The deterministic approach covers about 30% of enterprise processes. LLMs can improve the other 70% — but only if the knowledge they receive has the highest signal-to-noise ratio. Our difference lies in three things: repowering existing knowledge, the Enterprise Knowledge Map, and a progressive-convergence cycle that makes answers ever more complete and hallucinations trend to zero.
Throwing raw documents into a bin and hoping for correct answers — the “magic bin” — does not work: noise creates hallucinations. The leap happens when knowledge is sieved, structured and mapped before it reaches the LLM.
Individual tools: 80% of the result depends on the operator, 20% on the AI.
Similarity search retrieves fragments of the original documents and puts them in the prompt.
First structured on the Enterprise Knowledge Map, then delivered to the LLM already noise-free. RAG stays as a targeted tool.
Anyone promising an absolute zero is not credible. We build a virtuous system that improves progressively: Knowledge Guardrailing cuts errors from day one; the continuous-improvement cycle — with controlled refinement of the map — makes hallucinations converge to zero, and the share of complete answers grows month after month.
A user finds a missing or wrong piece of information and reports it with one click (Missing Data Alert).
The report opens a Knowledge Task, anchored to the exact point on the map: product, version, feature.
R&D and PM close the tasks in their own time: deep work stays protected, the flow is not broken.
The knowledge base is corrected and enriched: the same question will not come back unanswered.
Engineering spends 95% of its time on new products and 5% on documentation: imposing more documentation from above does not change these numbers, it fights them. The bottom-up cycle works with them: you document what the market asked for, when and where it's needed.
At every iteration the map covers more cases: the KPI “share of complete answers” grows month after month, and is the honest way to measure convergence toward zero hallucinations.
It is on the complex cases — the ones that deliver real process improvement — that the difference becomes decisive. With clean, structured knowledge the most advanced LLMs reach a 70–90% productivity gain.
| Criterion | Prompt Engineering | “Pure” RAG | KAG (EKRAI) |
|---|---|---|---|
| Knowledge source | Only training + prompt | Fragments of raw documents | Enterprise Knowledge Map on a structured base |
| Signal-to-noise | Low: no company context | Medium: noise enters the context | Maximum: noise removed upstream |
| Medium case (variants, versions) | Made up | Risky: versions mixed | Reliable: map disambiguates first |
| Complex case (tender, BOM) | Impractical | Out of reach | KAG's ground: intent-driven cycles |
| Hallucinations | Frequent | Present — unacceptable in enterprise | Trending to zero, by convergence |
| Improvement over time | None | Static (more docs = more noise) | Systemic: complete answers growing |
| Process integration (ERP/CRM) | Absent | Consultation only | Native: down to the transactional object |
| Productivity gain | Marginal | Moderate, simple cases | 70–90% on real processes |
A note of technical honesty: in EKRAI, RAG does not disappear — it is used where it helps, as targeted similarity search on already clean knowledge. It is the KAG + RAG combination, orchestrated by Progressive Prompting, that makes the difference.
The best way to see the difference is on your own content: a complex tender, a bill of materials, a ticket archive. Bring it to us.