Veritiana builds control infrastructure around AI systems — execution, policy, validation, observability and measurable outcomes.
Different systems solve different control problems. The shared principle is simple: models operate inside explicit boundaries that can be inspected and enforced.
Control how AI work is executed, repaired, validated and carried forward across a persistent project runtime.
Define what AI conversations are allowed. Guard prompts and responses against a policy you control.
Policy boundaries, execution control, validation and measurement are separate concerns — but they belong to the same controlled AI stack.
Apply user-owned policy to inputs and outputs.
Understand intent, context and work type.
Select the right path, model and tools.
Turn intent into explicit executable steps.
Run models and tools inside bounded execution.
Validate outputs against contracts and rules.
Capture traces, retries and execution signals.
Quantify cost, quality, impact and efficiency.
Tenrec controls how AI work is executed. Family Guard controls what AI communication is permitted. Both keep policy outside the model and make enforcement inspectable.
A persistent AI software runtime built around short-lived specialized workers, selective project memory and deterministic validation.
A model-agnostic policy control layer that converts natural-language intent into enforceable rules, evaluates AI inputs and outputs, and maps risk to explicit actions.
Veritiana develops practical AI products, data infrastructure, agent systems and experimental interfaces.
Explore LoRA, QLoRA, SFT, DPO, PPO and other fine-tuning and post-training methods by practical objective.
A controlled answer layer that turns verified website and company knowledge into precise, source-grounded responses.
A structured product-data layer that makes catalogs readable, usable and verifiable by AI systems.
Technical AI-readiness, structured product data and implementation services for e-commerce agencies and their clients.
A transformation layer that converts raw operational notes, events and observations into structured, usable outputs.
A browser-native mini-transformer experiment focused on lightweight learning, inference and local model execution.
Research into persistent digital entities, long-term memory, autonomous behaviour and governed decision processes.
A physical interface concept connecting real-world perception, contextual memory and controlled AI workflows.
Start with a concrete control problem and an outcome that can be tested.