What is an AI agent? How does Agent Studio work? What could it do for Red Bull, Campari, and Citibank? A working demonstration — the agents are live, the intelligence is assembled right now.
Every demo on this page is live. Not retrieved from a pre-written script. Not a recorded response. Assembled from real signals, right now.
Or read it here →A chatbot responds to what you type and forgets everything when you close the tab. An agent has a job, a memory, and access to real tools. It reasons. It acts. It gets smarter over time.
01 — Goal
Every agent has a specific purpose — a defined mission calibrated to a brand, a client, a business challenge.
02 — Memory
Agents remember across conversations and time. They build a persistent model of their domain and sharpen it with every interaction.
03 — Tools
Agents act. They search, query, read, write, and signal. They produce verified intelligence from real inputs.
The pipe that connects software to software. Every tool an agent uses — search, databases, brand data — is accessed via API. This is why Agent Studio is API-first.
Receive → Plan → Act → Synthesize. Not a generation step — a decision loop. The agent reasons about what it needs, uses tools to get it, builds a verified response.
Scout reads signals. Strategist interprets. Validator challenges. Writer produces. Teams of specialized agents checking each other produce intelligence no single agent could match.
Every response is a decision loop — not a generation step. The agent reasons about what it needs, acts to get it, and synthesizes a verified response.
Each agent was purpose-built for a specific brand intelligence use case. Ask a question from the chips or type your own. Intelligence assembled in real time.
01 — Red Bull
Cultural intelligence for youth, sports, music, and the creative underground.
02 — Campari Group
Audience intelligence for premium spirits consumers, cocktail culture, and bartender influence.
03 — Citibank
Consumer financial behavior, trust signals, and competitive intelligence for banking brands.
Not a chat interface. Not a vendor tool. Production-grade infrastructure for building, testing, and deploying AI agents that work for specific brands, in specific contexts, toward specific goals.
Every agent is API-first. Callable from any system, any platform, any workflow. The intelligence lives in the infrastructure — not in a chat window you close when the meeting ends.
Build
Define the agent's identity: purpose, personality, constraints. The system prompt is the agent's constitution.
Equip
Connect tools: web access, brand data, market signals, CRM feeds. Intelligence is only as good as what it can reach.
Deploy
Every agent ships as a callable API. Embed it in any system — briefing docs, event dashboards, client platforms.
Evaluate
Adversarial agents challenge outputs continuously. The system finds its own errors before they reach you.
Hallucination happens when a language model generates confident-sounding output under uncertainty. We treat this as a data architecture problem, not a model limitation.
SuperTruth — our patented data integrity layer — scores every signal for accuracy, provenance, consent, freshness, and consistency before it is ingested. The agent never works from unverified inputs.
6 years of data integrity R&D
This architecture has been operating in healthcare, hospital systems, and federal government before it ever touched marketing.
200+ pages of patent filings
AI orchestration, data integrity scoring, adaptive learning. Foundational IP. Filed because the architecture is novel.
Adversarial agent swarms
Agents whose entire job is to challenge what other agents produce. The system catches its own errors before they reach a human.
Zero black boxes
Clients see the signals, the tools, the reasoning — not just the output. Full transparency is a design requirement.
