Retail · AI Agent Enablement

AI analytics agent for a national retailer

A national retailer's AI analytics agent worked in pilot — but as scope grew, the context it had to reason over exploded, and accuracy, speed, and cost all slipped. Migrating its context layer onto Dataplane's ontology platform fixed all three.

Time to value: 6 weeks

Problem

A pilot that buckled as scope grew

The retailer had deployed an enterprise AI agent to let its teams ask questions of their data in plain language. In early pilots it worked. But as scope widened across more domains, sources, and edge cases, the context the agent had to reason over exploded. Accuracy slipped, hallucinations crept in, and cost and latency climbed together.

The root cause was structural. The off-the-shelf agent pulled its context from a document repository: definitions and documentation retrieved as text and dropped into the prompt. That left no control over the harness around it: no traceability into how an answer was reached, no access control over what the agent could see, and no way to thread in user context. Worse, those documents had accreted contradictions throughout the semantic model — the same term defined two ways, overlapping concepts, definitions gone stale. The agent grounded confidently on conflicting meaning.

Approach

Migrate the context layer onto an ontology

We rebuilt how the agent gets and uses context, splitting the work in two: a governed ontology to hold what the data means, and a custom harness to enforce everything the model shouldn't be left to guess.

The conceptual model

A governed ontology, built to compose

Migrated the context layer onto a governed ontology, built through automation with expert judgement in the loop.
Separated the conceptual model from its bindings, so one model binds across many source systems.
Modularized the ontology so its elements compose and reuse across use cases.

The custom harness

Deterministic control around the model

Owns the concerns the model shouldn't guess: auth, access control, and user context.
Enforces ambiguity handling and forced context reads, holding the agent to the conceptual model.
Walks the ontology graph to power semantic and lexical search across the context layer.

Data Quality, free from the same model

Because every term resolves through the ontology, Data Quality comes for free. The definitions that ground the agent's answers also flag the records that violate them.

Outcome

The same agent — now accurate, fast, and cheap

~20%

More accurate

~40%

Cheaper, by token usage

~10%

Faster, end to end

Grounded in the ontology, the same agent outperformed every approach it had been running on. Accuracy reached 99%, closing out the wrong answers that had eroded trust, while token cost and end-to-end latency both came down. Better answers and lower cost arrived together — not as a trade-off.

Metric	Context Injection	Document RAG	Ontology
Accuracy	27%	80%	99%
Latency	32.3s	25.3s	22.5s
Tokens in	5.2k	7.4k	4.4k
Tokens out	1,530	1,008	868
DB calls	11.1	1.6	1.5

The foundation under this benchmark generalizes well past it. A governed model kept separate from its bindings, wrapped in a harness that carries auth, access, and grounding, becomes infrastructure that scales to new sources, use cases, and domains.

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