The problem
LLMs are built to predict the next word, not to be right. Five known failure modes keep them out of the rooms where correctness matters.
Fabricated facts, citations, and clauses, delivered with full confidence. OpenAI's own researchers trace this to training and scoring that reward a confident guess over "I don't know."1
Each reasoning step is probabilistic. Chain them together and the error compounds. Transformer accuracy falls off as the number of steps grows, even on problems a child could do with pencil and paper.2
Over a long document or conversation, the model's attention wanders. Facts in the middle of a long input are used less reliably than facts at the ends, and performance degrades as the input grows.34
None of these are bugs to patch. They are fundamental properties of LLM architecture.
Where honest AI is overdue
These fields have been slow to adopt AI, and for good reason.
A New York court sanctioned lawyers in 2023 for filing six cases ChatGPT invented.9 By the end of 2025, courts had written more than 700 decisions about hallucinated filings.10 Even purpose-built legal tools hallucinate on roughly one query in six.11
Dosing rules and clinical protocols cannot tolerate a plausible-sounding mistake. Clinicians judged a fifth of a chatbot's inaccurate drug answers capable of severe harm or death.12
The numbers must reconcile. The largest tax-prep chatbots were unhelpful or wrong on up to half of questions.13 A Big Four firm refunded a government contract over fabricated citations.14
Policies are if-then logic and demand the same answer every time. When an airline's chatbot misstated its refund policy, a tribunal held the airline to it.15
Coverage decisions must trace to the policy and the facts. About half of US states now require insurers to document how AI reaches its decisions.16
Who owes whom, how much, and as of what date. Answers that depend on a sequence of events and a set of clauses.
What robuster.ai does differently
Every answer comes with a proof: the facts used, the rules applied, each step in order. You can audit it.
Same facts, same rules, same answer. Every time, on any machine.
If a conclusion can't be derived from what it knows, robuster.ai says so. It never fills a gap with a plausible guess.
A statement that conflicts with an established fact is rejected and the conflict is named. It cannot be talked out of what it knows.
Facts, rules, and definitions are stored in plain, inspectable form. Your experts can read what it believes and correct it.
Deadlines, durations, sequences and corrections. robuster.ai tracks when things happened and what was true at each moment.
How it works
It takes in the contract, the chart, the policy, and turns prose into simple statements.
Those statements become facts and rules in a knowledge graph that persists and does not drift.
Questions are answered by deduction over those facts, not by guessing.
The LLM explains it in plain English. robuster.ai guarantees the explanation matches the logic.
A language model hands robuster.ai a 59-clause contract, an amendment, and a 27-entry project record, then asks who owes whom.
LLM
Here is the contract. Contract · 59 clauses
robuster.ai
Stored. 59 clauses as 64 rules, in force from January 1.
LLM
Here's an amendment. Amendment 1 · 3 changes
robuster.ai
Applied 3 changes from March 28 on.
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Contact
robuster.ai is in active development and looking for partners in legal, medical, financial, and compliance domains. Tell us what you need to reason about.
