How to Automate Conflict Checks at Law Firm Intake: What AI Matches and What Attorneys Clear

Can AI replace a conflicts analyst?

No. Software can run every new party's name, in every form on file, against the firm's client, matter, and affiliate records, and surface every match it finds. Deciding whether a match is an actual conflict, whether a waiver or consent resolves it, and whether to accept the engagement is a professional judgment, not a search result.

The automatable part is the search: finding every exact, alias, affiliate, former-client, and adverse-party match against everything the firm already knows. The non-automatable part is clearance: weighing what a match means and whether the firm takes the matter at all. We recommend keeping that clearance decision with the responsible attorney, a conflicts partner, or general counsel, as the firm's own operating choice rather than a system default.

The match-vs-attorney-clearance table

A "conflict check" is actually several different searches, each needing a different method and a different degree of judgment before it's cleared. The table below sets a default system action and a clearance owner for five match types we recommend distinguishing, as a starting point to adapt to your own policy and approval chain.

What the intake-to-clearance audit trail should capture

Whatever system runs the search, whether it's built into the firm's matter management platform or a separate conflicts tool, the record it keeps matters as much as the match itself. The checklist below lists what to capture from the moment a new matter is entered to the moment it's cleared.

1. 01
   Search inputs

   Every name, alias, and affiliate searched, and the matter description or practice area it was searched against.

2. 02
   Search method and date

   Which database and matching method ran (exact, fuzzy, corporate-hierarchy), and when.

3. 03
   Every hit, including dismissed ones

   Each match the search returned, even one a person dismissed, with the reason it was dismissed.

4. 04
   Reviewer identity

   The named conflicts analyst or attorney who reviewed each hit.

5. 05
   Clearance decision and basis

   Cleared, escalated, or declined, and the stated reason; any waiver or consent referenced by document, not just by note.

6. 06
   Date cleared and by whom

   The date intake was allowed to proceed, and the name of whoever made that call.

What conflict matches can be automated safely?

The search itself is what automates safely: comparing a new party's name, in every form on file, against the firm's own records. What differs by match type is how much work a person has once a hit comes back, not whether a person sees it at all: every match type in the table above, including an exact match, still goes to a named clearance owner before intake proceeds.

• What automates fully: the search itself, run against every name and alias the firm has on file, for every match type.
• What still goes to a person every time: a conflicts analyst confirms the match, and the responsible attorney makes the clearance decision, from a quick confirmation on an exact match to a full review of a fuzzy, alias, or affiliate hit.

Fuzzy and alias matching trades false negatives for false positives: loosen the threshold and the system catches more real aliases, but a conflicts analyst reads more near-misses that aren't the same party. We cover that same trade-off, for a different high-volume review task, in our analysis of false positives in high-volume review; the discipline of measuring both error types applies directly to conflict matching too.

How do you catch conflicts hidden in corporate affiliates?

Catching conflicts hidden in corporate affiliates means resolving each named party to its corporate family, parent, subsidiaries, and entities under common control, before the search runs, not after. A new matter's named party is rarely the whole picture: a subsidiary, a parent company, or another entity under common control can create a conflict that a search limited to the name on the engagement letter will miss entirely.

Vendor documentation for one conflicts-checking product describes a system designed to detect issues "across matters, relationships, and corporate hierarchies"^[1], rather than relying only on the name typed on the engagement letter. The same documentation separately describes a feature to "enrich your firm's data ... with corporate trees and industry codes from trusted third-party sources"^[1].

• Resolve every named party to its ultimate parent and known subsidiaries before running the match, not as a follow-up step.
• Treat an affiliate match the same as a direct-party match for review purposes: flag it, don't auto-clear it.
• Re-run the affiliate resolution periodically, since mergers, acquisitions, and divestitures change corporate structures that the firm's own records won't reflect on their own.
• Apply the same affiliate check to a lateral hire's prior clients and matters, since an incoming attorney can bring affiliate relationships the firm's existing database has never seen.

What should the conflicts record show for each search?

A conflicts record is only useful if it lets someone else, later, see why a decision was made. That means recording the search and the reasoning behind each hit, not just the final outcome.

Vendor documentation for the same product frames this as keeping "an auditable record of all analyses and decisions" and the "history of every conflicts search and outcome"^[1]. We recommend keeping that same level of detail for every search the firm runs, so a later reviewer can see what was searched and why it was cleared, not just that it was.

• What was searched: every name, alias, and affiliate, against which databases.
• What came back: every hit, including the ones dismissed, and why.
• Who decided: the named reviewer for each hit, and for the final clearance.
• What it relied on: any waiver, consent, or ethical-screen document referenced, not just described.

Where to start: a first scoped workflow

A full taxonomy and audit trail is the target, not the starting point. A practical first scope keeps the highest-confidence automation and leaves everything judgment-heavy exactly where it is today.

• Automate exact and known-alias matching first, since it needs the least new infrastructure and we'd expect it to produce fewer false positives than fuzzy or affiliate matching.
• Add affiliate and corporate-family resolution once the exact-match pipeline is stable, since it depends on sourcing and maintaining corporate-hierarchy data.
• Keep former-client and adverse-party matching on the same manual review path you use today until the automated search has run in parallel against real intake for a full cycle.
• Define the audit-trail fields before go-live, not after, so the first automated searches are already captured the way you'll want to show them later.
• Run the automated search alongside the current manual process for a defined period, and compare hit lists before retiring any manual step.

Sequencing the search inside intake itself, rather than as a side step, is a pattern at least one intake platform documents: product documentation for one client-onboarding system describes a system that can "Automate conflicts checks and enforce OCGs in real time"^[2], which matches the sequence recommended above — run the automated search as a new matter enters the pipeline, not as a separate step attached afterward.

When this doesn't apply

• The firm's client, matter, and party data isn't reliably entered or deduplicated today; an automated search against unreliable data just automates the unreliable part.
• Matter volume is low enough that a manual search already clears in the time intake needs; the gain from automating is smaller than the cost of building it.
• There's no written policy for who clears which match type; automating the search without that policy just moves the ambiguity earlier.
• The firm has no reliable source of corporate-affiliate data and isn't ready to license or maintain one; affiliate matching without it will miss what it's meant to catch.

Frequently asked questions

Related reading

• Our legal industry page covers AI workflows across a law firm's operations, including intake and conflicts, at a broader level than this article.
• Our solution page on document review with human approval covers the same abstain-and-escalate pattern applied to playbook-based contract review.
• Our article on AI document intelligence for legal and compliance covers document review and extraction at more length; this article stays specific to conflicts at intake and doesn't restate its figures.
• For the broader principle behind keeping a person in the loop, see human-in-the-loop AI governance; this article narrows that to conflict-check matching and clearance.

Sources

1. [1] — Intapp Conflicts product page (accessed 2026-09-30)
2. [2] — Intapp Intake product page (accessed 2026-09-30)