AIVA Labs
AIVA Labs
A self-healing reliability layer to reduce hallucinations and model drift in LLMs.
AIVA
A self-healing reliability layer to reduce hallucinations and model drift in LLMs.
AIVA
A self-healing reliability layer to reduce hallucinations and model drift in LLMs.
AIVA
What AIVA Labs do
We build an AI reliability layer that keeps your LLMs accurate, safe and continuously improving without rewriting your stack.
Real-time Halluciantion Detection
We continuously evaluate every AI response, flagging hallucinations and risky outputs in real time before they reach your users.
Real-time Halluciantion Detection
We continuously evaluate every AI response, flagging hallucinations and risky outputs in real time before they reach your users.
Real-time Halluciantion Detection
We continuously evaluate every AI response, flagging hallucinations and risky outputs in real time before they reach your users.
Autonomous Correction Engine
When issues are detected, our self-healing layer automatically re-queries, cross-checks, or routes to safer models to return a corrected, trustworthy answer.
Autonomous Correction Engine
When issues are detected, our self-healing layer automatically re-queries, cross-checks, or routes to safer models to return a corrected, trustworthy answer.
Autonomous Correction Engine
When issues are detected, our self-healing layer automatically re-queries, cross-checks, or routes to safer models to return a corrected, trustworthy answer.
Model Drift Reduction
We track performance and data drift across models and use cases, so you see when quality degrades and can react before it hurts customers.
Model Drift Reduction
We track performance and data drift across models and use cases, so you see when quality degrades and can react before it hurts customers.
Model Drift Reduction
We track performance and data drift across models and use cases, so you see when quality degrades and can react before it hurts customers.
Policy, Safety, and Compliance Guardrails
We enforce your business rules, safety policies, and compliance constraints at the response layer, reducing legal and brand risk from AI failures.
Policy, Safety, and Compliance Guardrails
We enforce your business rules, safety policies, and compliance constraints at the response layer, reducing legal and brand risk from AI failures.
Policy, Safety, and Compliance Guardrails
We enforce your business rules, safety policies, and compliance constraints at the response layer, reducing legal and brand risk from AI failures.
Continuous Loop Learn
Feedback, incidents, and edge cases are turned into training signals, so your AI stack gets more reliable every week instead of decaying over time.
Continuous Loop Learn
Feedback, incidents, and edge cases are turned into training signals, so your AI stack gets more reliable every week instead of decaying over time.
Continuous Loop Learn
Feedback, incidents, and edge cases are turned into training signals, so your AI stack gets more reliable every week instead of decaying over time.
The process
01
Intercept
Our reliability layer sits between your users and the LLM. Every response passes through 5 verification stages before delivery—no hallucinations slip through.
01
Intercept
Our reliability layer sits between your users and the LLM. Every response passes through 5 verification stages before delivery—no hallucinations slip through.
01
Intercept
Our reliability layer sits between your users and the LLM. Every response passes through 5 verification stages before delivery—no hallucinations slip through.
02
Detect & Correct
Citation checking, semantic analysis, and LLM-as-Judge verification catch errors in real-time. Wrong facts are automatically corrected with source-backed answers.
02
Detect & Correct
Citation checking, semantic analysis, and LLM-as-Judge verification catch errors in real-time. Wrong facts are automatically corrected with source-backed answers.
02
Detect & Correct
Citation checking, semantic analysis, and LLM-as-Judge verification catch errors in real-time. Wrong facts are automatically corrected with source-backed answers.
03
Monitor & Evolve
Real-time drift detection tracks accuracy over time. The system continuously learns, maintaining 90%+ reliability as your data and needs change.
03
Monitor & Evolve
Real-time drift detection tracks accuracy over time. The system continuously learns, maintaining 90%+ reliability as your data and needs change.
03
Monitor & Evolve
Real-time drift detection tracks accuracy over time. The system continuously learns, maintaining 90%+ reliability as your data and needs change.
Our statistics
Hallucination Reduction
87%
87%
87%
Our 5-phase verification pipeline catches and corrects LLM hallucinations through citation checking, semantic entropy analysis, and automated retry loops with targeted feedback.
Response Accuracy
94%
Strict context grounding with COSTAR format transformation and Pydantic schema enforcement ensures every response is factually anchored to verified sources.
Response Accuracy
94%
Strict context grounding with COSTAR format transformation and Pydantic schema enforcement ensures every response is factually anchored to verified sources.
Response Accuracy
94%
Strict context grounding with COSTAR format transformation and Pydantic schema enforcement ensures every response is factually anchored to verified sources.
Drift Reduction
76%
Real-time embedding monitoring and faithfulness scoring detect model degradation early, preventing accuracy decay before it impacts production systems.
Drift Reduction
76%
Real-time embedding monitoring and faithfulness scoring detect model degradation early, preventing accuracy decay before it impacts production systems.
Drift Reduction
76%
Real-time embedding monitoring and faithfulness scoring detect model degradation early, preventing accuracy decay before it impacts production systems.
Cost saved per month
68%
Automated hallucination correction eliminates manual review cycles. Fast model refinement handles preprocessing, reserving expensive models only for generation.
Cost saved per month
68%
Automated hallucination correction eliminates manual review cycles. Fast model refinement handles preprocessing, reserving expensive models only for generation.
Cost saved per month
68%
Automated hallucination correction eliminates manual review cycles. Fast model refinement handles preprocessing, reserving expensive models only for generation.
Subscriptions
Contact for Pricing
For teams that want their AI to stay accurate, auditable, and production‑ready at scale.
Contact for Pricing
For teams that want their AI to stay accurate, auditable, and production‑ready at scale.
Contact for Pricing
For teams that want their AI to stay accurate, auditable, and production‑ready at scale.
Contact for Pricing
For teams that want their AI to stay accurate, auditable, and production‑ready at scale.