ARF —
Advanced Retrieval Framework
Structured Query Intelligence
ARF begins by transforming every user input into a clean, standardized representation.
This ensures the system understands intent precisely, not just surface-level keywords.
Deterministic, High-Accuracy Retrieval Engine
Unlike agentic systems driven by LLM reasoning,
ARF uses a rule-based + ML-driven architecture to guarantee predictable and verifiable results.
Efficient Cost by Design
LLM are the most expensive part of modern AI systems.
ARF is engineered to minimize dependency on large language models, while taking advantage of its feature as a component not as a 'brain'.
By handling query understanding, routing, retrieval, and verification with deterministic logic and lightweight ML components.
Comparison
| Capability | Standard RAG | Agentic RAG (LLM-Driven) | Advanced Retrieval Framework |
|---|---|---|---|
| How it works | Basic search + AI answer | LLM Agents decides search steps | MLP decides configuration of Adaptive RAG |
| Pipeline Control | No controller | Controlled by LLM | Controlled by Nueral Network |
| Query Understanding | Limited | Query Reformulation | Query Expansion + Query Reformulation |
| Accuracy | Medium | Medium–High | High |
| Hallucination Risk | Medium | Low–Medium | Very Low |
| Consistency | Medium | Low | High |
| Latency | 1–3 seconds | 5–15 seconds | 0.6/12 seconds |
| Cost per Query | Moderate | High | Low |
| Caching & Reuse | None | Limited | Built in |
| Domain Handling | Generic | Flexible but inconsistent | Structured with annotation |
| Scalability | Good | Poor | Excellent |
| Set Up Time | Short | Medium | Longer Initial Setup |
| Ideal Use Case | Basic Q&A, general info | Research workflows, tool-using AI | Legal search, compliance, and any high-accuracy domain |