IOanyT Innovations

Glossary

What is deterministic AI?

"Deterministic AI" describes AI-built systems whose production behaviour is deterministic: the same input always produces the same output, because the decision path runs as fixed, reviewed logic rather than a fresh model generation on every request.

It's how we build: compiled AI. Models do the engineering at build time; deterministic code does the executing.

Why "temperature 0" isn't deterministic

Setting a model's temperature to zero makes it pick its most likely next word, but it doesn't guarantee the same answer twice in production. Outputs can still drift because of:

  • Batching and parallel arithmetic. Floating-point operations run in different orders on shared hardware, and tiny differences can change which word wins.
  • Model updates. Hosted models are revised by their providers, sometimes behind the same name.
  • Context drift. A slightly different prompt, retrieved document or conversation history is a different input.

That's fine for drafting. It isn't fine for a credit decision, a claim outcome or a price quote. There the dependable fix is to make the decision path itself deterministic.

Deterministic vs generative vs agentic

Generative

Good at: drafting, summarising, conversation.

Can go wrong: different answers to the same question; confident errors.

Belongs: where a person reviews the output.

Agentic

Good at: multi-step tasks across tools.

Can go wrong: errors compound, and actions are taken on them.

Belongs: reversible, low-value tasks with hard permission limits.

Deterministic

Good at: decisions that must be repeatable and explainable.

Can go wrong: misses cases nobody anticipated, so changes ship as new, tested versions.

Belongs: anywhere a customer, auditor or regulator relies on the answer.

Where deterministic execution matters

  • Lending and collectionsAffordability, credit and dunning rules applied the same way to every customer.
  • InsuranceClaims triage and underwriting rules your team can read and defend.
  • Policy, price and eligibilityAnswers about what a customer is entitled to come from logic you approved.
  • Anything replayableDecisions an auditor may ask you to reproduce months later.

The Graduated Exposure Ladder

Deterministic doesn't mean all-or-nothing. Our framework decides, step by step, how much model belongs at run time. Every step starts at Level 0 and moves up only after it has proven itself against measured criteria on your data.

How much model, at run time? Every step starts at the bottom and moves up only after it has proven itself on your data. LEVEL 4: Promotion, proven patterns compiled into code. LEVEL 3: Constrained conversation, code controls every commitment. LEVEL 2: Bounded extraction, strict schema, human fallback. LEVEL 1: Internal assist, staff review before any action. LEVEL 0: Build-time only, zero runtime model calls. IOANYT FRAMEWORK / GRADUATED EXPOSURE LADDER How much model, at run time? Every step starts at the bottom and moves up only after it has proven itself on your data. LEVEL 4 Promotion proven patterns compiled into code LEVEL 3 Constrained conversation code controls every commitment LEVEL 2 Bounded extraction strict schema, human fallback LEVEL 1 Internal assist staff review before any action LEVEL 0 Build-time only zero runtime model calls stable patterns are compiled back down into code over time

Deterministic logic and the EU AI Act

Under the EU's 2026 Digital Omnibus agreement, the AI Act's timetable is:

  • 2 August 2026: Article 50 transparency obligations (telling people they are dealing with AI).
  • 2 December 2027: high-risk obligations for Annex III systems, including credit scoring, employment, education and insurance pricing.
  • 2 August 2028: high-risk obligations for Annex I regulated products.

High-risk systems need documentation, logging, human oversight and monitoring. Versioned, testable logic makes each of those easier to provide. Conformity remains the obligation of the system's provider or deployer; we design to support that work.

This is a summary for orientation, not legal advice.

Common questions

Is deterministic AI still AI?

Yes. The intelligence goes into building the system: models read the policies, draft the logic and generate the tests. What changes is run time, where the decision path executes as reviewed code instead of a new model output each time.

Can an LLM ever be fully deterministic?

Not reliably in production. Even at temperature 0, outputs can shift with batching, hardware, provider-side model updates and small changes in context. If a decision must be repeatable, it is safer to make the decision path itself deterministic.

Does deterministic mean no LLM at all?

No. A model can still read an unstructured document or handle conversation, as long as it works inside a strict schema, its output is validated by code, and a person takes over when it is unsure. What it never does is make the commitment on its own.

Which industries need deterministic execution most?

Anywhere a decision has to be explained or replayed: lending, credit and collections; insurance claims and underwriting; eligibility, pricing and policy answers; and regulated operations generally.

How do we test whether our current AI is consistent?

Run the same inputs many times and compare the outputs, then measure accuracy against examples your team has already labelled. A Proof Sprint does exactly this on one of your workflows and hands you the report.

Is your AI giving the same answer twice?

A Proof Sprint measures consistency, accuracy and cost per case on one of your workflows, then compiles it into deterministic logic. Fixed scope. You keep the code.

Scope a Proof Sprint

No commitment required