Medical administrative AI for high-trust operations

Buffaly helps healthcare teams automate operational workflows like eligibility, intake, EOB handling, denial investigation, revenue-cycle support, and audit-heavy back-office work. LLMs assist with language, but Buffaly controls execution through typed, ontology-grounded, auditable workflows.

Buffaly is a technology stack developed by IntelligenceFactory.ai. We open source core components including ontology storage, ProtoScript, deterministic NLU, and graph learning, while enterprise engagements focus on deploying governed operational automation in high-trust environments.

LLMs Under Control

LLMs are useful for language tasks, but they are not trustworthy as the control plane for high-consequence workflows. Buffaly is the control layer: it constrains model-assisted outputs to typed contracts, validates them against an ontology-backed policy graph, and emits an auditable trace that operators can review.

Isolating protected data from the model

Standard compliance frameworks, such as HIPAA in healthcare, help ensure models are hosted securely, but they can still require sensitive data to enter model context. Buffaly enables a stronger architectural baseline by enforcing a strict runtime boundary. Teams can run model-assisted workflows while keeping protected records outside the model context window.

Compliant model infrastructure

Sensitive data is sent into model context to complete the task. Protection depends primarily on vendor controls, agreements, and compliance posture after data reaches the model endpoint.

Quarantined runtime boundary

Protected data remains inside Buffaly's execution environment. The model receives only controlled views, typed references, or bounded access paths instead of raw sensitive payloads.

Healthcare makes this requirement obvious, but the same boundary is valuable anywhere regulated or sensitive enterprise data must stay outside model context.

How Buffaly grounds and validates

Buffaly uses structured ontologies (ICD-10, SNOMED-CT, custom domain ontologies) as the source of truth for interpretation and action. LLMs may assist with language normalization or proposal generation, but Buffaly performs validation and policy enforcement against ontology-backed constraints and produces an auditable reasoning trace.

How Buffaly is used in production

• Deployed inside your VPC or on-prem where required.
• Explicit tool boundaries and approval gates for any side effects.
• Deterministic policy and ontology resolution for compliance-critical decisions.
• Evidence by design: every decision and action produces a trace suitable for audit.

The Two Pillars of Buffaly

Buffaly treats "what the agent knows" and "what the agent can do" as versioned code, avoiding the unpredictability of ephemeral prompts.

Typed Contracts > Prompt Glue

Buffaly enforces correctness at the runtime level. We don't negotiate correct behavior in a system prompt; we enforce it via ProtoScript typed signatures, ensuring auditable, constrained actions.

// Prone to unconstrained generation
{
  "name": "update_directory",
  "description": "Runs a script.",
  "parameters": {
    "type": "object",
    "properties": {
      "target_path": { "type": "string" }
    }
  }
}

// Strict, safe, and bound to Ontology
[SemanticProgram.InfinitivePhrase("to build a visual studio solution from a build profile")]
prototype ToBuildVisualStudioSolutionFromBuildProfile : OpsAction
{
    function Execute(BuildProfile buildProfile) : string
    {
        if (buildProfile?.Solution == null)
            return "Invalid BuildProfile: Solution is required.";

        string buildOutput = VsProjects.BuildSolution(...);
        string output = ToTruncatedText(buildOutput, 4000);
        return "Build Solution: " + buildProfile.Solution.SolutionName;
    }
}

Auditability & Full Execution Tracing

"Explainable AI" usually means asking the model to guess why it did something post-hoc. Buffaly doesn't guess. It produces a deterministic execution trace of every action, parameter, and referenced prototype. You gain complete observability grounded in execution artifacts, not narrative.

Discuss a high-trust agent deployment

Include your industry, compliance constraints, deployment environment (VPC/on-prem), and the workflows you want the agent to run. We will respond with next steps and a proposed technical path.