The Truth Layer for

Enterprise AI

95% of enterprise generative AI pilots deliver no measurable return, according to MIT's 2025 research. Often the bottleneck is proving to the people who sign off on the deployment that the model works.

Find out where your evaluation process stands for free and get the report in your inbox within a week.

Evaluations delivered
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Vetted subject matter experts
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Why Most AI

Products Don’t Scale

Built as experiments, not systems. Most AI projects break when they meet real-world complexity. Without the right evaluation, ownership and evidence, scale becomes impossible.

Performance Risk

You can't see failures before your users do.

Unproven ROI

You can't prove model quality to compliance, legal or the board.

Expert bottleneck

You can't source credentialed reviewers fast enough to evaluate at all.

Start Free. Scale

When Ready.

You don't need a sales call to find out if this works. Start with a free assessment of your evaluation process, test us on your real outputs, then scale to a scoped engagement when the evidence stacks up.

Everything above the tiers is self-serve. You decide when, or whether, to talk to us.

The CACTUS

Capability Stack

Everything CACTUS offers in EaaS rests on five proprietary capabilities

Production-Grade JSON Outputs

Your Systems Can Consume

Every evaluation produces machine-readable outputs: confidence scores, audit metadata, expert attribution and structured annotations, ready for your data pipeline.

Speech Quality Evaluation | AI Training Data Validation

Accessibility Compliance | Publisher Content Audit

Strategic Solutions for Every

AI Milestone

Whether you're validating a pilot or outsourcing global governance, you get dedicated subject matter experts and the technical rigour your project demands

Tier 1

Targeted Validation

Tier 2

Packaged Evaluation

Tier 3

Managed Governance

Not ready for a scoped engagement? Start with the free Readiness Assessment above and let the evidence decide.

Don't Guess.

Know!

Start with the free Readiness Assessment or AI Output Trust Audit.
Choose your path: questionnaire, send sample outputs, or 30-minute call. Reports in one to four weeks.

No cost, no commitment. We'll email your full questionnaire straight away and your report within a week of your answers.

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Speech Quality Evaluation Output",
  "type": "object",
  "required": ["evaluation_id", "confidence_score", "expert_id", "domain", "verdict", "rationale", "annotations", "methodology_version", "audit_timestamp"],
  "properties": {
    "evaluation_id": {
      "type": "string",
      "description": "Unique identifier for the evaluation record"
    },
    "confidence_score": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Model confidence score for the evaluation"
    },
    "expert_id": {
      "type": "string",
      "description": "Identifier of the subject matter expert who reviewed the output"
    },
    "domain": {
      "type": "string",
      "description": "Subject domain of the evaluated content, e.g. speech_pathology"
    },
    "domain_specialty": {
      "type": "string",
      "description": "Specific specialty area within the domain the expert was matched on"
    },
    "verdict": {
      "type": "string",
      "enum": ["TRUE", "FALSE", "UNCERTAIN"],
      "description": "Final evaluation outcome"
    },
    "rationale": {
      "type": "string",
      "description": "Short explanation of why the verdict was reached, tied to the annotation scores"
    },
    "annotations": {
      "type": "object",
      "required": ["fluency", "accuracy", "naturalness"],
      "properties": {
        "fluency": { "type": "number", "minimum": 0, "maximum": 5, "description": "Confidence score for fluency assessment" },
        "accuracy": { "type": "number", "minimum": 0, "maximum": 5, "description": "Confidence score for accuracy assessment" },
        "naturalness": { "type": "number", "minimum": 0, "maximum": 5, "description": "Confidence score for naturalness assessment" }
      }
    },
    "methodology_version": {
      "type": "string",
      "description": "Version identifier of the evaluation rubric or model applied"
    },
    "audit_timestamp": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp of when the evaluation was recorded"
    }
  }
}
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Accessibility Compliance Output",
  "type": "object",
  "required": ["manifest_id", "compliance_status", "overall_score", "evaluator_panel", "findings", "flagged_reason", "remediation_required", "methodology_version"],
  "properties": {
    "manifest_id": {
      "type": "string",
      "description": "Unique identifier for the compliance audit manifest"
    },
    "compliance_status": {
      "type": "string",
      "enum": ["PASS", "FAIL", "CONDITIONAL"],
      "description": "Overall compliance outcome"
    },
    "overall_score": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Aggregate compliance score across all evaluated criteria"
    },
    "evaluator_panel": {
      "type": "array",
      "description": "Experts who reviewed the content",
      "items": {
        "type": "object",
        "required": ["expert_id", "domain_specialty"],
        "properties": {
          "expert_id": { "type": "string", "description": "Identifier of the reviewing expert" },
          "domain_specialty": { "type": "string", "description": "Specialty area the expert was matched on, e.g. accessibility_compliance" }
        }
      }
    },
    "findings": {
      "type": "object",
      "required": ["alt_text_coverage", "reading_order", "color_contrast"],
      "properties": {
        "alt_text_coverage": { "type": "number", "minimum": 0, "maximum": 1, "description": "Proportion of content with compliant alt text" },
        "reading_order": { "type": "number", "minimum": 0, "maximum": 1, "description": "Score for logical reading order compliance" },
        "color_contrast": { "type": "number", "minimum": 0, "maximum": 1, "description": "Score for color contrast compliance" }
      }
    },
    "flagged_reason": {
      "type": "string",
      "description": "Short explanation of the primary factor driving a CONDITIONAL or FAIL status, blank if PASS"
    },
    "remediation_required": {
      "type": "boolean",
      "description": "Whether further remediation action is needed before publication"
    },
    "methodology_version": {
      "type": "string",
      "description": "Version identifier of the compliance rubric or model applied"
    }
  }
}