When AI Assistants Get Your Firm Wrong and How to Fix It

A prospective client asks an AI assistant about your firm and gets an answer that is confidently wrong. It says you handle practice areas you exited years ago, lists a partner who left in 2022, quotes office hours for a location you closed, or blends your firm with a similarly named one across the state. Nobody at the firm sees the answer, no analytics record it, and the prospect quietly calls someone else. Wrong AI answers about your firm are a real and growing failure mode, and unlike a bad review, they happen in private, at scale, in a channel you cannot read. The good news is that these errors have causes, the causes live mostly in sources you control, and a systematic firm can fix the majority of them. This post is the diagnostic and repair process.

Why Assistants Get Firms Wrong

AI assistants produce firm descriptions from two mechanisms, and the failure modes differ. Answers grounded in live retrieval pull from current web sources, your site, your Google Business Profile, directories, news, and reviews, and when those sources disagree or are stale, the answer inherits the mess. Answers from model memory rely on training data with a cutoff, so a firm that rebranded, moved, merged, or changed practice mix since the training window can be described as its former self for years. Blended-entity errors, where your firm is fused with a similar name, come from weak entity disambiguation, common for firms named after common surnames. The diagnostic insight is that almost every wrong answer traces to something checkable, an inconsistent address across directories, an outdated bio page, a dead location page still indexed, a Wikipedia-less brand with no authoritative reference point, or a name collision nobody ever addressed. You cannot retrain a model, but you can fix every one of those inputs.

Run the Audit, Ask the Machines About Yourself

Start by measuring the problem. Quarterly, ask the major assistants the questions prospects actually ask, what does your firm do, who are the partners, where are the offices, what do people say about the firm, who is the best divorce lawyer in your city. Do it logged out and in fresh sessions where possible, record the answers verbatim in a tracking sheet, and grade each one, correct, stale, blended, or fabricated. Patterns will emerge fast. Stale answers cluster around things you changed, blended answers around your name’s collisions, and fabricated details around gaps where authoritative information simply does not exist for the model to find. The audit also captures a baseline, because after you fix the sources, retrieval-based answers typically improve within weeks while memory-based answers improve only as models refresh, and you want the evidence of movement. Treat this exactly like rank tracking, a recurring instrumented check, not a one-time panic.

Extend the audit beyond your own name. Ask the assistants the category questions, best personal injury firm in your city, who should I call after a car accident here, and note whether you appear at all, and which competitors do. Ask about your practice areas in the phrasing clients use, not the phrasing lawyers use. The category answers reveal a different problem than the entity answers, absence rather than error, and the two problems have different fixes. An entity error is repaired with source hygiene, an absence is repaired with the authority and citation work that earns a firm its place in recommendation-shaped answers. Knowing which problem you have keeps the budget pointed at the right machinery.

Fix the Source Layer First

Assistants that retrieve are only as good as what they retrieve, so the repair sequence starts with the boring, decisive work of source hygiene.

• Your website is the primary source, and it must state the basics unambiguously, current practice areas, current attorneys with dated bios, offices with full address details, and a clear about page that says in plain sentences what the firm is, where it operates, and what it does not do. Remove or redirect pages for departed attorneys, closed offices, and abandoned practice areas, an indexed ghost page is a fact an assistant will happily repeat.
• Structured data makes the facts machine-legible, legal service schema, attorney markup, address and hours markup, all consistent with the visible page content.
• The citation graph must agree with itself. Name, address, and phone inconsistencies across directories are a top source of blended and stale answers, and cleaning them is the same discipline as local search hygiene, covered in our system for law firm NAP consistency. Your legal directories and citations deserve a full pass, because assistants lean heavily on directory profiles when describing firms.
• Google Business Profile accuracy matters doubly, it feeds both map surfaces and AI answers about local businesses, so categories, hours, attributes, and descriptions need to be current and correct, per the practices in our Google Business Profile for lawyers guide.

Google has been explicit that its generative features draw on indexed, crawlable content under the same technical requirements as Search, there is no separate optimization layer, eligibility and accuracy flow from the same index your SEO already feeds. The stakes of thin source material are documented, Stanford HAI’s research on legal hallucinations in large language models found hallucination rates between 69 and 88 percent on specific legal queries, which is exactly why the material assistants retrieve about your firm has to be accurate and consistent. The practical consequence is encouraging, every hour spent on source hygiene pays into both channels at once.

Sequence the cleanup by authority, not by convenience. Fix the website first, then Google Business Profile, then the major legal directories, then the long tail of citation sites, because assistants weight sources unevenly and the top of that hierarchy corrects the most answers per hour of work. Set a realistic timeline expectation with the partners too, retrieval-grounded answers usually reflect source fixes within weeks, while answers drawn from model memory can lag for months until the next training refresh, and a program abandoned at week six because the chatbot still says the old address is a program that quit exactly when the slow half of the fix was underway.

Strengthen the Entity, Give the Machines an Anchor

Beyond fixing errors, you can make your firm harder to get wrong by building a stronger entity footprint. Consistent naming everywhere is the foundation, pick the exact form of the firm name and use it identically across the site, directories, social profiles, and bylines, variations feed blending. Third-party corroboration hardens the entity, bar association listings, chamber memberships, news coverage, and university or association pages that reference the firm give assistants authoritative cross-checks. If your name collides with another firm, differentiate actively, geographic qualifiers in profiles, distinct descriptions, and enough published material that the two entities develop separate footprints. For firms with any public profile, maintaining accurate profiles on the reference sites assistants trust, and correcting errors on them through their own processes, is quiet, high-leverage work. This is the same authority architecture that drives citation in generative search results, which we mapped in how law firms get cited by ChatGPT and other LLMs, the difference is that here the goal is not being recommended, it is being described correctly when someone asks.

The Correction Channels That Exist Today

Direct correction routes are immature but real, and a firm should use all of them. Feedback mechanisms inside the assistants, thumbs-down reports with specifics, feel futile individually but feed the systems that patch egregious errors. Publisher and business tools are emerging across platforms for entity claims and corrections, and whoever owns marketing should check quarterly what has launched, this landscape changes fast. For defamatory fabrications, a false statement that a named attorney was disciplined, for instance, document everything with screenshots and dates, report through the platform’s formal channels, and get advice on the developing legal remedies, several disputes over AI-fabricated claims about professionals have already reached courts, and the procedural landscape is evolving, so treat any specific remedy as something to verify at the time rather than assume. For run-of-the-mill staleness, patience plus source fixes is genuinely the mechanism, retrieval-grounded answers correct themselves once the sources agree.

Make It a Standing Program, Not a Crisis Response

The firms that stay correctly described will be the ones that operationalize this. A quarterly AI answer audit with the tracking sheet. Source hygiene tied to every firm change, attorney departures, office moves, and practice shifts trigger a same-week update sweep across site, schema, GBP, and top directories, staleness is created at change moments and prevented there too. Entity monitoring folded into existing brand monitoring, alerts on the firm name catch new sources before they propagate. And ownership, one named person accountable for the audit and the sweep, because unowned hygiene programs die by quarter three. The uncomfortable truth about AI answers is that they are compiled reputation, everything the web says about you, averaged by a machine with no duty of care. The firms that treat their web presence as a single consistent record, rather than a pile of profiles nobody reconciles, are the ones the machines describe accurately.

Rubiks runs entity and visibility programs for law firms across search and AI surfaces, source hygiene, structured data, directory reconciliation, and the quarterly answer audits that prove movement. If an assistant is telling prospects the wrong story about your firm, book a strategy call and we will run the diagnostic with you.