AI modernization

Software you already run, now answering back.

Assistants, automation and grounded search added to the products and systems you have today. Every answer cites its source, and a person approves every action.

Example. Names, numbers and files are illustrative.
How an approved action runs

Fast where it can be. A person where it counts.

The same six steps sit under every assistant and automation we build. Step four is never skipped for anything that changes a system.

  1. 01SignalA ticket, an alert, a question in Slack, a change in a record.
  2. 02GroundPull the documents, logs and records that answer it, and nothing else.
  3. 03DraftWrite the reply, the ticket update or the configuration change.
  4. 04ApproveA person reads it with its sources and clicks send, edit or reject.
  5. 05ActThe change goes through your existing systems and permissions.
  6. 06RecordWhat was asked, what was used, who approved and what changed.
Three ways in

Start with the work that piles up.

Assistants in your productYour customers ask inside the app and get answers from your help content and their own account data, with a hand-off to a person when it should.You keep: The assistant, its knowledge sources and its evaluation set.
Workflow automation with approvalRepetitive work in support, operations and back office is drafted for a person to approve, so the queue moves faster without anyone losing control of it.You keep: The workflows, the approval rules and the audit trail.
Grounded search across your knowledgeOne place to ask questions of the manuals, tickets, wikis and drives your team already has, in the app, in Slack or Teams, or in a headset.You keep: The index, the access rules and the sources it may use.
Where it runs

Wherever your people already are.

In your productA panel inside the app, with the user's own permissions.
In Slack or TeamsAnswers and approvals in the channel where the work is discussed.
In a headset or glassesVoice guidance from your manuals while hands stay on the job. VR/AR software
On the deviceSmaller models on iPhone or a headset where latency or privacy demand it.

On Azure, AWS or your own servers, in the language your system already uses: C# and .NET, Python, TypeScript and Node, Java, Go, Swift, C++ or any other well-documented stack.

Have a queue that never empties?