Your records should decide.
Not your chatbot.
Prepare your information before questions arrive. For supported questions, software selects from your records. The language model explains the result.
Evidence before eloquence
Know what supports an answer.
Know when the evidence runs out.
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.
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.
Introduced at AI Infra Summit, September 2026. See the event presentation.
The right documents are only the beginning.
RAG gives a chatbot documents to consult. That helps it find relevant information, but the model still writes the answer. Verificate separates preparing knowledge, selecting a supported result and explaining it.
Typical generative RAG
Find passages. Ask the model.
Useful for search and exploration. Correct retrieval does not, by itself, guarantee a supported answer. Verification and refusal need their own controls.
Documents → model-generated answer
Compile-Time Inference
Prepare knowledge. Explain the result.
For supported tasks, selection happens over prepared records and rules. The language model helps interpret the question and explain the result, with evidence to inspect.
Prepared records → selected result → explanation
RAG systems can also add verification and deterministic rules. The right comparison is the complete workflow, not its label.
Prepare once. Keep current. Answer with evidence.
Move the preparation ahead of the conversation. Maintain it as your information changes.
- 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.
Repeatability applies to supported decisions with the same interpreted inputs, records and rules. New evidence can change the result; wording may vary.
Choose the problem you need to solve.
Compile-Time Inference, Gate and Helix are independently available. Start with your knowledge, your AI-written work or your infrastructure.
Your knowledge & decisions
Compile-Time Inference
Prepare your records before questions arrive. Select supported answers and decisions, with evidence you can inspect.
Explore Compile-Time InferenceYour AI-written work
Verificate Gate
Check AI-written code and plans before accepting them. Get a verdict, findings and a path to address the risks.
Explore GateYour models & infrastructure
Verificate Helix
Run supported AI models on infrastructure you control. Use confidence signals to deliver an answer or ask for review.
Explore HelixDifferent records. The same need for evidence.
Video & broadcast
Ask about a broadcast and inspect the supporting clip. Explore the video demonstration with Vividas.
Demonstration · public footage
Legal research
Ask Kevin about Australian law and check the cited cases behind the answer. Discuss the coverage your work requires.
Research preview · not legal advice
Genomics research
Discuss an evidence-focused evaluation using biomedical records and your research questions.
Private demonstration · by arrangement
Inspect the claim. Understand its limits.
A citation that exists, an answer that is correct and a decision that repeats are different measurements. We keep those questions separate.
Understand the evidence
See which measurements support which claims—and what they do not establish.
Read moreInspect the research
Published studies on decision policies and inference. Results are specific to their workloads.
Read moreEvaluate your use case
Test correctness, coverage, refusal, repeatability and total cost on your own questions.
Read moreMake the next architecture decision an informed one.
Why RAG still hallucinates
Separate retrieval problems from unsupported generation before changing your stack.
Read the guideRAG vs Compile-Time Inference
Compare preparation, query-time work, evidence and the cost of keeping information current.
Read the guideGraphRAG vs Compile-Time Inference
A graph connects information. What decides the answer is a separate design choice.
Read the guideStart with what matters to your team.
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.
Bring a question your AI needs to get right.
We’ll discuss your records, the decisions you need to support and how to evaluate the result. No confidential documents needed for the first conversation.