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Despite the promise of agentic AI, some legal, tax, and risk professionals remain hesitant to adopt it. This paralysis is rooted in uncertainty, complexity, and a fear of falling behind without a clear path forward, leaving late adopters to fall behind exponentially.

Disconnect between future aspiration and current pace

Accuracy, budget, and security: perceived barriers to investment

The big five: Barriers to agentic AI adoption

Knowing your team’s roadblocks is the first step to overcoming them 

1. Paralysis by complexity

Organizations that are outside of the early adopter tier — those in “step zero”— are overwhelmed by the pace of change. They’ve heard the buzz around generative AI and agentic AI, but don’t know where to begin. Concerns about billable hours, client confidentiality, and talent disruption often cloud the conversation, leading to inaction. Those experiencing this paralysis by complexity can move forward by understanding the solutions that exist for their concerns. Such options include embedded security and privacy guardrails, human-in-the loop requirements, and strategies for re-allocating billable hours to other revenue-generating initiatives.

2. Misinformation

Early reports of AI hallucinations and unreliable outputs have left a lasting impression. These concerns, while valid in the early days of GenAI and particularly so in high stakes work such as law, accounting, and financial services, are often misapplied to today’s more advanced and secure agentic systems. Professionals may not realize that today’s strong agentic AI solutions operate in a controlled, enterprise-grade environment, far removed from the open web tools they’re wary of. For example, agentic systems developed by �������� are grounded in authoritative content curated for professional use. This training reduces the risk of hallucinations and helps ensure that outputs are aligned with legal, regulatory, and industry standards.

3. Workflow integration anxiety

Even organizations that have begun experimenting with AI often struggle with embedding it into their workflows. They fear AI will disrupt established processes or require significant retraining. However, agentic AI is designed to work like a junior professional that is capable of efficiently handling multistep work — but always under human oversight.

4. Lack of clear differentiation

With every vendor claiming to offer agentic AI, organizations are understandably skeptical and may lack a framework against which to evaluate their options. What separates a true agentic system from a glorified chatbot comes down to orchestration, evaluation, and memory. A legitimate agentic system uses large language models (LLMs) to call tools in a loop, make decisions, and adapt over time — something most off-the-shelf solutions can’t do. 

5. Endless debate over implementation vs. ROI

There is a realization within companies that inaction has a cost, but initial adoption will stall because they can’t determine which applications will generate the most return on investment (ROI) the fastest. They resist the idea of starting small, in low-risk environments, which is often the key to building confidence and forward movement within the company. 

This last point is essential. Because employees at any organization have different roles and varying levels of familiarity with agentic and GenAI, the easiest inroad to adoption is starting with more accessible tasks. From there, professionals can utilize more sophisticated applications that generate even more benefits and ROI.

Bridging the gap between wanting to implement agentic AI and making it a part of your everyday work begins with understanding what specific tasks the technology can assist with — determining use cases — and how that fits into standard workflows.

An essential underlying component of agentic AI — LLMs — is what makes these tools so accessible. LLMs use natural language, which means users can ask agentic AI questions or give directions, known as queries or prompts, in the same way they would when speaking to another person. Below are some examples of prompts a user might assign to their agent.

Reviewing a document

Scan, retrieve, analyze, summarize, and correct information

Legal, Review this NDA and extract any clauses regarding obligations of the receiving party. I also want you to flag risky or missing terms, summarize consequences of breach, and suggest edits based on state precedent or company policy.

Tax, Can you check for any potential errors in this document? Also, ensure correct deductions and credits, and analyze year-over-year changes. Make suggestions for improvement and explain why you made those suggestions.

Risk, For this document of customer profiles, assess the clients most at risk of defaulting on their loans. Prioritize them by risk level and explain your reasoning.

Checking compliance

Interpret information within the context of various regulations, requirements, and policies

Legal, Check this service agreement for compliance with California labor law, especially regarding non-compete and at-will employment clauses. Also, highlight any indemnity clauses that exceed our company’s standard liability threshold.

Tax, Review this fixed asset register for depreciation methods and confirm they comply with MACRS.

Risk, Review this batch of wire transfers for violations of our bank’s anti-money laundering thresholds. Is there any SAR-worthy activity worth flagging?

Researching and investigation

Find, analyze, and organize essential information

Legal, What are the current Federal Trade Commission (FTC) guidelines for advertising disclosures on social media?

