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Verifying AI Due Diligence for M&A and KYB
You rely on AI outputs for high-stakes decisions, but are those outputs stable or just random noise? VeritasLinks measures the judgment layer beneath the citations to separate true perception from stochastic variance.
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The Problem: Phantom Citations and Unstable Verdicts
M&A analysts, compliance teams, and credit officers increasingly use AI to review vendors and companies. Yet, the outputs can be inconsistent. LLMs are stochastic. Ask the same question multiple times and you get different answers. A single pass through a model might report a citation as a fact, but that citation could be a phantom reference or influenced by the data source.
Cornell University researchers found that around 147,000 pieces of phantom research were cited in papers last year due to AI compilation errors. Similarly, Stanford University researchers found that up to 17% of referee reports in major computer science conferences had been substantially modified by language models. If an AI agent recommends a vendor based on a single, potentially biased or hallucinated review, the risk is real.
OpenAI claimed its AI agents solved the Navier-Stokes equations in 88 hours, but mathematician Tristan Buckmaster raised concerns about potential data influence from OpenAI's Codex tool. This highlights a broader issue: when AI determines coverage or creditworthiness, it often relies on larger datasets rather than individual medical or financial history. CMS cautions against this reliance on aggregate data over specific context.
The Solution: Measuring the Judgment Layer
VeritasLinks is not a GEO tool that counts mentions. It measures how large language models perceive, describe and judge a specific company. It analyzes a company across several AI models, including ChatGPT, Claude, Grok, Perplexity and DeepSeek, and shows a separate score for each model.
Each analysis runs many prompts across several models with repeats and measures the spread of answers, instead of reporting a single model answer as a fact. This approach distinguishes true perception from random noise. It reveals where AI models disagree about the same company. The gap between models is itself a risk signal. This Split-Perception research by VeritasLinks founder Andrew Pomazkov is published on SSRN.
The analysis includes the emotional tone each model carries about the company. It also provides an AI Investor Review that reads the company the way an investor or credit officer researching through AI would. Reports include recommendations on what to fix, derived from relationships between the metrics.
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Independence and Stability
VeritasLinks scores are independent. There is no paid placement and scores cannot be bought. This independence is critical for due diligence. You need to know the score reflects the model's actual judgment, not a sponsored boost.
Subscriptions include automatic re-runs so changes in how models see the company can be tracked over time. This allows you to monitor stability. If a vendor's score drops, you can see if it was a temporary fluctuation or a genuine shift in perception.
The founder, Andrew Pomazkov, has 25 years in software engineering and built the scoring model at a national credit bureau. This background ensures the scoring logic is robust and suitable for enterprise risk and compliance teams.
Why It Matters for KYB and M&A
In high-stakes decisions, the lack of verification mechanisms creates risk. As AI agents drive more consequential decisions, you need to know if the output is stable. VeritasLinks provides a verifiable judgment layer.
For KYB reviewers, it surfaces objections and choices through AI-simulated focus groups in which synthetic buyer personas discuss the company. For M&A analysts, it clarifies where models disagree, helping you assess the reliability of AI-driven vendor selection and credit reviews.
The first analysis is free. You can start by verifying a single company or vendor. Agencies can also deliver VeritasLinks reports under their own brand, making it easy to integrate into existing workflows.
Questions
Short clarifications — positioning and proof live in the sections above.
How is VeritasLinks different from standard AI visibility tools?+
Standard tools count citations and mentions in a single pass. VeritasLinks measures the judgment layer by running many prompts across several models with repeats. It measures the spread of answers to distinguish true perception from random noise and phantom citations.
Does the score reflect a single model or multiple?+
VeritasLinks analyzes a company across several AI models, including ChatGPT, Claude, Grok, Perplexity and DeepSeek, and shows a separate score for each model. This reveals Split-Perception, where different models judge the same company differently.
Is the VeritasScore influenced by paid placement?+
No. VeritasLinks scores are independent. There is no paid placement and scores cannot be bought. The score reflects how models actually perceive the company.
What is the benefit for KYB and M&A teams?+
It provides a verifiable judgment layer for high-stakes decisions. By measuring stochastic spread, it helps you assess the stability and bias of AI outputs used in credit reviews, due diligence, and vendor selection.
Can I track changes in perception over time?+
Yes. Subscriptions include automatic re-runs so changes in how models see the company can be tracked over time. This helps you monitor stability and detect shifts in perception.
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- →Structured interpretation, not a one-off chat screenshot.
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