Solutions
Customers use Authentica for four kinds of work. Each one runs on the same operating model, the versioned description of how your organization works, and your people review every change to it.
01The solutions
We start with one and add the next when the first has paid for itself. Each builds on what the last one wrote down.
A workflow is one piece of work written into your operating model, such as a freight audit, a three-way match or an inbound quality check. Agents run the steps in the systems you already use, and the steps that need a person wait for one. We build the first one on sample data within a week, so you can watch it work before you hand anything over.
In production Typically 3-10x per workflow, 3x minimum.
Explore Workflows02 / 04
Best forOngoing work that needs an owner, like a queue, an inbox or a standing report
An AI employee is an agent with a standing role in your organization, such as accounts payable or shipment tracking. Work reaches it through Kate in Console or on a schedule. It acts only within the limits your operating model sets, hands what it cannot decide to the person responsible, and leaves a record of every action.
In production Each capability is set to run, wait for approval, or stay off.
Explore AI employees03 / 04
Best forEngineering teams with a review and release process they already trust
Software Factory writes your development process into the operating model: who can propose a change, who reviews it, which checks must pass, and who merges. Coding agents do the work inside that process. A different agent reviews every change, and the merge stays with your engineers.
Worked example: the Kubernetes project’s public review rules, encoded and tested on six months of changes the model had never seen.
Explore Software Factory04 / 04
Best forForecasting and planning decisions that rest on data nobody has fully checked
We set up an isolated data warehouse, interview the people who make the decisions, and have agents test what they said against the data. Once the data is ready, agents build the models the work needs, from demand forecasts to allocation, and each one has to beat current practice before it is used.
Each step ends in something you can inspect, from a readiness report to models running in governed workflows.
Explore Machine Learning02The layer underneath
The operating model is a versioned description of how your organization works: who owns what, which steps need approval, what each record means. Agents run it and your team builds on it. Whichever solution you start with, the next one builds on the same foundation instead of starting over.
Anyone with the right access hands Kate a job in plain language in Console, and she gives it to the agent responsible.
Steps marked for approval wait for a named reviewer. 95% handled autonomously, the uncertain rest escalated, every action logged.
Each agent action is logged against the task and the record it touched, and each decision keeps who made it and when.
The operating model, the workflows and any software we write for you are yours, and every change to them is reviewed by your people.
We scope tight on purpose: one workflow and one measurable result, and then you decide whether there is a second. Whatever comes next runs on the same operating model.
