Verificate Compile-Time Inference

Answers you can check.
Decisions you can repeat.

Prepare your records and supported decision rules before questions arrive. Select a result from that prepared knowledge, then let the language model explain it—with evidence you can inspect.

See it work Discuss your use case

Assisted evaluation · supported tasks and coverage agreed for your application

Beyond finding the document

The chatbot should explain the decision, not own it.

RAG—retrieval-augmented generation—gives a chatbot relevant material to consult. That can improve its answer, but a generated response can still miss, misread or add to the evidence. Verificate separates preparing knowledge, selecting a supported result and explaining it.

A familiar interface

Ask in everyday language. A model helps interpret the question and explain the selected result. The explanation is not a substitute for checking its supporting record.

A separate decision path

For supported decision workflows, selection is governed by prepared information and rules, rather than asking the chatbot to improvise a choice.

Strong RAG systems can also use verification and fixed rules. Evaluate the whole workflow against your requirements, not just the architecture’s name.

Read the RAG comparison

Preparation before conversation

Organise the knowledge. Keep it current. Use it.

“Compile-time” means preparation happens before questions arrive. It does not mean every possible answer is precomputed, or that no work happens when you ask.

01
Prepare your knowledge
Organise your records, their sources and relationships. Prepare supported decisions from evidence and past outcomes where the application supports it.
02
Keep it current
Track changes to records and rules. Agree how new information is checked and made available, rather than assuming yesterday’s answer still applies.
03
Answer with evidence
For supported requests, select a result from prepared knowledge. Use the language model to explain it, with checks and a path to decline when support is missing.

Know what you are evaluating

Two needs. Two things to measure.

Source-backed answers

Can I inspect the supporting evidence?

Check that cited sources exist and support the actual claim. Test what happens when records are missing or contradictory. The video and genomics examples focus on evidence, not an outcome-trained decision policy.

Supported decisions

Does the same situation produce the same choice?

Repeatability means the same interpreted inputs, authorised records and rules produce the same selected decision. Updated evidence may change the choice. Wording may vary; repeatability does not establish correctness or fairness.

Technical overview & evaluation Research and claim boundaries

Video example · Hybrid-VLM

Find the moment.
Check the answer.

Explore the News24 broadcast demonstration with Vividas. Ask about the footage, then follow the answer back to its supporting clip.

Open video demo

External demonstration · use public, non-sensitive questions.

What to look for in the demo

01
Ask about the footage
Start with a question about what appeared in the broadcast.
02
Inspect the supporting moment
Open the linked clip and check the time and source.
03
Check what the evidence supports
Does the footage support the claim? A signed source alone does not prove an interpretation is correct.

About the video application

Evidence, with its scope

Measure more than whether an answer sounds right.

Inspect correctness, supporting evidence, coverage, refusal and repeatability separately. Published study results do not guarantee the same outcome on a new task or dataset.

Understand the evidence

See which measurements support which claims—and what they do not establish.

Inspect the research

Published studies on decision policies and inference. Results are specific to their workloads.

Evaluate your use case

Test correctness, coverage, refusal, repeatability and total cost on your own questions.

A knowledge asset you maintain

Your information should outlast a model change.

Keep records and explanations separate

Prepare source-linked knowledge independently of how a model phrases its response. Validate interpretation and narration again when you change models.

Make change visible

Agree how source versions, permissions and policy changes are reflected in answers. Freshness and update costs are part of the evaluation.

Assess the total cost

Compare preparation, updates, storage and serving—not just the price of one model response.

Plan your deployment boundary

Discuss where records and models run, who can access them and how information reaches any external service.

Independently available

Need to check code or run your own models?

Verificate Gate

Check AI-written work before accepting it. Get findings from deterministic checks and an engineering review.

Explore Gate →

Verificate Helix

Run supported models on infrastructure you control, with confidence signals your application can use.

Explore Helix →

Start with a bounded task

A good fit begins with a clear question.

Worth evaluating

Answers that must point to an inspectable record.
Repeated decisions under an agreed policy.
Changing information whose versions matter.
Workflows that should decline when support is absent.

Keep the simpler option when it fits

A search tool may be enough for exploration. Ordinary business rules may be best for a fixed policy. Creative writing does not need to become a record-selection task. Preparation has a cost; establish its value on your workload.

What is Compile-Time Inference?

It is Verificate’s approach to preparing records and supported decision policies before questions arrive. At question time, software selects a supported result and a language model helps interpret and explain it. Preparation does not mean every possible answer is calculated in advance.

Do I need to replace my whole RAG system?

Not necessarily. RAG gives a chatbot documents to consult and can remain useful for search and open-ended exploration. Start by evaluating the answers or decisions where evidence and repeatability matter most. Integration and scope are agreed during an assisted evaluation.

Will I always get the same answer?

Repeatability applies to supported decisions with the same interpreted inputs, authorised records and rules. Updated information can correctly change the result. The wording of an explanation may vary. Repeatability alone does not prove correctness.

What happens when the information is missing?

The supported workflow is designed to decline when evidence is insufficient, rather than fill the gap with an unsupported answer. Coverage, refusal behaviour and output checks should be tested on your questions before deployment.

Can I buy Gate or Helix independently?

Yes. Verificate Gate checks AI-written work. Verificate Helix runs supported models on your infrastructure with confidence signals. Both are sold independently of Compile-Time Inference.

Independently available