Automation AI / machine learning for companies
Machine learning and predictions
We assess whether data can support a useful model and build only when there is a clear problem to solve.
Background
When this service makes sense
Models for scores, classifications, predictions or recommendations where sufficient data is available.
- Main needmachine learning for companies
- Role in the projectAI agents, document processing and automation connected to real processes, with rules, logbooks and human verification.
- Result pursuedWe assess whether data can support a useful model and build only when there is a clear problem to solve.
Deliverables
What usually comes into the project
The exact list shall be adjusted after the audit, but these modules frequently appear in machine learning projects and predictions.
01Analysis of the data set
02Objective definition
03Baseline model
04Accuracy assessment
05Flow integration
Connection
Service works better when it's connected to the rest of the project
Any service works better when connected to the rest of the ecosystem: content, SEO, data, operation, hosting and maintenance.
- AI agents
- Document processing
- Sales automation
- Integration with AI platforms
Recommended pages
Pages related to this type of service: technologies, fields of activity, guides and complementary services.
Frequently Asked Questions
Before we begin the project
Choice, stages and support for Machine learning and predictions.
01 / Choice
What's useful to know
Do we need a lot of data?
I usually do. If data is scarce, a controlled system of rules or generative AI can be more effective.
What information are we preparing for the first discussion?
Examples of requests or documents, process steps and approved sources. We clarify what data can be used, who validates the result and what actions require approval.
02 / Planning
Purpose, budget and stages
What can the project include?
For Machine learning and predictions, starting points are: Data Set Analysis; Objective definition; Baseline model; Accuracy Assessment; Flow Integration. In the offer we specify deliverables, limits and responsibilities; the list adapts to your situation.
How do we set the budget and the timetable?
We separate data preparation and integration from the cost of use. The volume, models, external services and the level of human verification influence the estimation; we start with a limited and measurable flow.
03 / Continuity
Verification and support
How do you verify the result before handover?
We use representative examples, including difficult cases, and compare results with validated answers or rules. We track errors, costs and required interventions: a fluent response is not proof of accuracy.
What happens after handover?
Updates of sources, adjustments of rules and reassessments of results may be required after launch. We set cost monitoring, usage limits and escalation to a person for sensitive situations.