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.
Side by side
Sampling versus extraction
| Generic LLM | Palm Ark | |
|---|---|---|
| Accuracy on production COBOL | Correct about 30% of the time in our benchmark | Around 95% accurate in the same benchmark |
| Determinism | Samples from a distribution — answers differently tomorrow | The same code yields the same extraction, every time |
| Source lineage | Cannot show its work | 100% — every rule arrives with the exact source lines attached |
| Scale | — | 50,000+ lines in a single pass |
| Audit posture | An unsourced answer — trust required | Your auditors don’t have to trust the AI. They can check it |
Weighing more than these two? The full vendor matrix — every serious option, refereed.
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.