PAR vs RAG
Same retrieval foundation.
No LLM in the decision path.
PAR keeps the proven embed-index-retrieve stack that powers 70% of enterprise AI. It replaces the generation step with human-approved pattern matching.
vs. RAG
RAG generates. PAR matches.
Zero LLM calls in the production path. Every decision has a named approver and a complete audit trail. Defensible to regulators by design.
PAR shares 60% of its technical stack with RAG. The same embeddings, vector indexes, and retrieval primitives. The difference is what happens after retrieval: RAG feeds context to an LLM that generates new text. PAR matches against patterns your compliance team already approved.
RAG Pipeline
- LLM generates new text on every call
- Hallucination risk — answers not grounded in retrieved content
- Non-deterministic — same query, different answer each time
- No native audit trail — LLM reasoning is not reproducible
- Per-token cost scaling — $62K/month at 10M records
- No human gate before retrieval reaches production
PAR Pipeline
- Matches human-approved insights — no generation step
- Zero hallucination — returns the approved insight directly
- Deterministic — same input, same result, every time
- Complete audit trail — every match traceable to a named approver
- Flat-rate matching — $6K/month at 10M records
- Human-in-the-loop — nothing reaches production without sign-off
| Dimension | RAG | PAR |
|---|---|---|
| Post-retrieval | LLM generates | Pattern match |
| Determinism | No | Yes |
| Hallucination risk | Present | Zero |
| Audit trail | Typically none | Every decision |
| Human oversight | None before output | Required |
| Throughput | 50–500 req/sec | 400K decisions/sec |
| Cost at 10M records | ~$62,000/mo | $6,000/mo |
"RAG generates. PAR matches."
Same retrieval foundation. Different guarantee.
See PAR on your data.
Run a proof-of-concept. See what the PAR pipeline surfaces and what it saves.
$10K pilot. 6 weeks. Credited to annual.