Automate Conflict Check Process: Build an AI Agent for Professional S…

If you've ever worked in a law firm's conflicts department—or worse, been the solo practitioner trying to do your own conflict checks at 11pm—you already know the drill. Someone wants to open a new matter. You need to make sure representing this client won't create an ethical catastrophe with an existing or former client. What should be a simple yes/no question turns into hours of searching, cross-referencing, emailing attorneys who won't respond, and manually Googling corporate subsidiaries until your eyes blur.

The whole process is begging to be automated. Not the judgment calls—those stay human. But the massive amount of grunt work that precedes those judgment calls? An AI agent can handle most of it, and it can do it in minutes instead of days.

Here's exactly how to build one using OpenClaw.

The Manual Conflict Check Workflow (And Why It Takes So Long)

Let's be specific about what actually happens when a new matter comes in at a mid-to-large professional services firm. This applies to law firms primarily, but accounting firms, consultancies, and investment banks run similar processes.

Step 1: Intake. Someone fills out a new matter form—client name, related parties, adverse parties, matter description, relevant keywords. At many firms, this is still a Word document or a basic web form that dumps into an email inbox. Time: 10–30 minutes.

Step 2: Database Search. The conflicts team runs the client name, adverse parties, and related entities through whatever system they have—Intapp, Aderant, a custom SQL database, or in plenty of cases, an Excel spreadsheet. They search for exact matches and try to catch variations. "Acme Corporation" vs. "Acme Corp." vs. "ACME LLC." Time: 15–45 minutes for simple matters.

Step 3: Corporate Family Tree Research. This is where things get ugly. If the client or adverse party is a corporation, you need to figure out who owns them, who their subsidiaries are, and whether any related entity has a relationship with your firm. People do this by searching EDGAR filings, Capital IQ, Bloomberg, OpenCorporates, or just plain Googling. For a PE-backed company with 40 portfolio companies? Good luck. Time: 30 minutes to 4+ hours.

Step 4: Attorney Certifications. Every attorney staffed on the matter—and sometimes all attorneys in relevant practice groups—needs to confirm they have no personal conflicts. Former clients from a prior firm, personal investments, family relationships. This means sending emails or questionnaires and then chasing people who don't respond. Time: 1–5 days of elapsed time, mostly waiting.

Step 5: Review and Analysis. The conflicts team or an ethics partner reviews all the search results, filters out false positives, and determines whether any hit represents an actual or potential conflict under the applicable rules (ABA Model Rules 1.7–1.10 for lawyers). Time: 30 minutes to several hours.

Step 6: Escalation, Waiver, or Decline. If there's a real conflict, someone drafts a memo, the ethics committee weighs in, and the firm either seeks a client waiver, builds an ethical wall, or declines the matter. Time: 1–10+ days.

Step 7: Documentation. Log the decision. Update the system. Move on.

Total time for a simple matter: 30–90 minutes of active work, but often 1–2 business days of elapsed time. For a complex matter (multinational client, M&A, PE fund): 4–20+ hours of active work, 5–10 business days elapsed. An Intapp study from 2022 found that 68% of firms reported complex conflict checks taking more than two business days. One AmLaw 100 firm estimated they were losing $2.8M–$4.5M per year in delayed matter openings alone.

What Makes This Painful

The time is the obvious problem, but it's not the only one.

False positives drown the team. Bad data quality, duplicate records, and unsophisticated name matching mean the conflicts team spends most of their time looking at results that aren't actually conflicts. When your system returns 200 hits for "Johnson" and 190 of them are irrelevant, you've got a filtering problem, not a conflicts problem.

False negatives are where the real risk lives. A fuzzy match system that can't connect "北京科技有限公司" with "Beijing Technology Co., Ltd." is a system that misses conflicts. A 2026 Law.com article described a firm whose AI pilot caught three conflicts that traditional search missed because of spelling variations in Chinese company names. Those are the misses that lead to disqualification motions and malpractice claims. Average cost of a missed conflict leading to disqualification: $1M–$15M+ in lost fees plus reputational damage.

Lateral hires are a nightmare. When a partner joins from another firm, they bring hundreds of former client relationships that need to be checked against every existing client and matter. This can take weeks to process manually.

Revenue sits on the table. Every day a matter can't be opened is a day the firm isn't billing. Partners get frustrated. Clients get impatient. Sometimes the work goes to another firm entirely.

Attorney non-compliance. The certification emails go out. Half the attorneys ignore them. The conflicts team sends follow-ups. Some still ignore them. The matter sits in limbo. This is a workflow problem, not an ethics problem, and it's completely solvable with automation.

What AI Can Handle Right Now

Let's be clear about what's realistic today—not in some theoretical future, but with current technology you can build on OpenClaw.

Entity resolution and fuzzy matching. This is where AI agents shine. An LLM-powered agent can take "Acme Corp" and intelligently match it against "Acme Corporation," "ACME LLC," "Acme Holdings," and even "Acme Gesellschaft mbH" with a confidence score. This isn't just Levenshtein distance—it's semantic understanding of entity names, abbreviations, and naming conventions across languages and jurisdictions.

