In many real deployments (fintech, enterprise workflows, agentic systems), the failure mode isn’t latency or cost — it’s hallucinations that sound confident and pass surface checks. Prompt engineering helps, but it doesn’t scale once systems grow long-running, tool-using, or multi-agent.
Verdic takes a different approach:
Define an explicit intent + scope contract for what the model is allowed to output
Validate LLM outputs before execution, not just inputs
Block or flag responses that drift semantically, contextually, or domain-wise
Keep enforcement deterministic and auditable (not “best effort” prompts)
It’s designed to sit between the LLM and your application, acting as a guardrail rather than another model.
This is still early, and I’m especially interested in feedback on:
Where this breaks down in real systems
How teams currently handle hallucinations beyond prompts
Whether deterministic enforcement is useful or too restrictive in practice
Site: https://www.verdic.dev
Happy to answer questions or share implementation details if useful.
