Reliable AI operators
for real systems.
Versioned AI functions with clear contracts, confidence signals, and predictable behavior — ready to embed in production.
Why this exists
AI is now part of many everyday systems — processing documents, validating data, and supporting decisions.
Most teams begin by calling AI models directly. That approach works early on, but problems appear once AI becomes part of a real workflow.
Teams commonly face:
outputs that change shape and break downstream logic
failures that are difficult to notice or explain
no clear signal for whether a result can be trusted
unexpected changes after model or prompt updates
To manage this, teams either avoid using AI in important paths or spend time building custom guardrails around models.
Operativez exists to reduce this complexity — so AI behavior is easier to understand, control, and depend on.
Reliable AI operators for real systems
An operator is a focused AI worker that performs one well-defined task. It behaves like a dependable software component — not an experiment and not a prompt.
Fixed Structure
inputs and outputs follow a fixed structure
- Structured schemas
- Type-safe interfaces
- Predictable formats
Confidence Signals
every result includes confidence and review signals
- Confidence scores
- Review flags
- Quality metrics
Version Control
behavior is consistent and version-controlled
- Semantic versioning
- Change tracking
- Rollback support
Async Execution
execution is asynchronous, with clear success and failure states
- Job-based processing
- Status tracking
- Error handling
Operators run on shared reliability infrastructure that handles retries, observability, and regression checks.
The goal is simple: AI functions that behave consistently inside real systems.
Who this is for
Operativez is built for teams that want to use AI, but cannot afford surprises.
This typically includes:
If a faulty AI output can block a workflow, corrupt data, or create manual rework — this is designed for you.
How it integrates
Operativez is API-first and designed to fit into existing architectures.
Execution Model
Execution is asynchronous by default. Each request returns a job ID, and results are delivered via webhooks with full traceability.
Integration Benefits
- Asynchronous execution
- Job ID tracking
- Webhook delivery
- Full traceability
Example Response
{
"job_id": "uuid",
"operator": "invoice.extractor",
"status": "completed",
"output": {},
"confidence": 0.92,
"needs_review": false,
"trace_id": "..."
}You keep your system design.
We make the AI part reliable.
If workflow tools coordinate steps, Operativez makes AI steps safe to run.
We sit between probabilistic models and deterministic systems.
Our role is not to decide what should happen next — but to ensure that when AI is used, it behaves in a way systems can depend on.
Upcoming operators
Operativez is being built around a small set of focused operators. Each operator has a single, clearly defined responsibility.
invoice.extractor
Extracts structured, validated data from invoices and flags low-confidence fields for review.
receipt.extractor
Reads receipts across formats and vendors, returning normalized data with confidence signals.
vendor.normalizer
Identifies and standardizes vendor information across documents and systems.
document.router
Classifies documents and routes them based on content and confidence thresholds.
output.validator
Checks AI outputs against expected schemas and business rules before they are used downstream.
These operators are designed to behave like dependable workers inside a system — each responsible for one task, with clear expectations.
Ready to make AI reliable?
Join teams building production AI systems with operators that behave consistently — every time.