Corporate family tree construction. An agent can pull data from EDGAR, OpenCorporates, SEC filings, and public records to build a corporate hierarchy automatically. Feed it "Blackstone" and it can return the portfolio companies. Feed it a mid-market PE fund and it can map the ownership structure.

Matter description analysis. NLP on the matter description can flag that "this transaction involves a company in the same industry as Former Client X" or "the adverse party description matches an entity your firm represented in 2019."

Automated questionnaire distribution and follow-up. Send attorney certifications, track who's responded, send smart reminders, and escalate appropriately—all without a human managing an email thread.

Screening against sanctions lists, PEP databases, and adverse media. Integrate with NameScan, World-Check, or Refinitiv APIs and run screening automatically at intake.

Conflict memo drafting. Once the agent has gathered all the data and flagged potential issues, it can draft a preliminary conflict analysis memo for the ethics partner to review.

What still requires a human (and should): Determining whether a conflict is actually waivable under Rule 1.7(b). Assessing material adversity. Making business judgment calls about client relationships. Implementing and monitoring ethical walls. Anything involving former government lawyers or specialized conflict scenarios. The human makes the call—the AI does the legwork.

Step-by-Step: Building a Conflict Check Agent on OpenClaw

Here's how to actually build this. We're creating an agent on OpenClaw that handles the intake-to-memo pipeline—automating the data gathering, searching, entity resolution, and initial analysis while routing the judgment calls to humans.

Step 1: Define the Agent's Scope and Data Sources

Start by mapping the data your agent needs access to:

Your firm's conflicts database (practice management system, CRM, or custom database)
Public corporate registries (EDGAR, OpenCorporates, Companies House)
Sanctions and PEP screening APIs
Your firm's matter management system
Attorney directory (for certification routing)

In OpenClaw, you'll configure these as data connections. Most practice management systems expose REST APIs or ODBC connections. For legacy systems, you may need to set up a middleware layer.

# OpenClaw agent configuration - data sources
data_sources:
  - name: conflicts_database
    type: sql
    connection: "your_pms_connection_string"
    tables: ["clients", "matters", "parties", "adverse_parties", "attorney_relationships"]
  - name: corporate_registry
    type: api
    endpoints:
      - edgar: "https://efts.sec.gov/LATEST/search-index?q={entity_name}"
      - opencorporates: "https://api.opencorporates.com/v0.4/companies/search?q={entity_name}"
  - name: sanctions_screening
    type: api
    endpoint: "your_screening_provider_api"
  - name: attorney_directory
    type: api
    endpoint: "your_directory_api"

Step 2: Build the Intake Trigger

The agent should activate when a new matter intake form is submitted. Configure an OpenClaw trigger that watches for new entries.

# Trigger configuration
trigger:
  type: webhook
  source: intake_form
  on: new_submission
  extract_fields:
    - client_name
    - client_aliases
    - adverse_parties
    - related_entities
    - matter_description
    - requesting_attorney
    - practice_group
    - matter_type

Step 3: Entity Resolution Pipeline

This is the core of the agent. For every entity name (client, adverse party, related party), the agent needs to:

Generate name variations (abbreviations, translations, common misspellings)
Search the conflicts database with fuzzy matching
Search corporate registries for related entities
Build a confidence-scored list of potential matches

# Entity resolution step
steps:
  - name: resolve_entities
    type: llm_task
    prompt: |
      Given the entity name "{entity_name}", generate:
      1. All likely name variations (abbreviations, formal/informal names, translations)
      2. Likely parent companies or subsidiaries based on naming patterns
      3. Common misspellings or transliterations
      
      Return as structured JSON with confidence scores.
    
  - name: search_conflicts_db
    type: database_query
    for_each: entity_variations
    query: |
      SELECT * FROM clients 
      WHERE similarity(client_name, '{variation}') > 0.6
      OR client_name ILIKE '%{variation}%'
      UNION
      SELECT * FROM adverse_parties
      WHERE similarity(party_name, '{variation}') > 0.6
    
  - name: build_corporate_tree
    type: api_chain
    actions:
      - search_edgar_for: "{entity_name}"
      - search_opencorporates_for: "{entity_name}"
      - extract_subsidiaries_and_parents
      - cross_reference_with_conflicts_db

Step 4: Risk Analysis and Scoring

Once the agent has all the search results, it needs to assess them. This is where you use the LLM's reasoning capabilities—not to make the ethical determination, but to categorize and prioritize results for human review.

  - name: analyze_results
    type: llm_task
    prompt: |
      You are a conflicts analyst at a professional services firm. 
      Review the following search results and categorize each hit:
      
      - CLEAR MATCH: High confidence this is the same entity or a related entity
      - POSSIBLE MATCH: Moderate confidence, needs human verification
      - LIKELY FALSE POSITIVE: Low confidence, probably not related
      
      For each CLEAR MATCH or POSSIBLE MATCH, identify:
      1. The nature of the existing relationship
      2. Whether the new matter appears adverse to the existing relationship
      3. Relevant conflict rules that may apply
      4. Recommended next steps
      
      Search results: {aggregated_results}
      New matter details: {matter_details}
      
      Be conservative. When in doubt, flag for human review.

