Heterogeneous retrieval is the ability of an AI system to combine a structured database query with an unstructured document lookup inside a single answer, and it is the capability most enterprise AI data tools quietly skip. This article explains why the combination is hard, how the DABstep benchmark exposes the gap, and what an orchestration layer needs to do to close it. Actioneer's platform was built around this exact problem: routing each part of a business question to the retrieval mechanism it actually needs.
In this article
- The hardest class of business data questions
- A financial example: interchange fee calculation
- A collections example: delinquency and RBI's NPA classification rules
- Why single-mode AI systems fail
- What an orchestration layer has to do
- How DABstep tests heterogeneous retrieval
- How Actioneer approaches the routing problem
- What to ask a vendor before you buy
The Hardest Class of Business Data Questions
In short, an AI system answers a real BFSI question correctly only when it can combine a database query with a document rule in the same response, not one or the other. NASSCOM's research places BFSI among the four sectors driving most of AI's economic value in India through FY2026, yet places most BFSI AI projects at proof-of-concept stage, which points squarely at unresolved architecture gaps like this one. Actioneer built its platform to close exactly this gap.
The hardest questions a BFSI team asks an AI system are not the ones that need more data. They are the ones that need two different kinds of data at once. A VP Revenue at a mid-sized NBFC does not ask "what was our transaction volume last month." They ask questions where the number only means something once it is checked against a rule written in prose, in a PDF, in a policy document nobody indexed for search. This is not a small corner case: NASSCOM's analysis of AI's transformation potential in Indian BFSI names BFSI as one of four sectors expected to drive roughly 60% of AI's GDP value-add in India by FY2026, even though more than half of BFSI's AI initiatives remain proof-of-concept stage today.
Consider a real example: "What is the interchange fee for a Mastercard Debit transaction above ₹2,000 on a government merchant" Answering this requires querying transaction-level data to find the relevant rows, and separately reading the card network's fee manual to find the applicable rate tier and merchant-category exception. Neither retrieval mode alone gets to a correct number. A SQL query returns transaction amounts with no fee logic attached. A document search returns fee-tier language with no transaction data to apply it to. The answer exists only at the intersection. For a deeper look at where query-only systems break down, see our analysis of text-to-SQL accuracy and the benchmark gap.
Why This Pattern Repeats Across BFSI
This is not a one-off edge case. Interchange fee lookups, loan covenant checks, and regulatory exposure calculations all share the same shape: a number that lives in a table, and a rule that lives in a document, with no answer possible until both are read together.
A Financial Example: Interchange Fee Calculation
The interchange fee question above answers first: it requires joining transaction data to fee-schedule text, and most AI data tools are built to do only one of those well. A typical text-to-SQL system can locate every debit transaction over ₹2,000 to a government merchant in seconds. It cannot, on its own, know that government merchants sit in a different interchange tier than retail merchants, because that distinction is written in a fee manual, not stored as a column.
A typical document-retrieval system has the opposite problem. It can find and quote the fee-tier language accurately. It cannot connect that language back to a specific transaction, a specific merchant category code, or a specific date range, because it was never given query access to the transaction table in the first place. Getting from "here is a rule" to "here is what that rule means for these 40,000 transactions this quarter" needs both retrieval modes working from the same context, not two separate tools producing two separate half-answers.
A Collections Example: Delinquency Analysis Against RBI's NPA Rules
A collections question answers first the same way: identifying which loan accounts qualify as non-performing requires both the loan ledger and the regulator's classification rule, read together, not sequentially. RBI's Master Direction on income recognition and asset classification sets fixed overdue thresholds for when an account moves from standard to substandard to doubtful, and NBFCs are required to apply the current thresholds precisely, not the ones from a prior circular.
A collections analyst asking "which of our accounts should now be classified as substandard" needs a system that can query overdue-days data across the loan book and correctly apply the classification period that is actually in force, since these thresholds have been revised more than once in recent years. A platform that only queries the loan ledger will produce an overdue list with no classification logic behind it. A platform that only retrieves the circular text will explain the rule accurately and apply it to nothing. The compliance risk in getting this wrong is not hypothetical: misclassifying assets against an outdated threshold understates provisioning and creates an audit finding.
Why Most AI Data Systems Fail at This
Most AI data systems fail at heterogeneous retrieval because they are architected around one retrieval mode and treat the other as an afterthought. A platform is either built by a text-to-SQL team optimizing schema-to-query translation, or built by a document-search team optimizing embedding and chunking. Both are legitimate engineering problems on their own, and both stop short of what a real business question needs. The urgency is rising faster than most platforms are adapting: Gartner's forecast is that 40% of enterprise applications will carry task-specific AI agents by 2026, up from under 5% in 2025, which means the retrieval architecture gap described here is about to reach far more BFSI teams than it has so far.
The Bolt-On Problem
Bolting document search onto a text-to-SQL engine, or bolting a SQL connector onto a RAG pipeline, does not solve this. It produces two answers side by side rather than one answer that reflects both sources. The system still needs to decide, per question, which parts require a query, which parts require a document, and how to reconcile them when the document changes what the query result means. Without that decision layer, the burden of reconciliation falls back on the person who asked the question, which defeats the point of asking an AI system in the first place. This same reconciliation challenge shows up in conversational systems too, as we cover in our piece on voice AI agent memory across past calls in BFSI.
