and sound judgment.
Now feed your entire company into that one person. Then create agents that use this knowledge to perform tasks better than people can, or to help where human memory falls short.
- AI can’t do this on its own. The data needs to be organized and the agents orchestrated. That’s what we do.
- With governance on top, so even a regulated industry can do it with confidence.
- Raw AI across a whole company’s data gets expensive fast. We do the heavy preparation once, so every question after that runs on ready-to-use context instead of starting from scratch.
- Meet Customer A. A happy account, and a quiet one.
- Every day, A4 reads the emails, tickets and contracts between you and them.
- Today, it notices something. They keep asking for what you sell, and their contract is up soon.
- So it decides this is worth a conversation, and who should have it.
- Their account executive calls with a reason, not a pitch.
- Now imagine it doing this for every customer, every day.
- More value for every client. A more strategic role in their business.
Everyone is waiting on the one person who can piece it together.
Sales knows the customer. Finance knows the economics. Legal knows the rules. Operations knows what actually works. Your most experienced people know the exceptions and the history. The information is all connected. The systems holding it are not.
Take one enterprise customer. What your company knows about it sits in nine places, each with its own owner. A young company on one well-run platform may have joined a few of them. A company with a vast ecosystem of systems often has gaps between them, and no single one holds the full context for fast, accurate work.
- A sales email promised an uptime credit.
- Legal never saw it.
- Finance owes it.
- Support has never heard of it.
Solve it once. Know it everywhere.
When the details slip through the cracks, your vendors and customers feel it too.
The information was there. It just wasn’t in the same place at the same time.
That is what happens when your business knows more than any one person or system can see.
Never take an agent’s word for it.
Most platforms ask you to trust what AI tells you. Cognethics is built the other way around: the platform itself is the skeptic. Three rules are built into every agent: every answer shows its source, every agent answers to a named person, and every piece of work lands on the record under its name.
Everything else on this page stands on that rule. It is what lets you say yes to AI without crossing your fingers.
Ask in plain words. Get the whole story at once.
A simple question replaces endless manual searching. Every answer cites the document by its exact name and its path in your own system, so anyone can open the source and check it.
Behind the answer is one living picture of every customer, asset, contract and case, read from the records your business already produces (contracts, invoices, manuals, email) and linked the way your business actually works, in your vocabulary, not ours. Facts the AI extracts are checked before they are trusted, and the uncertain ones go to a person to decide.
One plain question returns the whole picture: the numbers, what is driving them, and a recommendation.
AI finds, cross-checks and drafts. The platform carries it through.
The purchase order is placed, the credit memo is posted, the notice goes out, and the result is written back to the system the record belongs in. A fixed rule decides what may happen, not the AI. The AI never pays the vendor.
- Rules do the routine.Your people draw a line. Below it, the rule they wrote is the approval, so routine work moves without waiting on anyone.
- People decide the exceptions.Above the line, a person approves first. The work reaches them already drafted, every fact linked to its source, so they decide instead of digging.
- Every agent acts for a real person.Like any employee, each agent has its own login, permissions, spending limit and a named person it answers to. Anything beyond that person’s access is a time-limited grant your team approved.
- It acts before anyone asks.A maintenance date approaches, so the platform checks the spare parts, finds one missing, raises the purchase order and tells the approver why, across departments, on one platform.
Spend your time on the decision, not the search.
The model brings intelligence. Your company brings the context and the rules.
A general-purpose AI assistant can only work with what you give it. It does not inherently know your customers, your contracts, your equipment, your policies, or who has authority to do what.A4 provides that operating context. It prepares and structures the information, applies your business rules and permissions, gives agents the right context for the task, and governs how they investigate, reason, and act.The intelligence comes from the model. The way the work is framed, grounded, governed, and executed comes from A4.A4 is not a search tool, a data warehouse, or a chatbot layered over your data. It is the operating layer that lets AI work inside your business.
A bigger model can read more. That does not mean it should.A4 determines what matters, removes what does not, preserves the source behind every piece of context, and applies your rules throughout the process.The advantage comes from giving AI the right context, efficiently, not from giving it more to read.
Your systems stay. Your data stays yours.
Nothing has to be ripped or replaced. The platform reads the systems you already own, acts across them under a named person’s authority, and writes the result back.
- LAYER 01Your peopleOne place to work. Ask, see, decide and act the same way, whatever the task. Nobody has to be an expert in five systems to get one thing done.
- LAYER 02A4One living picture of every customer, asset, contract and case. It does the work and writes the result back.
- LAYER 03Keep your systems.Your systems of record stay in place while A4 works across them. Move one workflow at a time, without forcing a rip-and-replace. When A4 can take over the work, retire a system only where it makes sense.
