Veterinary Clinical Decision Support
A veterinary software startup engaged Clearlead to investigate whether AI-powered clinical guidance could be delivered to a standard that matched clinical expectations, without hallucinating or drawing on unsupported sources.
The client
- A veterinary software startup
- Veterinary medicine, Digital health
- United States
Overview
The client builds software for veterinary clinicians and came to Clearlead to investigate whether AI-powered clinical decision support was deliverable to a standard that would meet clinical expectations.
Clearlead designed and built a multi-step pipeline combining retrieval-augmented generation with a deterministic drug dosing layer, then validated outputs against real clinical test cases with the client's veterinary team.
The engagement demonstrated that the mechanism works: AI-generated guidance can match what an experienced vet would produce, with the architecture providing the grounding guarantees needed for a clinical context.
Methods
- LLMs
- RAG
- Clinical decision support
- Multi-step AI pipelines
- Guardrailed AI
Engagement type
- Design and build
Duration
The situation
The client builds software for veterinary clinicians and wanted to understand whether AI could be used to generate clinical decision support guidance that met an acceptable standard. They came to Clearlead because of specific expertise in building AI systems for contexts where output quality and grounding need to be demonstrable, not assumed.
Anyone working seriously with large language models in healthcare understands the core risk: models can generate plausible-sounding but incorrect outputs. In a clinical context that risk has to be designed out of the architecture from the start. The system needed to produce guidance grounded in verified clinical sources, with drug dosing information drawn from a reliable reference system rather than generated by the model. The question was whether this could be built to a standard that would hold up to clinical scrutiny.
The engagement
Clearlead led the design and build of a clinical decision support pipeline, with outputs reviewed against real test cases by the client's veterinary team throughout development.
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Retrieval-augmented clinical reasoning — clinical guidance is grounded against a curated knowledge base of veterinary guidelines and reference material. The model cannot draw on general parametric knowledge for clinical claims; all reasoning is anchored to retrieved, domain-filtered sources.
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Deterministic drug dosing — dosing and fluid rate calculations are handled by code, not the language model. The model places markers in its output; the pipeline resolves them to verified values from a structured reference database. This removes a category of hallucination risk from a part of the output where errors would have direct clinical consequences.
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Multi-step pipeline with committed intermediate outputs — the pipeline is structured as a sequence of focused steps, each producing a committed intermediate result before the next step begins. This prevents the model from conflating distinct reasoning tasks and makes each step independently testable.
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Structured output validation — every output passes through a rule-based validation step before being returned, covering scope of guidance, language constraints, and the handling of uncertainty in the clinical picture. The validation runs as a hard gate outside the model; the clinical safety boundaries are enforced structurally, not reliant on prompt instructions or model compliance.
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Clinical sign-off at each stage — outputs were reviewed against a fixed test case set by the client's veterinary team throughout development. This shaped the clinical reasoning rules iteratively before they were formally stabilised, and provided the evidence base to confirm the system was meeting expectations.
Outcomes
- The MVP demonstrated that AI-generated clinical guidance can match what the client's veterinary team produced for the same cases. Outputs were grounded in retrieved clinical sources and drug dosing calculations were handled deterministically, with no hallucinated clinical claims observed across the test case set.
- A clean API integrated with the client's existing frontend, ready for the client to evaluate in a live clinical context.
- A versioned set of clinical reasoning rules and a fixed test case suite, giving the client a reproducible baseline against which future iterations can be assessed.
Similar applications
The underlying approach applies wherever AI output quality and grounding need to be demonstrable rather than assumed.
Clinical decision support
Human medicine, physiotherapy, or nursing tools where outputs must be grounded in verified sources and stay within a defined scope of practice.
Pharmaceutical and formulary guidance
AI-assisted guidance requiring a hard boundary between model-generated reasoning and safety-critical reference data.
Legal research tools
AI that surfaces relevant material without generating conclusions that go beyond what the sources support.
Financial compliance and risk advisory
Outputs that need to be traceable and grounded in verified regulatory material.
Deterministic safety layers in AI systems
Systems where specific outputs must be guaranteed correct rather than probabilistically accurate, requiring rule-based components alongside model inference to enforce hard limits regardless of model performance.
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