AI that Generates Pipelines,
Not Guesses.

We are crystal clear about what AI does here — and what it doesn't. AI builds the deterministic pipe. The pipe runs your data. These are two completely separate steps.

AI Generation Phase

LLMs process your prompts and schemas to write deterministic validation logic.

Prompt: Ensure the email domain matches the corporate domain, otherwise flag as error.

Deterministic Execution Phase

Your data runs through compiled, inspectable code. No AI at runtime. No hallucinations.

if (email.split('@')[1] !== domain) {
throw new ValidationError();
}

Natural Language to Logic

This is inspectable, auditable, version-controllable logic — not a black box. You see exactly what validations are being applied to your data.

Input Prompt"Validate that the SKU exists in our database via REST call"

Step 3: REST Validation
→ GET /api/sku/{value}
→ if 404, flag row as invalid

Pipe Inspector

1. Type Conversion

2. Null Check

3. Custom REST Validation

GET /api/sku/{val}

The Consensus Engine for PDFs

For unstructured documents, we don't rely on a single OCR engine. We use multiple, and force them to agree.

Engine A Result

Engine B Result

Math Checksum

All agree → HIGH Confidence

Why This Matters for CTOs

Auditable

Every validation step is stored as readable configuration. Product managers and engineers can read it.

Version-Controlled

Pipelines are managed like code. Roll back changes instantly if something breaks.

No Hallucinations at Runtime

The AI writes the code. The code processes the data. Zero unpredictability in production.

AI that Generates Pipelines, Not Guesses.