Tax, What are the tax implications of intercompany loans under U.S. and Organisation for Economic Co-operation and Development (OECD) transfer pricing rules?

Risk, Cross-reference these IP addresses and phone numbers with known fraud cases or scam operations.

Drafting a document

Prepare customized and accurate documentation and templates

Legal, Draft a term of service agreement for a subscription-based mobile app, including a dispute resolution clause.

Tax, Create a cover letter for submitting an amended tax return to the IRS with an explanation of the changes.

Risk, Generate a fraud investigation report including findings, timelines, and recommended remediation steps.

Training and knowledge retrieval

Help junior associates advance their knowledge in particular areas and better understand processes faster.

Legal, Should I be using a unilateral or mutual non-disclosure agreement (NDA) for this case? Explain the difference and why I should use one and not the other.

Tax, How can I check whether a client is eligible for the research and development (R&D) tax credit? Break the process down into steps.

Risk, Walk me through how to perform a basic third-party risk assessment using a standard checklist.

Why are humans essential to agentic AI workflows?

Even though agentic AI can independently execute complex tasks concurrently, humans remain the drivers and safekeepers of the workflow, particularly in certain phases. This is referred to as human-in-the-loop interactions. Humans choose which prompts or queries to use, refine and iterate on those prompts by revising their requests or changing direction of the task, and review the final product to ensure accuracy, validity, and relevance.

Now that you’re familiar with the types of tasks agentic AI can complete and how to initiate them, let’s look at what entire processes might look like for legal, tax, and risk use cases, including the human-in-the-loop interaction.

Legal professionals are under increasing pressure to deliver faster, more accurate work while navigating growing complexity across jurisdictions, practice areas, and client demands. Agentic AI offers a transformative solution by automating multistep legal workflows while keeping attorneys in control. Unlike traditional automation or standalone generative tools, solutions powered by agentic AI such as can plan, adapt, and execute tasks across platforms, surfacing insights that might otherwise be missed. This specialization enables law firms to outcomes with greater speed and confidence.

Use case #1: Policy drafting

One of the most tangible examples of how agentic AI can transform legal workflows is policy drafting. Today, when an attorney needs to draft an employee policy, such as a data privacy or workplace conduct policy, they must manually locate a relevant template, research applicable state laws, and tailor the language to their organization's specific needs. This process can take hours or even days, especially when compliance across multiple jurisdictions is involved.

With agentic AI, this workflow becomes dramatically more efficient. The attorney begins by entering a natural-language prompt into the system, such as, “Draft a workplace marijuana policy for a company with 500 employees operating in California and Colorado.” The AI agent then initiates a multistep process:

1. Planning and research. The agent identifies the relevant legal frameworks in both states, pulling from Practical Law’s jurisdiction-specific guidance and Westlaw’s primary law content. It determines which provisions are required, optional, or prohibited in each jurisdiction. 
2. Template selection and customization. Based on the user’s input and legal research, the agent selects a base template and begins customizing it. It incorporates the user’s parameters such as company size, location, or policy goals, and adjusts the language accordingly. 
3. Draft generation. The agent produces a first draft of the policy, complete with citations to the underlying Practical Law content that informed each clause. This “show your work” approach allows the attorney to verify the legal basis for each provision. 
4. Human-in-the loop iterative refinement. If the agent identifies gaps, such as missing information, it prompts the user for clarification. This check ensures the final output is tailored, accurate, and aligned with the firm’s needs. 
5. Human-in-the-loop review. Before finalizing the draft, the agent presents a summary of its plan and key decisions to the user. The attorney can review the draft, request revisions like, “Add a section for unionized employees”, or provide missing information. 
6. Training and knowledge transfer. For junior attorneys, this workflow doubles as a learning experience. The citations and linked content provide context and rationale, helping them understand not just what the policy says, but why it says it. 

This same agentic framework can be extended to other legal workflows, such as preparing litigation documents. In each case, the agent handles the heavy lifting — research, synthesis, drafting — while the attorney guides, reviews, and validates the output.

Use case #2: Incorporating a company

Incorporating a company is a multistep legal process that involves selecting the appropriate entity type, preparing and filing formation documents, drafting bylaws, registering for tax IDs, and ensuring compliance with jurisdiction-specific regulations. Traditionally, this process requires attorneys to manually coordinate across multiple systems, reference various legal sources, and manage client communications.

