The Adaptive Architecture Protocol: 21 Ways to Search Your Data

Most AI vendors sell you "Vanilla RAG." They split your text into chunks, turn them into numbers (embeddings), and hope for the best. It works for simple questions like "What is the liability cap?" but fails completely on complex tasks like "Compare the risks in this contract versus the addendum."

At OpsSolved, we don't guess. We use the Adaptive Architecture Protocol.

Why "Vanilla RAG" Fails in Production

Vanilla RAG is a hammer. But your data isn't always a nail. It fails for:

1. Global Trends: It only looks at a few small pieces of text. It can't "see" the big picture across 1,000 documents.
2. Relationships: It doesn't understand how Document A relates to Document B.
3. Complex Tables: Tables are notoriously hard for basic AI. If your data is in a complex financial table, Vanilla RAG will likely hallucinate.

Our Protocol: 21 Distinct RAG Architectures

We maintain a toolkit of 21 distinct architectures. Our system automatically benchmarks your data to select the winning strategy. Here are the core types:

1. GraphRAG (The Relationship Map)

Instead of just searching for words, we build a "Knowledge Graph." It maps out entities (people, companies, dates) and how they connect.

• Best for: Finding "Who worked with Company X on Project Y?"

2. Hybrid Search (Semantic + Keyword)

We combine semantic search (understanding meaning) with old-school keyword search (finding exact words).

• Best for: Regulated industries where specific legal terms or product IDs must match exactly.

3. Parent-Child Indexing (Context Awareness)

We search using small, precise snippets, but we show the AI the larger surrounding context to ensure it actually understands the answer.

• Best for: Legal clauses where context is everything.

4. Table-Aware RAG (The Data Specialist)

We extract the structure of your tables—rows, columns, and headers—before the AI reads them. No more "guessing" what a cell means.

• Best for: Financial reports, M&A audits, and technical specs.

5. Multi-Hop Reasoning (The Researcher)

The system doesn't just look once. It finds a clue in Document A, realizes it needs more info from Document B, and "hops" between them until it has the full story.

• Best for: Comparing terms across multiple contracts or addendums.

The Benchmarking Process: Data Science, Not Guesswork

We don't pick a strategy because it's trendy. We use our Automated Benchmarking Suite:

1. Generate Test Sets: We create 200-500 hard questions based on your actual data.
2. The Race: We run these questions through multiple architectures simultaneously.
3. Measure: We track Recall (did we find it?), Precision (is it right?), and Latency (is it fast?).
4. Select: The architecture that mathematically proves it's the most reliable for your data wins.

Conclusion

Industrial-grade AI requires the right tool for the job. Don't settle for a vendor who only has a hammer. Demand the Adaptive Architecture Protocol.

OpsSolved: Engineering the right path to your data.