Three members of the same services team were each asked to fact-check a regulatory claim using their preferred AI tool. They returned three different answers. One said the figure was accurate, one flagged it as outdated, and one returned a source from a website the team didn’t recognise.
This happens when teams treat fact-checking AI as a single category. Different tools are built for different tasks, and using the wrong one for a given job produces unreliable results regardless of how capable the underlying model is.
What choice are you actually facing with AI fact-checking?
Choosing the right AI tool for fact-checking depends on four variables that often get overlooked: the type of claim you are checking, what happens if it is wrong, who in your firm is accountable for the output, and what level of evidence would satisfy a regulator or a client. Different tools perform very differently across those four dimensions, and matching tool to task is where the real decision sits.
Three main types of AI tool operate in this space. Retrieval-based search tools, with Perplexity as the clearest current example, blend live web search with an LLM and surface citations alongside every answer. Specialist tools like ClaimBuster, built with RAND support, detect which sentences in a document contain factual claims worth checking. General-purpose LLMs, when connected to a structured knowledge base or document set, can check claims against internal sources rather than the public web.
The choice carries a regulatory dimension as well as a technical one. The ICO’s guidance on AI and data protection sets out that accuracy obligations under UK GDPR apply to AI-generated outputs, particularly where they affect individuals. The FCA holds regulated firms responsible for the accuracy of financial promotions and client communications regardless of the tools used to produce or check them. Any fact-checking tool has to sit inside a governance structure that can demonstrate those obligations are met.
When does a retrieval-based search tool do the job?
Tools like Perplexity blend live web search with an LLM, returning answers with inline citations you can inspect before relying on them. A 2024 Stanford-led study found that LLM fact-checking performance improves substantially when models have access to curated external evidence. Retrieval-first tools provide exactly that mechanism. For marketing claims, regulatory thresholds, and published statistics at low-to-medium risk, they combine speed with an auditable citation trail.
They perform well when the claim you’re checking has multiple independent sources on the public web, when the underlying evidence is current and findable, and when you want to point directly to a source without additional research. Confirming the current corporation tax rate, checking whether a product claim is consistent with published HMRC or FCA guidance, or verifying a statistic before it goes into a client report are all tasks where these tools add real value.
The limits are structural. Retrieval-based tools depend on the quality of what is indexed. Outdated guidance, poorly sourced trade press, or conflicting regulatory documents can all produce misleading verification. Where a claim lives in grey-literature sources, an FCA consultation paper, an ICO enforcement decision, or recent case law, a retrieval tool’s first pass needs confirming against the primary document directly.
A sensible workflow: use the AI search tool to surface candidate sources, then go to the primary source yourself before signing off.
When does a specialist fact-checking tool earn its place?
Specialist tools solve problems that retrieval-based search cannot. ClaimBuster, built with RAND support, identifies which sentences in long texts contain factual claims worth checking, triaging the effort before a human reviews them. Elicit queries more than 125 million academic papers to check whether claims are grounded in actual research findings. These tools earn their place when accuracy is professionally or legally load-bearing.
The key distinction from retrieval-first tools is precision of purpose. Factiverse demonstrated near-real-time fact-checking during EU Parliament election debates in 2024, detecting claims from live speech and matching them to established fact databases. Sourcely helps verify that claims in white papers and technical guides trace back to the specific studies being cited, rather than to summaries or secondary sources that may have changed the original finding.
For a services firm, three situations push towards specialist tools. Producing content that will be professionally relied on: legal commentary, HR guidance, financial analysis, compliance documentation. Publishing research or thought leadership that will be cited by others. Monitoring what is being said about your sector or brand in public speech, press, or regulatory consultation.
In those situations, the cost of a specialist tool is nearly always lower than the professional cost of one published error.
What does it cost to pick the wrong tool?
The costs come in three categories, and they compound in regulated sectors. If you are in financial services, the FCA holds you responsible for the accuracy of client communications regardless of the tools used to check them. For professional services firms more broadly, UK PI insurers are increasingly asking about AI controls, and firms without documented review processes have weaker footing when a claim is disputed. Published errors damage client trust immediately.
The clearest external warning came in 2023, when a US lawyer was sanctioned and fined after citing non-existent case law that ChatGPT had fabricated in a court filing. The professional and public damage was significant. UK professional indemnity policies now frequently address AI risk, and some require evidence of appropriate controls as a condition of cover.
Data protection adds a layer that catches many firms unprepared. If your fact-checking process involves personal data, client records, staff profiles, or case files, the ICO’s accuracy principle under UK GDPR requires that you can correct AI errors and that those errors do not cause unfair harm to individuals. The NCSC’s guidance on using AI systems securely is direct on this point: sensitive client documents should not be uploaded to public AI tools. Data residency and confidentiality risks are real, and a vendor’s privacy policy may allow prompts and content to be used for model improvement unless you explicitly opt out or use a private enterprise deployment.
What should you ask before you commit to a tool?
Tool vendors in this space make confident accuracy claims, and the gap between marketing and real-world performance is often wide. Before committing to any tool for a professional workflow, there are four things worth testing directly: how it handles claims with no clear source, what it does when sources conflict, where your data is stored, and whether you can export an audit trail if a regulator asks how content was checked.
On evidence and benchmarks: ask what independent evaluations show about fact-checking performance and how the vendor measures hallucination rates. Accurate answers to both questions are the minimum bar. A vendor who cannot provide them is not ready for use in a professional context.
On source control: ask whether you can specify or restrict to trusted domains, whether every answer includes clickable citations, and whether you can limit the tool to internal documents when your own policies or product specifications matter more than public web coverage.
On data protection: the NCSC’s guidance on using AI systems securely makes clear that organisations should treat AI tools as untrusted by default and restrict sensitive data input. For confidential client work, use sanitised summaries or a private enterprise deployment with a documented data processing agreement.
On exit and flexibility: the CMA’s ongoing review of AI foundation models has flagged vendor lock-in as a genuine concern for organisations building workflows around a single provider. An architecture where you can switch the underlying model without re-platforming adds meaningful resilience for little additional cost.
The right approach for a UK services firm is a stack matched to risk level: retrieval-based search for speed and citation transparency on general claims, specialist tools where accuracy is professionally or legally load-bearing, and human sign-off for anything where the FCA, ICO, or a professional body holds you responsible. That combination is straightforward to build. What it requires is a clear decision about which category each task falls into before you start.