With agentic AI, the process becomes orchestrated, adaptive, and significantly more efficient. The workflow might look like this: 

1. Initial prompt and planning. The attorney begins by entering a prompt such as, “Incorporate a Delaware C-Corp for a software-as-a-service (SaaS) company with three founders and remote employees in five states.” The agentic AI system interprets the request and generates a step-by-step plan, outlining the necessary filings, documents, and compliance checks.
2. Tool orchestration and content retrieval. The agent accesses internal tools and trusted content sources such as Practical Law and Westlaw to retrieve jurisdiction-specific requirements, entity type comparisons, and filing procedures. It selects the appropriate templates and legal forms based on the user’s input. 
3. Human-in-the-loop review of the plan. Before proceeding, the agent presents the incorporation plan to the attorney, including the rationale for each step and links to the legal sources used. The attorney can approve the plan, request modifications, or provide additional context such as, “Include a clause for founder vesting schedules”.
4. Execution with adaptive prompts. As the agent begins drafting documents and preparing filings, it may encounter missing information, such as the company’s registered agent or the founders’ equity split. It pauses and prompts the attorney to supply the missing details, ensuring accuracy and compliance.
5. Draft generation and validation. The agent generates the incorporation documents, including the certificate of incorporation, bylaws, and initial board resolutions. Each document includes citations to the legal sources that informed its content, allowing the attorney to trust but verify results.
6. Iterative refinement and filing. The attorney reviews the drafts, makes any necessary edits, and instructs the agent to proceed with filing. The agent can then prepare the documents for submission, depending on integration capabilities.
7. Training and knowledge transfer. For junior attorneys, this workflow serves as a training tool. By reviewing the agent’s plan, citations, and drafting logic, they gain insight into the incorporation process and the legal reasoning behind each step.
8. Future expansion. As agentic capabilities mature, this workflow could expand to include post-incorporation tasks such as issuing stock, setting up cap tables, and registering for state-specific business licenses, further reducing manual effort and increasing consistency.

This scenario highlights how agentic AI can turn a traditionally fragmented and manual legal process into a streamlined, intelligent workflow that takes a fraction of the time. It also reinforces the importance of human oversight, not just for validation, but for guiding the AI with strategic decisions and nuanced judgment.

One of the most promising applications of agentic AI in tax and accounting is in tax advisory services. This workflow, which traditionally involves a mix of manual research, spreadsheet modeling, and client communication, evolves into a guided, intelligent process that accelerates outcomes while maintaining professional discretion. 

Use case #1: Tax strategy planning and execution

A certified public accountant (CPA) advising a small business client on how to reduce their tax liability for the upcoming fiscal year typically begins by reviewing the client’s financials, identifying applicable tax strategies, modeling potential outcomes, and preparing a proposal. This process is time-consuming, error-prone, and heavily reliant on the advisor’s ability to synthesize complex tax code and firm-specific data. With agentic AI, the workflow becomes structured, adaptive, and significantly more efficient:

1. Client intake and document review. The CPA uploads the client’s financial documents, such as prior tax returns, income statements, and ownership structure, into the system. The AI agent reviews the documents, extracts relevant data such as revenue, entity type, or number of employees, and flags any missing or inconsistent information. The CPA then uploads that additional information, links to it, or responds directly to the agent with the needed data. 
2. Strategy identification and eligibility assessment. The agent then evaluates the client’s profile against a library of tax strategies — income shifting, entity restructuring, or retirement plan contributions. It determines which strategies are applicable based on eligibility criteria and regulatory thresholds, using embedded logic derived from the agentic platform’s content, such as Checkpoint. 
3. Impact modeling and proposal drafting. For each viable strategy, the agent calculates the estimated tax impact and generates a draft proposal. This includes a summary of the strategy, projected savings, and a step-by-step implementation plan. The proposal is linked to authoritative sources, allowing the CPA to confirm the recommendations. 
4. Human-in-the-loop review and customization. Before sharing the proposal with the client, the CPA reviews the agent’s output. If needed, they can adjust assumptions, add client-specific context, or request clarification from the agent like, “What assumptions were used in the income shifting model?” The agent responds with citations and reasoning, enabling the CPA to better understand and explain rationale to the client. 
5. Guided execution workflow. Once the client approves a strategy, the agent initiates a guided workflow to implement it. For example, if the strategy involves forming a new limited liability company (LLC), the agent outlines the required steps —filing articles of organization, updating payroll systems, notifying stakeholders — and tracks progress. At each step, the agent prompts the CPA for inputs or confirmations such as “Upload the signed operating agreement”. 
6. Compliance and documentation. Throughout the process, the agent ensures all actions are documented and compliant with relevant tax codes. It generates audit-ready records and flags any deviations from standard procedures. 
7. Training and knowledge transfer. For junior accountants, this workflow serves as a built-in training tool. The agent’s explanations, citations, and structured guidance help them understand not just what to do, but why. This explanation accelerates the learning curve and reduces reliance on senior staff. 

