Documents read one by one
Invoices, forms, contracts, and attachments are opened, interpreted, and copied by hand.
Let's start with a repetitive and measurable task. Let's design a system that classifies, extracts, suggests, or responds, integrating with the tools you already use.
Luxygroup analyzes the process, builds a prototype using real-world scenarios, integrates the solution into existing software, and maintains human oversight and traceability where needed.
You don't need to start with a huge project. You need to identify the areas where qualified people spend time on predictable tasks.
Invoices, forms, contracts, and attachments are opened, interpreted, and copied by hand.
Emails and tickets arrive without a priority level, category, or a pre-written initial response for the agent.
Procedures and information are scattered across files, folders, and people, who end up creating bottlenecks.
Anomalies, emergencies, and useful indicators only become apparent after hours of monitoring or when the problem is already visible.
If an activity is frequent, follows recognizable patterns, and generates a lot of data, it’s worth measuring. The initial analysis helps determine the potential benefits, the available data, and where human oversight should remain.
Describe your situationWe connect data sources, company policies, and existing tools. The output reaches the team right at the point where they need to make a decision or take action.
Information extracted from PDFs, images, and forms, with validation and exception reporting.
Requests sorted by topic and urgency, with consistent drafts to be approved before submission.
Responses based on procedures and authorized documents, with sources available to the team.
Useful indicators highlighted to help people focus on the cases that truly require attention.
Who uses it, what data goes into it, where it slows down, and what it costs today.
Let's agree on what needs to be improved and what indicators we can use to measure that improvement.
Let's write the most useful block of code, test it with real-world examples, and correct it right away.
New features and integrations are coming after we've seen the value of the first release.
During the initial assessment, we evaluate the volume and quality of the data, the risk of errors, and the expected benefit. If simple automation is more suitable than AI, we’ll let you know.
Clear answers regarding feasibility, risks, and how we work—before committing time and budget.
Not always. It depends on the use case. For document extraction or internal search, representative examples and well-organized content may be sufficient; for predictive models, however, sufficient historical data is required. We verify this before proposing development.
We design the architecture, select vendors, and establish retention policies based on data sensitivity. These decisions are documented prior to release; we do not assume the indiscriminate use of public services.
If the software provides appropriate APIs, export functions, or technical access points, we can integrate the workflow. When an integration is unreliable, we identify limitations and alternatives before beginning development.
Yes. We prefer a limited pilot project with real-world examples, success criteria, and clearly defined oversight. Only after a successful evaluation do we expand the system to other departments or larger scales.
That's not the goal. We automate predictable steps and route exceptions to the right people. For sensitive processes, we maintain approvals and traceability.
It depends on data sources, integrations, the required level of accuracy, and the level of control. After analyzing the process, we define an initial phase and provide a clear estimate, avoiding the need to plan everything in detail before we have concrete evidence.
Describe it in simple terms. We'll help you figure out whether it's worth automating, which approach to take, and which test to start with.