AI for business, embedded in a clearly defined process.
From document search to content generation, we define a use case, validate the results and build in human oversight.
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- Use case
- Sample-based test
- Review and integration
An engagement to define around your data and your process.
Where AI can help—and where it must be validated.
We use generative AI, RAG systems and OCR to add new capabilities to business software. We start with a specific task and the available data, define what the solution must do and how its results will be assessed, then integrate it into the team’s work.
When your team has to search through large volumes of documents, rework recurring content or manually transfer information from scans and PDFs into business systems.
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Scattered knowledge
Procedures and documents require repeated searches; a source-grounded assistant can make them easier to consult.
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Recurring content
Drafts, training materials and summaries take time to produce, yet still require accountable review.
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Data locked in documents
Information from PDFs and scans is copied into systems; extraction and validation must be designed together.
Define the task first. Then test the AI.
RAG, generative models and OCR are technical options, not the starting point. The choice depends on the required outcome and the data that can be used.
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We choose a verifiable use case
We define the inputs, expected outputs, people involved and situations in which the solution must stop or request confirmation.
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We assess representative examples
We compare responses or extractions against agreed criteria, including errors and edge cases.
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We design for real-world use
Only after validation do we define integration, roles, access, human review and monitoring.
A meaningful test has explicit boundaries.
Activities and conditions are defined around the actual case before the proposal.
Activities to include in the scope
- Use case, available data and validation criteria
- Testing on an agreed representative sample
- Review of responses, extracted data and generated content
- Integration, access and oversight tailored to the context
To assess together
- Quality, usage rights and availability of data or documents
- Providers, data processing and security requirements
- Quality thresholds, edge cases and review responsibilities
The initial discussion helps frame the use case. Any subsequent test has a scope, activities and costs agreed before work begins.
Generative AI already integrated into a live platform.
As Tentacle evolved, we worked with Progetto Automazione and Digital Dictionary to integrate generative features for courses, podcasts, learning paths and AI agents.
This case study documents generative AI within the Tentacle platform; it does not claim that the project includes RAG or OCR capabilities.
Before we begin, let’s clarify.
A few answers to help you get to know us.
Ask ZenCode a questionCan AI work with our documents?
It depends on the documents’ availability, quality, usage rights and sensitivity. Before choosing a model or provider, we define access, data processing and the scope of the test.
How do you assess response quality?
We define examples and acceptance criteria for the use case. We also assess errors and edge cases; where the impact requires it, an accountable person remains responsible for review.
Is artificial intelligence always necessary?
No. If a deterministic rule, better search or a straightforward integration solves the problem with less complexity, those options should be considered first.