Here, we see how agentic AI can turn a complicated advisory process into a task requiring far less effort. By pairing automation with expert content and human oversight, tax professionals can deliver faster, more accurate, and more strategic advice while maintaining the trust and rigor their clients expect.

Use case #2: Document review and compliance checks in tax advisory

In addition to planning and executing tax strategies, CPAs often need to review a client’s financial documents to ensure completeness, consistency, and compliance with applicable tax regulations. This process is largely manual and includes the comparison of multiple documents, cross-referencing with tax codes, and identifying missing or conflicting information. With agentic AI, this document review process becomes more intelligent, proactive, and guided: 

1. Document upload and preprocessing. The CPA uploads a set of client documents, such as W-2s, 1099s, prior returns, and financial statements, into the system. The AI agent scans and extracts relevant data points, including income, deductions, and entity structure.
2. Automated review and flagging. The agent reviews the documents for completeness and consistency. It flags missing forms such as “No Schedule C found for reported self-employment income”, or inconsistencies like, “Income reported on 1099 does not match bank statement deposits”.
3. Compliance validation. The agent checks the extracted data against current tax regulations using embedded logic and authoritative content from Checkpoint. It identifies potential compliance issues or opportunities including, “Client may qualify for R&D tax credit based on expense patterns”.
4. Human-in-the-loop interaction. When the agent encounters ambiguous or incomplete information, it prompts the CPA for clarification. For example, “Please confirm whether the client has any dependents listed on their return”. The CPA can upload additional documents or enter responses directly.
5. Summary report generation. Once the review is complete, the agent generates a summary report outlining findings, flagged issues, and recommended next steps. Each item is linked to the relevant source document and regulation, enabling the CPA to sign off the output or return it to the agent for revision.
6. Training and knowledge transfer. The agent’s explanations and citations help new employees understand the rationale behind each compliance check and how to interpret tax documentation more effectively.

This scenario demonstrates how agentic AI can enhance accuracy, reduce manual effort, and improve audit readiness in tax and accounting workflows. By combining document intelligence with regulatory awareness and human oversight, it enables professionals to deliver higher-quality outcomes with greater confidence.

Agentic AI can dramatically accelerate and enhance the risk and fraud investigation process by automating the data gathering, synthesis, and risk flagging steps, while keeping the investigator in control of key decisions. 

Use case #1: Investigative due diligence for a high-risk entity at a bank 

Risk and fraud professionals — in both corporations and government agencies — often face the challenge of conducting deep due diligence on individuals or entities that may pose reputational, financial, or legal risk. This process involves gathering data from disparate sources, verifying identities, uncovering hidden relationships, and finding red flags such as criminal records, sanctions, or financial irregularities. With agentic AI, this investigative workflow becomes more intelligent, iterative, and auditable:

