What We Build
AI that meets the public.
Quallaa is an AI agency. We build custom AI-native systems for teams who need them and can't build them alone — with a center of gravity in the building industry. Some of those systems face inward, to your team. Some face the public: the AI represents your business to people who never log in — strangers reaching you over calls, texts, email, web chat, booking pages, and websites. That second kind has structurally different requirements, and it's the hardest thing we do well.
Where this kind of AI lives
Anywhere a stranger reaches your business:
- Inbound voice calls — answering, screening, booking, escalating
- SMS / text — instant response, full conversation handling
- Email — triage and reply on behalf of the business
- Web chat — embedded on the business's own website
- Booking pages — conversational + self-serve scheduling
- AI-native marketing surfaces — the business's website, content, follow-up cadence
The common factor: the AI is talking to people who haven't authenticated, haven't agreed to terms, and have no shared context with the business.
Why facing the public is structurally different from internal AI
Internal-team AI talks to your team. Strangers don't.
- Strangers don't sign agreements. No EULA, no NDA, no terms of service in place before the conversation. Whatever the AI says is on the record from word one.
- Strangers don't share your context. They don't know your tone, your hours, your services, your boundaries. The AI has to introduce them to your business — fairly and accurately — every time.
- Failures land publicly. When an internal-team AI hallucinates a number, the team catches it. When AI facing the public hallucinates a price, screenshots end up on social media within minutes.
- The legal surface is different. Disclosure requirements (Colorado AI Act, June 2026), FTC guidance on transparency, and emerging state-level rules around consent — all kick in the moment AI starts talking to strangers.
This is why most AI that works for internal use fails when pointed outward. Internal AI optimizes for productivity; AI that meets the public has to optimize for trust. That gap is most of why teams hire us instead of building it themselves.
AI that meets the public ≠ “Public AI”
One quick distinction. The phrase “Public AI” already refers to two existing movements, and neither is what we mean:
- The Public AI Inference Utility — Mozilla-funded, Metagov-sponsored. Public AI as inference utility — democratic, low-cost access to state-of-the-art models (Apertus from Switzerland, SEA-LION v4 from Singapore).
- PublicAI ($PUBLIC) — a Web3 token, decentralized data labeling network with on-chain stake-slashing and BFT consensus. Public AI as human data layer — economic coordination for the inputs that train models.
What we mean is neither. We mean the decision layer that sits between a business that wants AI to represent it in public and the unauthenticated strangers that AI will speak with — built to be understood, with the owner in control.
We say “AI that meets the public” deliberately. “Public AI” alone is ambiguous across the two existing movements; the longer phrase is precise about what the AI actually does and avoids the collision.
What the trust layer requires
Trust isn't a marketing claim; it's a structural requirement. For AI that meets the public to be deployable without becoming a liability, four things have to be true on the way in:
- Risk-scoring across the relevant dimensions. Different deployments have different risk surfaces. A locksmith answering missed calls and a Super Bowl ad-running AI agent occupy opposite ends of the same continuum. The intensity of safety engagement has to scale with the actual risk profile, not with a fixed curriculum. Quallaa uses an 8-dimension framework (autonomy, action capability, consequence severity, reversibility, audience exposure, domain sensitivity, identity representation, data sensitivity).
- Citations on every claim the AI makes. When the AI quotes a price, references a policy, or describes a service, the source must be traceable. No claim should be downstream of nothing.
- Audit trails captured by default. Every conversation, every disclosure, every guardrail trip — logged. Reasonable care isn't a posture; it's a verifiable record.
- Proportional safety — hard stops that match the deployment. Some risk combinations require human-in-the-loop. Some require explicit owner authorization. Some require categorical no's. The framework names them, the runtime enforces them.
Together these are the floor for putting AI in front of strangers — the price of entry, not the differentiator. Most AI skips them. That is how AI in front of the public got the reputation it has. We build it the other way.
Who builds AI that meets the public
Right now: not many people. Most of the AI ecosystem is still pointed inward — coding assistants, internal copilots, productivity tools. AI that meets the public is the next frontier and structurally harder, which is exactly why teams who need it can't build it alone.
Quallaa is an AI agency, a member of Anthropic's Claude Partner Network. We build custom AI-native systems from the ground up — a website with AI in it, email that thinks, voice and SMS lines, a CRM with judgment, content that ships — with a center of gravity in the building industry, and public health and polling alongside. AI that meets the public on your behalf is one of those systems. Built in partnership, built to be understood, so you always know what your AI is doing. Engagements are scoped per project; the first conversation is free.