Most enterprise “knowledge” isn’t prose - it’s tables: price books, SLAs, entitlement rules, territories, SKUs, and KPIs spread across Salesforce objects, reports, and connected systems. Classic retrieval‑augmented generation (RAG) excels on documents, but it struggles on tables - especially when answers span multiple tables.

New research proposes a table‑aware RAG architecture, T‑RAG, that organizes tables into a hierarchical memory and uses multi‑stage retrieval to deliver higher accuracy and faster responses, exactly the outcomes CIOs need in production AI. (arXiv)

The 60‑Second Version (for CIOs)

• Problem: Traditional, text‑centric RAG underperforms on enterprise tables (Salesforce objects, reports, exports).

• What’s new: T‑RAG builds a hierarchical index over tables, does coarse‑to‑fine retrieval, and sends graph‑aware prompts to the LLM—leading to better accuracy, recall, and runtime on a multi‑table benchmark.

• Why you care: More right answers from your tabular data = higher case deflection, faster handle time, and lower model spend.

• Where Convopro fits: ConvoPro runs natively inside Salesforce, is model‑agnostic, and ships flow‑native automation—a practical foundation to operationalize T‑RAG patterns in customer service and ops.

What the Research Actually Says (Plain English)

1) Hierarchical memory over tables.
T‑RAG doesn’t treat each table as a blob. It first linearizes table structure (caption + headers) and then extracts three kinds of features: semantic (embeddings), structural (format/POS counts), and heuristic (TF‑IDF). It clusters tables separately per feature type, forming a heterogeneous hypergraph that captures different “views” of similarity.

2) Coarse‑to‑fine retrieval.
At query time, T‑RAG picks typical nodes per cluster (fast pruning), assigns the query to the union of best‑fit clusters across those feature types (high recall), then builds a small, dense local subgraph and runs personalized PageRank to rank final tables (precision).

3) Graph‑aware prompting.
The LLM doesn’t just see raw rows; the prompt includes which tables are most connected/relevant and guides structured, stepwise reasoning to stitch answers across tables. (The paper calls this “hierarchical long CoT” and multi‑step guidance.)

So what?
On a new benchmark where many queries require multiple tables, T‑RAG shows state‑of‑the‑art performance across accuracy, recall, and speed - critical for production SLAs.

Why This Matters in Salesforce Shops

If your teams live in Salesforce, your “documents” are objects and list views, not PDFs. That means:

• Answers are scattered across Cases, Entitlements, Products, Knowledge, and custom objects.

• Keys aren’t clean. Real questions rarely align neatly to primary/foreign keys; the “join” often lives in headers, captions, or field semantics.

• Latency budgets are tight. Service agents can’t wait; you need fast, correct retrieval and a prompt that helps models reason over tables every time.

ConvoPro is designed to meet those realities: native Salesforce deployment, model‑agnostic routing, and flow‑native automation to act on answers (update a Case, trigger an approval) once you have them.