RL Environment
Ad Creative Generation, an environment where an AI agent turns a shopper's profile and query into a personalized ad. The agent uses real tools to retrieve product information, write and compose the creative, and receives a verifiable hybrid reward based on how well the ad matches the product, the customer's query, and their profile. Supports both fashion and electronics domains.

Screenshots

Industry
Environment specs
Persona / role
Problem
Brands need ad copy that matches both what a shopper asks for and who they are—their tastes, intent, and history—without exposing private tracking data. Manual workflows do not scale, while generic AI copy tools hallucinate product details, ignore meaningful customer context (or leak private signals into the visible ad), and provide no objective way to measure whether an ad actually satisfies the user's request. Public datasets are also insufficient: none include ad-creative ground truth, and most lack customer queries altogether.
Solution
We build a single-turn environment where a model completes the full advertising task. It receives a customer query and a context profile derived only from privacy-safe signals, infers user intent, generates a structured headline, body, and call-to-action, and is evaluated using a verifiable hybrid reward. Fast deterministic checks validate structure, approved CTAs, length, character set, forbidden claims, and contradictions, while an LLM/VLM judge scores product grounding, query alignment, personalization, and overall copy quality. A multi-step Ad Studio layer enables the model to operate as an autonomous agent, selecting one validated tool per turn and recovering safely from errors, with every action and judgment labeled by execution mode. A shared, domain-agnostic core supports multiple verticals through a common set of validation rules, rewards, and execution contracts.
Impact
Ad quality becomes measurable, reproducible, and auditable rather than subjective. Personalization is demonstrated without revealing customer tracking or browsing history, while unsupported claims are detected before human review. The same shared framework powers fashion and electronics use cases today and can be extended to new domains without redesigning the underlying system. Transparent provenance, together with both cached and live execution modes, ensures every generation is traceable, trustworthy, and reproducible.
Security
Disciplined security and privacy practices aligned with global standards to protect sensitive data, intellectual property, and model assets throughout the AI lifecycle.
Centific applies rigorous security, access control, and auditability standards to safeguard enterprise data, human workflows, and AI systems at scale.
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