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.

In enterprise AI, SI stands for superintelligence. At ClearData AI, enterprise SI means an operating layer where governed data, AI agents, and orchestration work as one system — not a single all-knowing model, but a composed system that outperforms any one model, tool, or person on your company's own work. (In IT services, SI can also mean systems integrator.) Enterprise superintelligence →

AI engineering is the discipline of turning AI models into dependable production systems. It covers choosing and routing models, writing prompts, engineering context, connecting tools and data, evaluating outputs, and operating the result with monitoring, budgets, and governance. Prompt, context, harness, and loop engineering are its layers, from a single instruction up to autonomous agents. AI consultancy & engineering →

Prompt engineering is writing the instructions a model receives — its role, task, constraints, examples, and output format — so it answers reliably. It matters, but it is rarely enough in an enterprise: a perfect prompt still fails without the right data in front of the model, tools to act with, and checks on the result. That is where context and harness engineering take over. Advanced tool building →

Context engineering is deciding exactly what information an AI model sees at each step — the relevant documents, data, memory, and tool results — and leaving everything else out. Well-engineered context makes answers more accurate and runs cheaper, because the model reasons over a few precise facts instead of a flood of tokens. Better context beats bigger prompts. AI Agent Fleet →

They are layers of one stack. Prompt engineering writes the instruction. Context engineering decides what information the model sees. Harness engineering builds the tools, memory, guardrails, budgets, and verification around it. Loop engineering runs the whole system on its own — planning, acting, verifying, and continuing under human oversight. Each layer makes the one beneath it reliable at scale. Agentic systems & loop engineering →

LLM fine-tuning is further training an existing large language model on curated examples so it adopts a domain's vocabulary, output format, or judgment. It does not teach the model new facts as reliably as retrieval does — its strength is consistent behavior. Most of the success or failure is decided by the dataset: clean, deduplicated, scrubbed of personal data, versioned, and separate from evaluation data. AI Data Foundry →

Usually start with RAG. Retrieval-augmented generation grounds answers in your current documents with citations and needs no retraining when the data changes. Fine-tune when you need a consistent style, format, or specialized judgment the base model lacks — or a smaller, cheaper model that performs like a larger one. Many enterprise systems combine both. Knowledge synthesis & RAG →

Synthetic data is artificial data generated to mirror the statistical patterns of a real dataset without copying any real person or transaction. Enterprises use it to build and test software, train and evaluate AI agents, run vendor demos, and simulate rare events such as fraud. Good synthetic data is measured twice: for privacy, so nothing real leaks, and for fidelity, so results still hold. Synthetic data generation →

No. A data warehouse stores structured, modeled tables for reporting. A lakehouse also keeps raw and semi-structured data — documents, logs, images — in open formats, and refines it through governed layers. That lets one foundation serve business intelligence, data science, and AI agents, instead of copying data back and forth between a lake and a warehouse. The ClearData AI LakeHouse →

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.

It is the foundation. Superintelligence emerges from a system of governed data, specialized agents, and orchestrated fleets, and the LakeHouse supplies the governed data: quality-scored, versioned, and refined into a Platinum meaning layer that agents query through governed endpoints. Harness engineering wires that ground truth into every agent, which is why answers stay sourced instead of guessed.

The AI Agent Fleet 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.

Loop engineering is the practice of designing a system that runs an AI agent on its own, rather than prompting it one step at a time. The loop discovers the work, plans it, acts through governed tools, verifies the result with a separate checker, and decides whether to continue — all under hard time and spend budgets with human oversight. Where harness engineering builds everything around the model, loop engineering sets it in motion.

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. The ClearData AI LakeHouse adds a fourth tier, Platinum, which turns published data products into meaning that AI agents can use. Each layer adds trust, so people and agents always know what quality of data they're standing on.

Platinum is the LakeHouse's meaning layer — the tier AI agents talk to. It is built only from published data products that have a schema, an owner, and a quality score, and it adds an ontology, a knowledge graph, vector search, and domain warehouses on top. Agents reach it through governed MCP endpoints, so they reason over trusted data rather than raw files.

Yes, through governed MCP endpoints. Agents such as the AI Agent Fleet, Claude Code, or Claude Desktop connect with a personal key or company sign-in, and every call is scope-checked, rate-limited, and audited. Teams can also turn saved queries and lookups into purpose-named agent tools, which go live only after an administrator approves them.

No — it builds the dataset, which is where most fine-tuning projects succeed or fail. The LakeHouse curates, cleans, and versions training-ready data in eight formats, from question-and-answer chat to preference pairs and reasoning traces, and exports train, validation, and test splits ready for standard open-source trainers on the compute you choose.

It is designed to be, and the LakeHouse measures it rather than assuming it. Every synthetic dataset carries a privacy score based on exact copies of real rows and how close generated rows sit to real ones, alongside fidelity, diversity, and validity. Output is tagged as synthetic with full lineage, so nobody mistakes it for production data.

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.

An AI data foundry is the process — and the tooling — that turns raw enterprise data into assets AI can safely use. The ClearData AI Data Foundry does it in three stages: intelligent data cleansing so the data can be trusted, synthetic data generation so it can be shared and scaled, and fine-tuning dataset curation so it can teach a model your domain.

It removes what people can't see: zero-width spaces, soft hyphens, byte-order marks, direction-control codes, and hidden watermark characters that AI-written and copy-pasted text often carries. It also flags personal data and records content provenance where files carry it. Every change is counted per column and logged, and writing-style signals are offered only as advisory — never as a verdict.

Use synthetic data whenever real data is too sensitive, too scarce, or too clean to test with — for development and QA environments, vendor demos, AI agent testing, and rare events such as fraud that real data barely contains. Keep real data for final validation and production. The Foundry measures privacy and fidelity so you know how far each synthetic dataset can travel.

A good fine-tuning dataset is in the right format for the training method, free of duplicates, low-quality and toxic examples, scrubbed of personal data, and strictly separated from the test questions used to evaluate it. It should also be versioned and documented, so you can explain exactly what the model learned from — which regulations such as the EU AI Act increasingly expect.

An AI-native CRM fills itself in from the work your team already does. ClearData AI CRM turns recorded meetings, voice memos, and AI agent research into contacts, deals, commitments, and tasks — then keeps a person in charge of every change. Instead of reps typing up notes, the CRM listens, proposes, and waits for approval.

Recorded Microsoft Teams meetings are collected automatically across your organization and transcribed. AI extracts the commitments, opportunities, and follow-ups, and each suggestion lands in a review inbox grouped by meeting, with the supporting evidence shown. Nothing reaches the pipeline until a person accepts it, and every accepted record remembers where it came from.

Yes, within limits you control. An administrator switches on the CRM's MCP server, and each person creates their own key that sees exactly what they can see. Agents such as Claude Code, Claude Desktop, or your AI Agent Fleet can search, create leads, add notes and tasks, and draft quotes — but there are no delete tools, and agents can never send, approve, or accept a quote.

No. Your Outlook inbox appears inside the CRM with a side panel showing the related records, and you choose which messages to log, but the CRM itself stores none of your mail. Administrators can also withhold transcripts, contact details, custom fields, or attachments from the AI entirely.

Yes. Typed custom fields, page layouts, and stage rules let you shape the CRM to your process, and starter packs give manufacturers, distributors, restaurants and caterers, consultancies, resellers, and software companies a ready-made head start.

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.