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Reducing Agent Hallucinations by 99% with Ontology-Augmented Retrieval for Mirakalous
Built the knowledge layer behind Mirakalous's Neurostack Ontology Builder, reducing agent hallucination rates from 32% to 0.2% and giving every agentic system the company ships a single grounded model of the business to reason over.
At a glance
Industry
Artificial Intelligence / Enterprise Software
Location
Canada
Successive Highlight
32% → 0.2%
Agent Hallucination Rate
0 Rebuilds
Knowledge Reused Across Every New Agentic System
About the Client
Mirakalous is an AI platform provider building agentic systems for enterprises in regulated industries. Their agents needed to reason over a customer’s organization accurately enough to be trusted in production, not just demonstrate well and stall at the compliance review.
Challenge
Mirakalous’s agents, grounded on conventional retrieval, hallucinated on roughly a third of responses, a rate that disqualifies a system from production in domains where an answer feeds a claim, an authorization, or a compliance decision. The root cause was a missing semantic layer: the same term carried different meanings across different systems, and the relationships between entities existed only in the heads of domain experts. Every agent reconstructed the organization’s logic on its own, and reconstructed it differently, with nothing learned in one workflow carrying over to the next.
Solutions Implemented
Successive Digital owned the complete technology build of Neurostack Ontology Builder and delivered the ontology-augmented retrieval layer that resolved the grounding problem.
Structured Knowledge Graph Construction
The platform converts fragmented enterprise data, documents, systems, and expert knowledge into a governed business knowledge graph, the entities the organization operates on, the relationships between them, the rules that govern them, and the context each fact sits in. Meaning becomes explicit and machine-readable rather than implied.
Graph Construction from Unstructured Sources
Claude performs the ingest extraction that builds the graph, reading the enterprise corpus, policy documents, contracts, internal standards, and operational records, and deriving the entities, relationships, rules, and context that make up the ontology.
Ontology-Augmented Retrieval via the Claude API
Claude also runs in the live query path. When a user asks a question, Neurostack resolves it against the knowledge graph and passes that grounded structure to Claude through the Claude API. Claude reasons over verified context rather than inferring connections from retrieved fragments, which is what removed the failure mode responsible for hallucination.
Model Selection by Query Complexity
A routing layer matches each request to the appropriate Claude model by complexity. Routine lookups are served by a smaller, faster model; multi-hop reasoning and policy interpretation route to a more capable one, holding the cost and latency profile steady as query difficulty varies.
Consistent Answers Across Use Cases
Because agents reason over the same governed model of the business, answers stay consistent across support, sales, operations, and knowledge management rather than varying by which system or prompt produced them.
Reusable Across Agentic Systems
Enterprise context lives in the ontology rather than inside prompts or a fine-tuned model, so the same grounded layer serves every agent built on it. Mirakalous has since deployed it across multiple agentic systems without rebuilding the knowledge beneath each one.
Outcome
Explore key outcomes and business impact delivered.
Agent hallucination rate reduced from 32% to 0.2% through ontology-augmented retrieval powered by the Claude API.
The same grounding layer now powers every agentic system Mirakalous has shipped since, with no need to rebuild the knowledge base for each new use case.
Agents produce consistent answers across support, sales, operations, and knowledge management, rather than answers varying by which system or prompt generated them.
Agent responses are now reliable enough to be used in regulated processes that previously could not go near production.
“A third of our agent responses were unreliable, which meant none of them could go near a regulated process. Grounding the agents in an ontology and reasoning over it with Claude took that to effectively zero. What made it worth building properly is that we only had to do it once, every agentic system we have shipped since runs on the same knowledge layer. Successive built it with us and still runs it.”
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