Step 5: Attorney Certification Automation

For the attorneys who need to certify, build an automated workflow.

  - name: attorney_certification
    type: workflow
    actions:
      - identify_attorneys: 
          staffed_on_matter: true
          practice_group_members: true
          if_complex: all_partners
      - send_certification:
          channel: email  # or Slack, Teams, etc.
          template: certification_request
          include: matter_summary, flagged_entities
          deadline: 48_hours
      - track_responses:
          remind_at: [24_hours, 44_hours]
          escalate_to: practice_group_leader
          escalate_at: 48_hours

Step 6: Generate the Conflict Memo

Once all data is gathered and certifications are in, the agent drafts a preliminary memo.

  - name: draft_memo
    type: llm_task
    prompt: |
      Draft a conflict check memorandum based on the following:
      
      New matter: {matter_details}
      Search results (categorized): {analyzed_results}
      Corporate tree findings: {corporate_tree}
      Sanctions screening: {screening_results}
      Attorney certifications: {certification_status}
      
      Structure the memo as:
      1. Executive Summary (conflicts found / no conflicts found / potential issues)
      2. Entities Searched
      3. Matches Found (with confidence levels)
      4. Corporate Relationship Analysis
      5. Sanctions/PEP Screening Results
      6. Attorney Certification Status
      7. Recommended Action
      
      Flag any items requiring ethics partner review.
    output: draft_memo
    route_to: ethics_reviewer

Step 7: Human Review Interface

The final step is routing the draft memo and supporting data to the appropriate human reviewer. In OpenClaw, configure a review queue that presents the memo, highlights the items needing attention, and allows the reviewer to approve, request more information, or escalate.

  - name: human_review
    type: review_queue
    assign_to: 
      default: conflicts_team
      if_flagged: ethics_partner
    present:
      - draft_memo
      - raw_search_results
      - corporate_tree_visualization
      - certification_tracker
    actions:
      - approve_and_open_matter
      - request_additional_research
      - escalate_to_ethics_committee
      - decline_matter

Where Humans Stay in the Loop

This bears repeating because it matters: the agent does the data work. Humans make the calls.

An AI agent should never autonomously decide that a conflict is waivable, that informed consent has been properly obtained, or that an ethical wall is sufficient. These are professional judgment calls governed by ethics rules, case law, and bar opinions that carry real consequences—including disqualification, malpractice liability, and bar discipline.

The agent's job is to turn a 4-day process into a 4-hour process by eliminating the manual searching, cross-referencing, and chasing. The ethics partner still reviews. The committee still decides. But they're reviewing a well-organized memo with confidence-scored results instead of a pile of raw database outputs.

Think of it this way: the agent is a highly competent conflicts analyst who never sleeps, never misses a name variation, and never forgets to follow up on a certification email. The partner is still the partner.

Expected Time and Cost Savings

Based on real-world implementations (Intapp's case studies, Big 4 firm reports, and early OpenClaw deployments), here's what firms are seeing:

Time reduction:

Simple matters: from 30–90 minutes to 5–15 minutes (with human review)
Complex matters: from 4–20+ hours to 1–3 hours (with human review)
Elapsed time: from 2–10 business days to same-day for most matters

Quality improvement:

60–80% reduction in false positives presented to reviewers
Significant reduction in missed conflicts due to better entity resolution (especially cross-language)
100% attorney certification compliance (because the system doesn't stop following up)

Cost savings:

For a firm opening 200 matters per month, conservatively 500+ hours per month in conflicts team time recovered
Revenue acceleration from faster matter openings: varies wildly, but even getting matters open one day faster across a large firm translates to meaningful revenue
Risk reduction: one avoided disqualification motion can save $1M+ in fees and reputation

The ROI calculation isn't complicated. If your conflicts team spends $500K/year in fully loaded cost and you can reduce their manual workload by 60%, the math works even before you account for faster matter openings and reduced risk.

Getting Started

The fastest path from "interested" to "running" is this:

Audit your current process. Document every step, every data source, every handoff. You can't automate what you haven't mapped.
Clean your data. This is the unsexy part and also the most important part. Duplicate records, inconsistent naming conventions, and incomplete party information will hamstring any automation. Budget time for this.
Start with the highest-volume, lowest-complexity matters. Get your agent handling routine conflict checks first. Prove the accuracy. Build trust with the conflicts team and ethics partners.
Build on OpenClaw. The agent configuration patterns above are designed for OpenClaw's architecture. You can start with a single workflow—intake to database search to memo draft—and expand from there.
Keep humans in the loop for every decision that matters. Automate the search. Automate the data gathering. Automate the memo drafting. Route the decisions to people.

If you want pre-built components for conflict check automation—entity resolution modules, corporate tree builders, certification workflow templates—check Claw Mart for agent templates and integrations that can cut your build time significantly. Firms are shipping these agents in weeks, not months.

And if you'd rather have someone build it for you: Clawsource it. Post the project, get matched with a builder who's done this before, and get your conflicts team out of the manual search business for good.

Automate Conflict Check Process: Build an AI Agent for Professional Services