Claim verification product
Knowledge & Document Software · Claim Verification / NLP Platform
Turning free-form claims into structured semantic units for verification
The product uses a controlled semantic representation underneath AI reasoning. Free-form factual claims are decomposed and classified through a structured ontology, then evaluated using a combination of rules, embeddings, vector retrieval and LLM reasoning.
Claim Verification / NLP Platform
Semantic Decomposition
Where the claim becomes structured units.
- Claim decomposition
- 114-token ontology
- Hybrid classification
- Vector-backed verification
AI verification and classification product.
The starting point
Unconstrained model reasoning is difficult to classify or evaluate consistently.
What had to change
The product needed a repeatable, structured representation of a claim.
What couldn't break
The ontology had to stay stable while the hybrid AI and retrieval stack around it evolved.
Why this was hard
The system has to bridge ambiguous natural language and deterministic structure.
What Datics changed
Datics built claim decomposition over a 114-token ontology, evaluated through semantic embeddings, vector retrieval, rules and LLM reasoning.
Claim decomposition
Free-form claims broken into semantic units.
114-token ontology
A controlled vocabulary underneath the reasoning.
Hybrid classification
Rules, embeddings and LLM reasoning combined for classification.
Vector-backed verification
Retrieval supports evidence for each unit.
How it works
Connects to: Qdrant vector search plus Anthropic and OpenAI model services.
- 1
Claim Intake
Where a free-form claim enters the system.
- 2
Semantic Decomposition
Where the claim becomes structured units.
- 3
Verification Workflow
Where units are classified and checked.
Runs within: AI verification and classification product.
Engagement at a glance
Free-form factual claims can be decomposed into structured semantic units for more repeatable downstream classification and verification.
- Semantic model
- 114-token ontology
- Reasoning stack
- Rules, embeddings, retrieval and LLMs
- Status
- Ongoing AI product
Under the hood
- Datics role
- AI product engineering across NLP classification, retrieval and decomposition.
- Integrations
- Qdrant vector search plus Anthropic and OpenAI model services.
- Deployment
- Dockerized application with asynchronous background processing.
- Production boundary
- AI verification and classification product.
Technology
What this unlocked
A structured semantic layer gives downstream verification and evidence workflows a repeatable representation to reason over rather than relying solely on opaque model interpretation.
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