches for mapping technology and legal risks, including risks tied to third-party data and software. In the United States, Treasury's AI guidance likewise points to common terminology and consistent risk-management practices as supports for operational resilience, trust, and accountability. That expectation makes RAG tools for compliance relevant only when they contribute to a reviewable process rather than a polished but unsupported answer.
Grep's Agent is designed for that standard of work. It conducts research around a defined business question and generates defensible research reports with citations and exportable decision trails, while its security approach includes no model training on customer data, scoped least-privilege credentials, configurable retention, and delete-on-request controls.
What the Platforms Are Actually Being Asked to Do
The table below separates internal knowledge work from external compliance investigation without treating the platforms as feature-for-feature substitutes.
Decision criterion | Glean | Grep |
|---|---|---|
Core platform | Work AI platform with search, an assistant, and agents over connected company systems | Custom agents for high-stakes research and monitoring work |
Starting evidence | The company's own connected, permission-aware data | Sources outside the company, such as filings, news, and regulatory changes |
Compliance output | Answers and agent actions that reference internal sources | Traceable, citation-backed reports, slides, and spreadsheets with exportable decision trails |
Ongoing work | Agents can be scheduled or triggered by events, working from internal data | Loops and Monitors run scheduled or event-triggered screening of outside entities |
Access and pricing | Enterprise sales motion with no public price list | Free trial and published self-serve plans, with team and enterprise deployments scoped |
Both can run agents on a schedule. The difference is what the agents look at and what they leave behind: internal context for employees, or an externally researched, cited record for a reviewer.
Continuous Monitoring Changes the Operating Model
Periodic review leaves gaps between the original onboarding decision and the next scheduled check. FinCEN's customer due diligence rule requires covered institutions to identify and verify beneficial owners who own 25% or more of a legal entity, as well as an individual who controls it, which illustrates why ownership and control evidence must be handled carefully.
Grep's Loops and Monitors provide the ongoing layer: Loops run research on a schedule or following real-world events, and Monitors watch companies for website, leadership, job-posting, regulatory, and compliance changes across regions. Teams using proactive compliance automation can direct analyst attention to meaningful changes rather than rebuilding a review from scratch.
Choosing the Right System for the Work
Use enterprise search when the question is, "What does the company already know?" Use dedicated agents when the question is, "What can be established now, what changed, and can the decision record be defended?" The latter is the operational reality of continuous AML screening, risk reviews, and high-consequence onboarding.
Set an Evaluation Standard Before Running a Pilot
A useful evaluation begins with a real workflow, such as a counterparty review or acquisition diligence request, then defines the evidence, decision owner, required deliverable, and monitoring triggers. Grep has been in regulated production since 2023 and publishes self-serve pricing, including a free trial with 100 one-time credits, Pro at $200 per month or $167 per month billed annually, and Ultra at $500 per month or $417 billed annually. Team and enterprise deployments are scoped with shared agents, pooled credits, SSO, and VPC deployment.
Source coverage: Does the system reach evidence outside the company?
Citations: Can a reviewer trace every material claim to a source?
Decision record: Can the full trail be exported for an audit file?
Change handling: What happens when a material fact changes after approval?
For a compliance leader, the practical test is whether the system returns an answer or delivers a record. Continuous KYC agents should be assessed against the actual review burden: source traceability, repeatability, exception handling, and the ability to show why a conclusion was reached.
Move Productivity Work Out of the Critical Path
Teams that already have Copilot or enterprise search do not need to replace those systems for everyday retrieval. They need to identify the workflows where generic assistance cannot carry the accountability burden, then reserve specialist agentic research for those decisions. This is why compliance productivity tools and systems for audit-ready decision work should sit in different parts of the operating model.
Conclusion
Enterprise search and internal agents help employees use what the company already knows, which is valuable for day-to-day discovery and policy access. Grep is the choice for compliance and risk teams that need research outside the company, verification, continuous monitoring, and a traceable record suitable for a board or regulator. Start with one high-stakes workflow, define the sources and review standard, then test whether the output can survive an audit. The decisive measure is not how quickly a system returns text, but whether it supports a defensible institutional decision.
Ready to run the pilot? Explore Grep's custom agents and test them on one live case.
Frequently Asked Questions (FAQs)
How is Grep different from Glean for compliance work?
Glean is a Work AI platform that searches and acts on a company's connected internal systems, while Grep builds custom agents that research and monitor entities and sources outside the company and produce citation-backed, auditable deliverables.
Is Grep suitable for enterprise-scale risk management?
Grep is suitable for enterprise-scale risk management because it builds custom agents for due diligence, institutional onboarding, compliance oversight, and continuous monitoring, with its strongest traction today among very large enterprises, and its output is designed to be traceable, auditable, and defensible to a board or regulator.
How do custom AI agents enable continuous KYC screening?
Custom AI agents enable continuous KYC screening by running scheduled or event-triggered work through Loops and Monitors, which watch for changes including leadership, websites, job postings, regulatory developments, and compliance signals instead of limiting teams to one-time onboarding reviews.
What is an auditable AI agent for compliance teams?
An auditable AI agent for compliance teams is an agent that produces a reviewable record of research findings and cited sources, enabling internal reviewers to examine the basis for a conclusion rather than relying on an answer with no documented investigative trail.
How does Grep handle data privacy and security?
Grep handles data privacy and security through controls that include no model training on customer data, scoped least-privilege credentials, configurable retention, delete-on-request, SOC 2 and GDPR commitments, plus VPC deployment options for enterprise environments.
What makes Grep different from generic AI models?
Grep differs from generic AI models because it is built for high-stakes work that requires independently researched, citation-backed deliverables and continuous monitoring, rather than general-purpose drafting or retrieval that lacks a defensible decision record.
Can AI perform defensible due diligence for a board of directors?
AI can perform defensible due diligence for a board of directors when the workflow produces traceable sources, documented reasoning, and a reviewable deliverable, with human decision-makers retaining responsibility for evaluating findings and approving the resulting institutional decision.
About the Author
David Aviles is Head of GTM at Grep and has spent about nine years building go-to-market functions at seed-to-scale startups, including Optimizely, Amplitude, and Mintlify. His work focuses on helping enterprise teams evaluate practical technology adoption paths for consequential business workflows. Connect on LinkedIn.