PRISM unifies data and analytical strategy into a single platform.
Seven cooperating layers deliver natural-language analytics with verifiable reasoning at every step.
The system remembers your team's expertise. AI executes the strategy.
The data dictionary and the analysis-strategy dictionary are kept separate — data and analysis methods each managed against verified standards.
Goal checks and comparison of multiple strategies catch any wavering in AI answers. Measured across 2,000+ comparison experiments.
Successful analysis strategies are automatically collected and accumulated as organizational knowledge — an auto-accumulating strategy approach.
From human intent to physical storage, each layer is separated by single-responsibility principle and exposes no internal implementation. This structure guarantees long-term maintainability and accuracy simultaneously.
Natural language queries and strategic intent. The system supports judgment — not just understanding and answering questions.
The senior AI classifies the question intent, selects and assembles an analysis strategy, and verifies the validity of results.
Past successful patterns are automatically collected and reused. New questions are resolved by combining steps of existing analysis strategies.
A shared state space used by all AI Agents. External research proves a higher success rate than simple point-to-point structures.
A 3-phase sequence from onboarding to operation — generating, expanding, and evolving the analytics universe.
Business semantics abstracting 1,000+ tables. Fields, relationships, synonyms, and permissions unified in one place.
Connects directly to the operational DB and DW customers already own. Zero-copy — no data movement required.
How the role of SI / consulting practitioners using PRISM for client analytics projects changes. Manual query validation is delegated to the AI training ground via autonomous learning and reuse of verified analysis — so consultants focus on directing strategy and scoring quality.
When the consultant sets the direction, the AI training ground self-generates hundreds of queries, accumulates correct answers in the verified strategy store, and re-diagnoses and relearns from wrong answers. The three steps cycle endlessly — driving serving accuracy higher.
Starting from a single seed query, parameters, synonyms, and conditions are automatically varied to self-generate hundreds of queries — replacing consultant manual work.
Only analysis strategies passing comparison validation are promoted as correct answers into the verified strategy store. As they accumulate, serving accuracy rises and they build up as shared organizational assets.
When discrepancies, automatic validation warnings, or negative feedback are detected, AI self-diagnoses → relearns a corrected strategy → re-validates. A sample scoring step blocks wrong patterns from setting in.
Numbers are the language of claims. Internal measurements and academic/industry evidence are organized in a single table, with sources fully traceable.
The AI training ground handles the consultant's manual query validation. Autonomous learning loads verified analysis strategies, and consultants only need to do 3 things: direct strategy, score final answers, and score sampling accuracy.
AS-IS · Full manual survey (hundreds of hours)
TO-BE · AI training ground autonomous learning (direction only)
| Metric | Value | Source |
|---|---|---|
| Natural Language → Data Query Accuracy | 99%+ | Internal benchmark |
| Meta Routing Accuracy | 98% | 2,000+ comparison experiments |
| Multi-step Reasoning Chain PASS | 95%+ | Internal validation |
| Self-Learning Loop Success Rate (target) | 90%+ | Design target |
| Strategy Adoption Rate | +30% | Before/after comparison |
In an era where Palantir and Databricks dominate data and infrastructure, PRISM opens an entirely new category — turning analytical methodology itself into an asset.
| Capability | PRISM | Palantir | Databricks |
|---|---|---|---|
| Category | Analysis-Strategy Platform | Decision Platform | Lakehouse / ML |
| Analysis Strategy Dictionary | ✓ Exclusive · reusable | — | — |
| Implementation Lead Time | 4–5 months | 12–18 months | 9–14 months |
| Output | Answer + SQL + reasoning + follow-ups | Decision app | Notebook / model |