1. Initial prompt and objective setting. The investigator begins by entering a natural-language prompt such as, “Conduct a due diligence investigation on John Doe, a potential vendor based in Miami, with suspected ties to offshore entities.” The agent interprets the objective and generates a multistep plan to gather, analyze, and synthesize relevant data.
2. Data aggregation and identity resolution. The agent accesses a wide range of public and proprietary data sources — court records, corporate registries, sanctions lists, and news archives — to build a comprehensive profile. It uses entity resolution techniques to distinguish between individuals with similar names and confirm identity through cross-referenced attributes like date of birth, address history, and known associates.
3. Relationship mapping and risk flagging. The agent constructs a network map of the subject’s known business affiliations, associates, and historical transactions. It flags potential risks such as links to sanctioned entities, prior fraud investigations, or shell companies in high-risk jurisdictions. These insights are surfaced in a visual dashboard or structured report.
4. Human-in-the-loop review and refinement. At key decision points, the agent prompts the investigator to validate assumptions or provide additional context. For example, if the agent finds multiple John Does in Florida, it may ask, “Is your subject associated with XYZ Holdings LLC?” The investigator can confirm or correct the path, ensuring the AI stays on track. If the investigator doesn’t know, the agent can suggest alternative paths to uncover further information.
5. Narrative report generation with source attribution. Once the investigation is complete, the agent generates a narrative report summarizing findings, supported by citations and links to original source documents. This allows the investigator to verify each claim and ensure transparency in how conclusions were reached.
6. Iterative exploration and scenario testing: If new information emerges, such as a tip about a related entity in Panama, the investigator can prompt the agent to expand the scope. The agent adapts its plan, retrieves new data, and updates the report accordingly, maintaining a clear audit trail of changes.
7. Training and knowledge transfer. For junior analysts or new team members, the agent’s explanations, source links, and structured logic help them understand investigative best practices and regulatory frameworks. This helps them quickly become familiar with ways to overcome roadblocks or useful ways to find additional information.

The above scenario demonstrates how agentic AI can enhance the speed, depth, and reliability of risk investigations. By automating the labor-intensive aspects of data gathering and synthesis — all while keeping the human expert in control — agentic systems empower professionals to make faster, more informed decisions with confidence.

Use case #2: Investigative lead development for law enforcement

Police investigators are often tasked with identifying and assessing individuals or entities that may be involved in criminal activity, fraud, or public safety threats. These investigations usually involve pulling together fragmented data from multiple sources — such as arrest records, court filings, social media, business affiliations, and geographic movement patterns — to build a comprehensive picture of a subject’s background and potential risk. 

1. Initial prompt and objective setting. An officer begins by entering a prompt such as, “Investigate Marcus Taylor, a suspected gang affiliate operating in Atlanta and Dallas, with possible ties to synthetic drug distribution.” The agent interprets the objective and generates a multistep plan to gather relevant data, assess risk, and surface actionable leads.
2. Data aggregation and identity resolution. The agent pulls from a wide array of public and proprietary sources including arrest records, incarceration history, known aliases, vehicle registrations, and property ownership. It uses entity resolution to distinguish between individuals with similar names and confirm identity through cross-referenced attributes such as date of birth, known associates, and prior addresses.
3. Network mapping and behavioral pattern analysis. The agent constructs a network of known associates, flagged addresses, and prior incidents. It identifies patterns such as frequent travel between high-risk areas, connections to previously investigated individuals, or overlapping phone numbers and vehicles. These insights are visualized in a relationship map or structured report.
4. Human-in-the-loop review and decision points. At key junctures, the agent prompts the officer for clarification or confirmation. For example, if multiple individuals named Marcus Taylor are found, the agent may ask, “Is your subject associated with a 2021 arrest in Fulton County?” The officer’s input helps refine the investigation and ensure accuracy. If the officer doesn’t know, they can ask the agent for more details about that arrest to make a determination.
5. Narrative report generation with source attribution. Once the investigation is complete, the agent generates a narrative report summarizing findings, including citations and links to original source documents. This allows the officer to verify each claim and remain clear about how conclusions were reached.
6. Scenario expansion and real-time monitoring. If new intelligence emerges, such as a tip about a recent arrest or a new alias, the officer can prompt the agent to update the narrative and any ensuing investigation leads to keep them as up to date as possible. The agent adapts its plan, retrieves new data, and updates the report accordingly. In future iterations, the agent could also monitor and alert for real-time updates, such as new bookings or court filings.
7. Training and knowledge transfer. For newer crime analysts, the agent’s explanations, source links, and structured logic help them understand investigative best practices, legal thresholds, and how to interpret complex data sets.

This real-life use case demonstrates how an AI agent can support law enforcement by reducing the time and effort required to build a case file while improving the depth and reliability of the analysis. By automating the labor-intensive aspects of data gathering and synthesis — and prompting for human judgment at critical points — agentic systems can help law enforcement keep up with increased incidents and more sophisticated criminals.

Agentic AI is a force multiplier, but success doesn’t come from adopting the technology alone. Organizations must implement it with intention, align it with company strategy, and measure its impact in ways that go beyond surface-level efficiency. To do all this, companies should use several tactics to improve success in the short- and long-term.