- Your data sits inside your own organization’s boundary and is never mixed with anyone else’s.
- An agent sees and does only what the person it works for may; anything more is a time-limited grant your team approved.
- Decisions, who made them and their evidence stay on an audit trail that cannot be changed without leaving a trace.
- In Studio, use the AI model your company has already chosen, under the same rules.
Connect what you already own.
The platform reads what your systems already hold, through connections you approve, and can write approved changes back to them, with every step on a tamper-evident record.
Connecting is where it starts, not where it ends.
A4 also does the work itself: finance, legal, operations, HR, sales, projects, learning and more, built for people and agents together. So every system you connect is one you can move onto A4 later, one workflow at a time, with its history already in the record, connected and understood before anyone decides to migrate.
We run our own company on it every day.
Start with one team and one workflow.
Pick the question that most often sends your people across the islands. For operators of plants, buildings and fleets, it is usually about one piece of equipment and the paperwork behind it: manuals, drawings, letters and emails, parts lists and maintenance records.
- Pull the history.The platform pulls a piece of equipment’s history out of email, document stores and technical files.
- Write it up, with sources.It writes that history up as an investigation in which every statement links to the document it came from, ready for an engineer to review.
- File it back.The approved result is filed back into Microsoft 365, where your people already work.
“What should the strategy of my business be?” and “I have a broken pump. From all this hard data, is there a solution?” are two very different questions. We start with hard facts, where there is a true right and wrong answer and it is somewhere in your records. Nothing touches your systems until you say so, and you expand one workflow at a time.
Describe your business. The platform stands it up.
Tell the platform what your business is: the departments, the roles, the rules, who approves what. It stands up the working environment: the organization structure, the permissions, the records, and an agent workforce shaped to your operation, every agent bound to a named person and capped by limits you set.
Try it before you trust it. The platform stands up a disposable working copy of your operation to push, break and throw away. Describe a view, a report or a tool in plain language and it builds that too, on the same data, under the same permissions, approval and audit as everything else. Your people ask for what they need instead of filing tickets for it.
People and agents stand up new tools and views with no engineering cycle and the same governance.
See what the business needs to run before you commit to any of it.
The blueprint tells you how many people the business needs to run, role by role, and shows how much of that shrinks as you hand more of the work to agents.
You see all of it against a described business before anything is delivered for real, and the same description is what stands the environment up when you decide to go.
Whoever you are, the first question is the same.
The first question is whether you can put AI to work without losing control. The answer depends on where you sit: the chair, the security team, the auditor, the people drowning in routine. Here is the case for each.
Permissions that hold.
When someone asks “what did it do, and was it allowed?”, the answer can’t be a promise. It has to be a record. Three controls are built into the foundation, and they are what let your CFO, your CISO, and your auditor say yes.
An agent can only ever touch what you allow.
Permissions are fine-grained and compose into roles scoped to each organization. Access is resolved per person, denied unless you allow it. Every grant is explainable, tracing back to the rule that decided it. “Just ask the AI” can never become a way around your controls: the agent works inside the permissions of the person it acts for, and anything beyond them is a time-limited grant someone on your team approved. Change the person’s access and every agent acting as them changes with it; deactivate the person and their agents are locked out. And it works with the sign-on and directory you already run.
Proof, not promise.
Every action is on a record you can verify.
Each covered change advances a tamper-evident, SHA-256 hash-chained record of what was touched, by whom, and whether it was allowed. Alter one entry and the chain breaks, detectably. An auditor can confirm the record without taking our word for it.
Authority narrows as work goes down. Exceptions climb back up.
Agents delegate multi-step work down a governed chain, each bound to a named person and capped by a hard spending and scope limit. A helper can never widen authority: it can do no more than the agent that launched it. Routine work inside the rules runs on its own; exceptions above the line your people drew wait for the named person, and every refusal is shown in plain language.
Watch an agent do the work on your data.
Bring an active question: a piece of equipment’s history, a finance review, a record you can’t reconcile. We’ll open the platform against your data, let an agent do the work in front of you, and show you the answer arrive with every fact linked to its source.
Tell us the workflow you would start with. A real person replies within one business day.
Priced by the year. Nothing metered.
Annual plans from $9,900 a year: choose 10 apps on Studio, or every app on Team and Enterprise. Larger deployments are scoped with you.
- Every app on Team and Enterprise; choose 10 on Studio.
- Bring your own AI to Studio; we never charge for inference.
- Add Studio seats, compute and storage in fixed blocks.
- Agents are never priced per agent.