Define the task
Select a bounded workflow, document its inputs and outputs, and agree on quality, cost, and latency targets.
WORKFLOW SPECIFICATIONApplied intelligence, built around your business.
Models built
around your work.
Your process. Your standards.
Your model.
Your model.
We turn the recurring work inside your business into custom AI models. Trained on approved examples. Tested against your current approach. Built for infrastructure you can operate.
Explore the workWhat we’re building
From task-specific training to end-to-end execution.
From individual tasks
to complete workflows.
We’re building toward models that carry a defined business workflow from the first request to a verified outcome. That means learning the whole job: the decisions, the tools, and the follow-through needed to finish it.
01 The foundation
Learn the task.
Train on approved examples of a specific job: understand a request, extract the right information, or apply a policy. Establish reliable behavior before expanding the scope.
Measured on task quality.
02 The next layer
Learn the sequence.
Train on connected decisions and actions. Retrieve context, select a tool, inspect its result, and choose the next step as the state of the work changes.
Measured on the full sequence.
03 The goal
Learn to finish.
Build complete workflow models that follow through to the outcome, recover from failed actions, and involve a person when approval or judgment is needed.
Measured on verified completion.
What end-to-end means in practice
Illustrative target · Customer support
The request
“My order arrived damaged. I need a replacement.”
- 01
Understand
Identify the issue and ask for missing information.
- 02
Investigate
Retrieve the order, check the policy, and confirm stock.
- 03
Decide
Select a permitted resolution. Obtain approval if required.
- 04
Act
Create the approved replacement through business systems.
- 05
Verify
Confirm the replacement, notify the customer, and close the case.
One model trained across the full workflow
The completed work
Replacement created. Customer informed. Case resolved.
02 / How we work
A model is only useful
if the work gets better.
Agree on the task, establish a baseline, and measure whether adaptation improves the workflow before deployment.
Prepare the data
Curate approved examples, remove duplicates, and hold back representative cases for independent evaluation.
APPROVED EXAMPLES & HELD-OUT TESTSTrain and evaluate
Compare a custom adaptation with a strong baseline. Review task quality, failure modes, operating cost, and speed.
BASELINE COMPARISONIntegrate and monitor
Validate the serving path, route exceptions to people, and use approved corrections to improve future versions.
DEPLOYMENT & REVIEWDEPLOYMENT CRITERIA
A deployment decision supported by results on your task.
04 / Questions
Before we begin
Why a custom model instead of another prompt?
Prompts and retrieval are often the right starting point. Custom training is worth testing when a repeated task needs consistent behavior, a specific output style, or better economics. We compare against a strong baseline first; tuning has to earn its place.
What do we need to get started?
One well-defined workflow, examples of good work, and someone who can judge the outcome. The amount and type of data depend on the task. We start by finding the gaps, agreeing on success, and building an evaluation set.
Can we use our existing model provider?
Where the model, adapter format, and provider are compatible, that's the goal. Hosting options are confirmed during scoping. Portability depends on the base-model license, architecture, and serving support.
What happens to our data?
Before a pilot, we agree on what data is needed, who can access it, where it is processed, and how long it is retained. Sensitive data, provider terms, and deployment constraints shape the training approach.
Is this for every workflow?
No. The best starting points are repeated, bounded tasks with clear feedback. Frequently changing facts may belong in retrieval; exact business rules may belong in code. Custom models can work alongside both, with human review where it matters.
Next steps
What would you
like to improve?
Bring a recurring task, a few examples of good work, and a definition of better.