First, focus on starting small and thinking big. Begin with familiar, high-impact use cases — summarizing depositions, analyzing tax data, automating compliance checks — and use those early wins to build confidence and momentum. Don’t view these steps as pilot projects — consider them strategic footholds. The key is to tie adoption directly to business priorities and roll out agentic workflows with a clear plan for change management, training, and support.

This kind of transformation doesn’t happen in silos. The most successful implementations are cross-functional. They involve leadership, IT, pricing, and talent management working together to define what success looks like and how to get there. Those that treat agentic AI as an organization-wide initiative and not solely as a tech upgrade are the ones seeing the greatest returns. In fact, legal professionals expect to free up nearly 240 hours per year through AI adoption, unlocking an average annual value of $19,000 per professional, according to the Future of Professionals Report 2025. Across the U.S. legal and tax sectors, that translates to a $32 billion opportunity.

When thinking about ROI, it’s natural to default to first thinking about time saved. Take it further and consider how your strategy will use returned time. Some professionals are using it to take on more work, others to shift toward fixed-fee billing models, and still others to deepen client relationships. The most forward-thinking organizations are measuring success through a combination of leading and lagging indicators. This option includes time saved per task, increased capacity, usage metrics, and ultimately, financial outcomes such as improved margins, increased revenue per professional, or greater client value realization. The companies that have found the most success with agentic AI both track these metrics and have a strategy for turning efficiency into value.

Choosing the right partner is just as critical as choosing the right use case. Organizations need more than a vendor; they need a collaborator who brings domain expertise, robust security, and a long-term commitment to innovation. Don’t think only in terms of the agent but about the entire ecosystem surrounding it — content, tools, and guidance.

For this reason, teams are increasingly looking to providers who can offer the training, support, and integration needed to make this technology work in the real world. The pace of change is fast, and the cost of inaction is rising. Thirty percent of professionals believe their firms are moving too slowly on AI, even as 80% expect it to have a transformational impact within five years. The firms that will thrive are those that embrace adaptability, foster a culture of experimentation, and build systems that are designed to evolve. The goal isn’t to resist change — it’s to design for it.

The �������� Future of Professionals Report 2025 outlines a four-layer pyramid defining organizational foundation for successful AI adoption. At the top is strategy, where organizations articulate a clear vision for how AI will drive value across the business. This strategic clarity must be championed by leadership. The second layer plays a critical role in setting priorities, allocating resources, and modeling adoption. The third layer, operations, ensures that the right infrastructure, workflows, and governance are in place to support scalable implementation. At the base of the pyramid are individual users — the professionals whose daily engagement with AI tools ultimately determines the success of the initiative. Each layer reinforces the others, creating a cohesive framework enabling teams to move from experimentation to enterprise-wide transformation. 

To learn more about crafting an effective agentic AI implementation strategy, read, Are you future-proofed? Steps to prepare for agentic AI in professional services.

Agentic AI is not a silver bullet, but it is a powerful tool. When implemented thoughtfully, it can transform workflows, elevate client service, and unlock new levels of performance. Those that succeed won’t be the ones that simply adopted it the fastest, they’ll be the ones that also adopted it in the smartest way.

�������� offers the most comprehensive and future-ready AI solution available for professionals who are looking to evolve under the weight of growing complexity and role expectations. It empowers professionals to work more accurately, thoroughly, and quickly by orchestrating entire workflows across research, drafting, analysis, and review. 

�������� isn’t just ready for what’s next — it’s building it. That’s why professionals who begin using CoCounsel and CLEAR today will be ready to apply agentic AI to more advanced use cases as new features and capabilities emerge.

CoCounsel Legal

The most advanced single AI tool for quickly and accurately completing complex legal work is —the AI solution of choice for 78% of the Am Law 100 and many courts across the US federal court system.

• Applies guided agentic AI across the full legal workflow
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Transform core workflows with intelligent support for research, document analysis, advisory work, and audit testing with .

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CLEAR

Use to empower risk and fraud professionals to make data-driven decisions and conduct faster, more accurate investigations by aggregating and analyzing data from thousands of public and proprietary sources.

• Connects to thousands of public and proprietary data sources
• Uses AI to resolve identities and uncover hidden relationships
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• Visualizes networks of people, businesses, and assets
• Provides audit-ready transparency with source linked results
• Speeds up investigations while maintaining accuracy and control
• Trusted by law enforcement, corporations, and public agencies