Technology
Agentic AI with auditable controls
Our product architecture combines orchestration, LLM inference, vector search, data pipelines, and guardrails so AI outputs can be measured, reviewed, and improved.
agent-runtime.yaml
input:Product data, campaign data, CV text, commerce events
agent layer:Planning, scoring, creative generation, guardrails
control:Confidence thresholds, factuality checks, human escalation
record:Decision logs, audit trail, measurable outcomes
Multi-agent runtime
Specialized agents plan, score, generate, validate, and escalate actions depending on product context.
Data and decision layer
Product systems maintain structured events, source records, vector context, and decision logs.
AI governance
Factuality guards, confidence scoring, explainable decisions, and human review paths reduce unsupported automation.
Cloud stack under evaluation and use
Azure OpenAI
Azure AI Foundry
Cosmos DB
Container Apps
Vertex AI
Gemini
BigQuery
GKE
Amazon Bedrock
Claude
SageMaker
ECS/Fargate
NVIDIA GPU compute