Think better

We start with a short intro call to understand your goals, then a discovery session with your team to map where AI agents can create the most value. From there we propose a scoped pilot — a working system in your environment, not a slide deck.

No. We meet you where you are. Whether you're exploring AI for the first time or scaling existing initiatives, we calibrate the engagement to your data, processes, and team — and build maturity together along the way.

Yes. Our platform orchestrates around your existing data, tools, and workflows rather than replacing them. We connect AI agents to the systems your teams already use and put them to work inside your real processes — with humans in the loop.

Security and governance are designed in from day one: encryption in transit and at rest, single sign-on, role-based access controls, and a full audit trail of every agent run. Deployment options range from our managed cloud to environments you control — and we're glad to do a deep dive with your security team.

We orchestrate the frontier rather than betting on a single vendor — routing each task to the right model, from leading frontier models to fine-tuned and locally hosted ones. As models improve, your platform improves with them.

Engagements are scoped to your goals — from SaaS subscriptions to owning the platform outright. Value is measured, not promised: we baseline first, meter every run, and let the results in your own telemetry make the case.

Yes. AI agents can connect to SQL databases, document repositories, contracts, HR records, email, and custom APIs through open integration standards — with access controls deciding exactly what each agent may touch. The AI works inside your real systems rather than a copy of them.

All of the leading ones. The platform treats Anthropic Claude, OpenAI GPT, and Google Gemini as first-class providers, alongside open-weight models running on your own hardware. Each task is routed to the model that fits it best, and providers can be mixed within a single system — so you are never locked into one vendor.

Yes. Agents can run on schedules, within business-hours windows, or autonomously on long-running tasks. Every run operates under hard time and spend budgets enforced by the platform, and every action is logged — so unattended never means uncontrolled.

Yes. Agents can write and execute code in isolated, sandboxed environments to analyze data, build charts, train models, and produce reports — with a full data-science toolkit. Results are saved and auditable; the sandbox means agent code never runs loose in your systems.

Yes. The same AI agents that work in chat can operate on the public phone network — answering inbound calls, running outbound campaigns within configured calling hours, transferring to humans when needed, and producing faithful transcripts of every conversation.

Yes. Through generative UI, agents respond with live, interactive components — charts, KPI cards, maps, and controls — generated on demand from your data. Users can click an option and the agent continues from their selection, collapsing the gap between question and dashboard.

Yes. Long-term memory carries user preferences, goals, and history across conversations so the system gets more useful over time, while short-term telemetry records what happened in each run for auditing. Memory is curated automatically and remains inspectable.

Yes. Agents are reachable through standard APIs and emerging agent-to-agent interoperability protocols, so existing enterprise platforms and other AI systems can discover and invoke them — your agents become infrastructure, not an island.

Your data is used to ground and personalize your own AI systems — and for nothing else. It is never pooled or shared across customers, and for the most sensitive workloads, local inference keeps content entirely on hardware you control.

By grounding, citing, and verifying. Retrieval-augmented generation makes agents answer from your actual documents with clickable citations; structured workflows and review steps check work before it ships; and human-in-the-loop checkpoints catch what automation shouldn't decide alone.

With hard budgets and full metering. Every run operates under enforced time and token caps — when a budget is reached, the agent halts cleanly rather than billing on. Complete telemetry shows what every conversation cost, by team and by task, so spend is forecastable instead of surprising.

Yes — by design. Consequential actions require human approval, complex work can be planned collaboratively and signed off before execution, and running agents can be steered mid-task without losing progress. The model plays; the human conducts.

Yes. Deployment ranges from a managed cloud service to environments you control, and open-weight models can run entirely on your own hardware through local inference — keeping sensitive content on-premise and eliminating per-token costs for routine work.

First working pilots typically arrive in weeks, not quarters. Engagements start with a short discovery to find where agents create the most value, then a scoped pilot running in your environment — a working system, not a slide deck — which then expands based on measured results.

Single-vendor assistants give everyone the same general-purpose helper. ClearData AI orchestrates the frontier instead of competing with it — building systems on top of the leading models that are grounded in your data, equipped with your tools, governed by your rules, and measured in your telemetry. It is the difference between hiring one generalist and operating a managed team of specialists.

In your telemetry, not our promises. Engagements baseline current performance first, then meter every agent run — time saved, work produced, cost incurred — so realized value is observed in your own data. The conservative number is the one we stand behind.

Enterprise superintelligence is an operating layer where a company's governed data, AI agents, and orchestration work as one system — delivering capability no single model, tool, or person could produce alone. It isn't one giant model; it's many specialized parts engineered to work in concert, built around your data and workflows.

