Retail · AI Agent Enablement

AI analytics agent for a national retailer

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

Time to value: 6 weeks

Problem

A pilot that buckled as scope grew

—The client developed an enterprise AI agent to get faster access to data-driven answers. It worked in early pilots, but as scope widened across domains, the system started to break.
—Accuracy slipped, hallucinations crept in, and cost and latency exploded.
—The off-the-shelf agent used a document-based knowledge repository and a black-box harness: no traceability, no access control, and no way to control how business context was being used.
—As the scope grew, the knowledge repository accumulated contradictions throughout the documents, leading to a semantic layer that hurt more than it helped.

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 touch.

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.

27%

80%

99%

Context Injection

Document RAG

Ontology

Agent answer accuracy

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 we implemented as part of this engagement is built to last. A governed model with the proper deterministic guardrails becomes infrastructure that scales to new sources, use cases, and domains.

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