Solutions · Machine Learning
Find out what your data can support, then build the models
Before anyone trains a model, we find out how the decision is made today and whether your data can support a better one. Then agents build the models the work needs, and your team reviews each one before anyone relies on it.
Warehouse
Discovery
Claims
Readiness
Models
Workflows
01The process
Six steps, and what each one leaves you with
The model comes last. Every step before it ends in something your team can inspect, and agents carry the checking and the reporting.
Stand up an isolated warehouse
We load the data the work needs into a warehouse of its own inside your Authentica environment, refreshed from your systems on a schedule. Analysis and modeling happen there, so nothing touches a production system while we learn.
You end up withA warehouse of your own with the data the work needs, kept apart from your production systems.
Interview the people who make the decisions
Planners, finance, operations, and the IT team that runs the systems. For every number in the process we ask who creates it, who can change it, and what kind of number it is.
You end up withA written account of how each decision is made today, and who owns each number in it.
Test every claim against the data
Each interview becomes a set of claims about how the process works and what the data holds. Agents and workflows test those claims against the warehouse, and anything the data does not support is flagged for a person to resolve.
You end up withA record of what your people believe about the process set against what the data shows.
Write the data readiness report
A measured account of the data: which sources are fresh, where the gaps are, and which decisions have enough history to model. It is evidence you can inspect, linked to the source it measured.
You end up withA readiness report stating which questions the data can support, which it cannot yet, and why.
Build the models the decisions need
With the data ready, agents build and test as many kinds of models as the work calls for. Each one is compared with how the decision is made today before it goes anywhere near the work.
You end up withModels matched to specific decisions, each measured against current practice.
Put the results to work
Results show up first as dashboards and as answers from the Analytics agent. Once they hold up, the AI forward-deployed engineer builds them into workflows, and any step that changes a plan or an order waits for a person to approve it.
You end up withModels running inside governed workflows, with a person approving what matters.
02Discovery
The questions that decide whether a model is useful
They are business questions, and we settle them with the people who own the decision before any modeling starts. We also freeze a baseline of current practice so the comparison stays honest. A model earns a live trial by beating that baseline on past decisions first.
- Q1 Who creates each number in the process, and who can change it?
- Q2 Is that number a budget, a target, a forecast, an allocation or a recommendation?
- Q3 Where does the plan break first: the starting numbers, the changes made along the way, or how people act on them?
- Q4 What should a model optimize, at what level of detail, and over what period?
- Q5 What did current practice look like at the moment each decision was made, and can we reconstruct it?
- Q6 How is the cost of a wrong decision counted today, and which parts are measured rather than estimated?
03After readiness
What agents build once the data is ready
Agents build whichever models the decisions call for. The results land where the rest of your agents work, so a forecast can feed a workflow, and any step that changes a plan still waits for the person who owns it.
Everything built for you is yours, including the models and the code that trains them, and every action the agents take along the way is on the record.
- Forecasts Demand, sales or volume, by product, location and period
- Clustering Customers, sites or products that behave alike
- Allocation How much of each item goes to each location
- Mixes and curves Product, size or assortment ratios by location
- Flow When stock or work moves from one place to the next
- And so on Discovery decides what is worth building first
04Questions
What teams ask before the data work starts
Do you need access to our production systems?
We need read access to the sources the work depends on. That data is loaded into an isolated warehouse on a schedule, and discovery, analysis and modeling happen there. When a result is ready to act on, the steps that change anything in your systems run as workflows with approval gates.
What kinds of models do you build?
Whatever the decisions need once the data is ready. For planning work that usually means forecasts, clustering, allocation, product and assortment mixes, and flow between locations. Discovery decides which of them are worth building first.
How do we know a model is better than what we do today?
Before modeling starts, we agree with your team what the model should optimize and freeze a baseline of how the decision is made today. A model has to beat that baseline on past decisions before it is tried live, and a person on your team approves each step forward.
Does building models mean training on our data?
Yes, and only for you. The models built for you are trained on your data for your business, and your data is never used to train models for anyone else. The models and the code that trains them are yours, like everything else built for you on Authentica.
Do we need a data science team to work with you?
No. You supply the people who own the decisions and someone who knows the data sources. Agents do the discovery, the data checks and the modeling, and our engineers and data scientists review the work and handle anything the agents cannot.
Bring the decision you want a model for
We start with one decision and the people who own it, and scope the data work from there.