Product · PoC

AI Proof of Concept: Validate Before You Scale

A well-scoped AI proof of concept lets you confirm that a specific workflow can be automated or augmented with AI before you invest in production infrastructure.

Why Start With a Proof of Concept

Most AI projects fail not because the technology is wrong, but because the scope is too broad and the first production build tries to solve everything at once. A proof of concept inverts that risk. You spend a small amount of time on a narrow problem, collect evidence that the approach works, and then decide whether to scale.

At Digiton we build PoCs for clients across 8 countries who want to automate a specific process, such as a document intake pipeline, a knowledge retrieval layer over internal files, or an agent that qualifies inbound leads. Each PoC targets one workflow, uses real data wherever possible, and ends with a live prototype you can actually test.

What a Digiton AI PoC Covers

Problem Definition and Scope Lock

The first step is choosing the right problem. We look for a workflow that is repetitive, has clear inputs and outputs, and is painful enough that a measurable improvement in speed or accuracy would justify the investment. We then lock scope tightly so the PoC does not drift.

Data and System Access

A PoC built on synthetic data tells you very little. We work with your actual documents, CRM exports, or API endpoints from the start. If live integration is not possible in the time window, we use a representative sample that reflects real variation in your data.

Working Prototype

The deliverable is a running prototype, not a slide deck. Depending on the use case this might be a RAG knowledge agent that answers questions from your internal documents, an automation that processes incoming forms and routes them to the right team, or a conversational agent that qualifies leads captured on your website. You interact with it, test it with real inputs, and measure its accuracy against a baseline.

Evaluation and Go or No-Go

After the prototype is live we run a structured evaluation: accuracy on test cases, latency, failure modes, and integration complexity for a production build. You get a written assessment with a recommendation on whether to proceed, which parts of the design to change, and what a full build would involve.

Common PoC Use Cases We Build

• Document extraction and classification from PDFs, emails, or scanned forms
• Internal knowledge retrieval over company documents using retrieval-augmented generation
• Conversational lead qualification connected to a CRM or spreadsheet
• Automated report generation pulled from operational data on a schedule
• Customer support triage that routes inbound queries before a human agent responds

From PoC to Production

If the PoC confirms the approach, the path to production is straightforward because the architecture decisions are already made. Digiton handles the full build in-house, including integrations, hosting, monitoring, and iteration. We work in English, Portuguese, and French, and deploy across Europe and beyond.

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