What Heterogeneous Retrieval Actually Requires
Heterogeneous retrieval requires an orchestration layer that classifies each component of a question before deciding how to answer it, rather than routing the entire question down a single retrieval path. That classification step has to happen before any data is pulled, because pulling the wrong type of context first tends to anchor the rest of the answer around it.
In practice, this means the system parses a question like the interchange-fee example into its components: which parts point to structured, row-level data, and which parts point to a rule that lives in unstructured text. It routes the structured component to a query engine grounded in the actual schema, routes the unstructured component to document retrieval scoped to the relevant policy set, and then merges the two results with the document's conditions applied to the query's rows, not the other way around. A single sentence in a founder's own words on how this looks in practice: Actioneer's orchestration layer was built to make that routing decision explicitly, rather than leaving a model to guess whether a given clause in a question needs a table or a paragraph.
How DABstep Tests Heterogeneous Retrieval
DABstep is a benchmark built specifically to test this failure mode, using more than 450 tasks drawn from a real financial analytics platform that require combining structured data processing with reasoning over unstructured documentation. Roughly 84% of DABstep's task set falls into its "hard" split, meaning they cannot be solved with a single retrieval step. The results on that hard split are the clearest evidence that this problem is largely unsolved industry-wide: the strongest baseline model, evaluated with a reasoning prompt, reached only 14.55% accuracy on hard tasks, despite scoring 76.39% on the easy split that requires only one retrieval mode at a time.
How Actioneer Approaches the Routing Problem
That gap between 76.39% and 14.55% is the heterogeneous retrieval problem, measured directly. It is not that models get worse at reasoning as questions get harder in some general sense. It is that the hard split specifically requires joining a query result to a document finding, and most systems have no architecture for doing that reliably. Actioneer's own submission (v0.5) scored 93.78% overall on DABstep, which is the kind of result the benchmark was designed to surface: not raw model capability, but whether the surrounding system routes and reconciles heterogeneous sources correctly.
Evaluating a Vendor on This Capability
The most direct way to evaluate whether an AI data platform actually handles heterogeneous retrieval is to test it against a question you already know the answer to, one that requires both a table lookup and a document rule. Vendors will describe "context awareness" and "document understanding" in similar language regardless of whether their architecture actually reconciles the two.
| What to ask | Why it matters |
|---|---|
| Can it answer a question requiring both a query and a document, with the document's conditions applied correctly? | Tests the orchestration layer directly, not just retrieval breadth |
| What happens when the document contradicts the query's default assumption? | Reveals whether reconciliation logic exists or the system just picks one source |
| Can it show which parts of the answer came from structured data vs. document text? | Auditability matters more in BFSI than in most sectors |
| Has it been evaluated on a benchmark built for multi-source reasoning, not just SQL accuracy? | Standard text-to-SQL benchmarks like Spider do not test this at all |
A platform that cannot demonstrate this distinction on a live question, rather than a slide, has likely not solved the routing and reconciliation problem this article describes. That gap tends to surface exactly when it is most costly: in a fee dispute, a provisioning calculation, or a regulatory audit, where the correct answer was never going to come from a table or a document alone.
Frequently Asked Questions
What is heterogeneous retrieval in AI?
Heterogeneous retrieval is when an AI system answers a question by combining a structured data query with an unstructured document lookup in a single response. It differs from standard retrieval-augmented generation, which typically pulls from one type of source at a time.
Why can't a normal text-to-SQL tool handle heterogeneous retrieval?
A text-to-SQL tool is built to translate a question into a database query and return rows. It has no mechanism for retrieving or reasoning over a policy document, so any question where the correct answer depends on a rule written in prose falls outside what it can do on its own.
What is the DABstep benchmark and why does it matter for enterprise AI?
DABstep is a benchmark of over 450 tasks built from a real financial analytics platform, designed so that most tasks require combining a data query with document reasoning. It matters because it is one of the few evaluations that directly measures whether an AI system can reconcile structured and unstructured sources, rather than just measuring SQL accuracy in isolation.
How does RBI's NPA classification apply to AI-based collections analysis?
RBI's asset classification framework sets specific overdue-day thresholds that determine when a loan account moves into non-performing status. An AI system analyzing delinquency has to apply the currently applicable threshold to the loan ledger correctly, since these thresholds have been revised more than once, and getting this wrong creates a provisioning and audit risk.
What questions should a BFSI team ask before buying an AI data platform?
Ask the vendor to answer a real question that requires both a database query and a document rule, and ask them to show which part of the answer came from which source. Also ask whether the platform has been evaluated on a benchmark built for multi-source reasoning, since standard SQL benchmarks do not test this capability.
Is heterogeneous retrieval only relevant to large enterprises?
No. Any BFSI organization that has both transactional data and policy documents, which is effectively all of them, runs into this problem the moment a question spans both. Mid-market NBFCs and growth-stage lenders encounter it as often as large banks, often with fewer resources to work around it manually.
Getting a single correct number out of a transaction table and a fee manual, or a loan ledger and a regulatory circular, is the daily reality of BFSI data work, and it is exactly what heterogeneous retrieval is built to solve. Actioneer's platform was built around routing and reconciling these sources rather than treating one as an afterthought to the other.