Harness engineering is the discipline of building the scaffolding around AI models — context, tools, memory, guardrails, budgets, and verification — that turns raw model capability into reliable, accountable business systems. The model supplies the horsepower; the harness makes it pull in the right direction. It is ClearData AI's signature discipline.

No. Enterprise superintelligence describes systems that exceed what any individual could do across your specific workflows — by orchestrating models, data, and tools — not a claim about artificial general intelligence. The capability is measurable in your telemetry today, not speculative.

Assistant seats give every employee the same generalist helper. An operating layer puts specialized AI agents inside your actual workflows — grounded in your data, equipped with your tools, governed by your rules. The two aren't exclusive: assistants help individuals work faster, while superintelligence compounds across the whole organization.

No. Engagements begin with a short discovery and a scoped pilot that ships in weeks — a working system in your environment. Value is baselined and metered from day one, and the system expands only as the measured results justify it.

The ClearData AI Suite is the enterprise AI operating layer: a platform where AI agents, your governed data, your tools, and human oversight work as one system. It is available as a SaaS subscription or as platform ownership (PaaS) for organizations that want the asset on their own terms.

No. Anthropic Claude, OpenAI GPT, and Google Gemini are all first-class providers, alongside open-weight models on your own hardware. Models can be mixed within a single system and switched without re-engineering — the operating layer is the constant; the models are interchangeable.

Neither is the core model. Pricing is value-based — anchored to the outcomes the platform delivers, not consumption meters. You can bring your own model keys with no markup, and hard run budgets enforced by the platform keep costs predictable rather than open-ended.

Yes. The PaaS model means platform ownership: the system becomes your asset, with services, support, and continuous updates engaged as you need them. Teams that prefer a subscription start with SaaS and can move toward ownership as adoption grows.

In our managed cloud or in an environment you control — and for sensitive workloads, open-weight models can run entirely on hardware you own. Deployment is scoped during the pilot so security review and procurement happen once, properly.

A chatbot answers; an agent acts. An AI agent pursues a goal — it plans the work, chooses tools, queries systems, takes actions, checks its own results, and continues until the job is done or a human checkpoint is reached. The conversation is just its interface, not its limit.

There is no fixed number. Systems are composed of an orchestrator plus specialist sub-agents — a researcher, a data analyst, a reviewer — each with its own role, tools, and guardrails. Most clients start with one well-scoped workflow and grow the fleet as measured value justifies it.

Layered guardrails. Each agent only holds the tools its role requires; consequential actions need human approval; runs operate under hard time and spend caps; code executes in isolated sandboxes; and every action lands in an audit trail. Safety is enforced by the platform, not promised by a policy.

Yes — that's the point of orchestration. An orchestrator decomposes work and delegates to specialists, who can run in parallel and hand results back for assembly. Agents are also reachable through open interoperability standards, so external systems and agents can collaborate with yours.

They take the toil, not the judgment. Agents absorb the repetitive, high-volume work — drafting, querying, reconciling, monitoring — while people set direction, review what matters, and own the decisions. The model plays; the human conducts.

A data lakehouse combines the flexibility of a data lake — store anything, structured or not — with the governance and performance of a data warehouse. One architecture serves analytics, operations, and AI from the same governed foundation.

Because there is no AI without an IA — an information architecture. AI agents are only as reliable as the data that grounds them. A lakehouse gives them trusted, current, well-governed data to reason from, which is the difference between cited answers and confident guesses.

A progressive refinement pattern: Bronze holds raw data exactly as it arrived, Silver holds cleaned and conformed data, and Gold holds business-ready data products. Each layer adds trust, so both people and AI agents always know what quality of data they're standing on.

No. We meet your data where it is — pilots start with the sources one workflow actually needs, and the governed foundation grows with use. Big-bang migrations aren't a prerequisite for value; they're usually what kills it.

Where you choose. Deployment ranges from our managed cloud to environments you control, with strict tenant isolation in every case — your data is never pooled or shared across customers. For the most sensitive workloads, local inference keeps content entirely on hardware you own.

Consequential actions — anything destructive, external, or hard to reverse — require human approval through built-in checkpoints. Routine work proceeds autonomously, but only within each agent's scoped permissions and budgets, and everything is logged either way.

Every run is recorded end-to-end: what was asked, what data was touched, which tools were called, what actions were taken, and what it cost. The trail is reviewable by your teams, so compliance questions are answered from evidence, not recollection.

It stops. Hard time and spend budgets are enforced by the platform — when a cap is reached, the run halts cleanly and can be resumed deliberately. Running agents can also be redirected mid-task by a human without losing their progress.

Yes. Users authenticate through your identity provider via SSO, access is restricted by email and domain allow-listing, and role-based controls govern who can see and do what — the same identity and permissions discipline you apply to any enterprise system.