vs generic LLMs
A guess at scale
is still a guess.
Ask a general-purpose model to extract business rules from production COBOL and it answers fluently, confidently — and, in our benchmark, correctly about 30% of the time. Charm is not lineage.
The model rolls.
Seven of ten come up wrong.
Every Palm rule carries
the address it came from.
The difference
Liability with good grammar
The difference isn’t the model — it’s the method. A chat window samples from a distribution; it cannot show its work, and it answers differently tomorrow. In a regulated cutover, an unsourced answer is not an answer. It’s a liability with good grammar.
Palm Ark is deterministic: the same code yields the same extraction, every time, across 50,000+ lines in a single pass — and every rule arrives with the exact source lines attached, around 95% accurate in benchmark against ~30% for generic LLMs. Your auditors don’t have to trust the AI. They can check it.