Data & automation · I Soft Solutions
AI PoC & MVP Development
Test one valuable AI workflow with real examples, safeguards, and a clear decision point.
The opportunity
A practical answer to your brief.
Before building a broad AI product, we define the task, sample cases, success criteria, and human review needed for a focused proof of concept. A useful pilot should reveal where the model helps, where it fails, and what an initial product would need to operate safely.
Inside the service
Where we can focus.
The exact scope follows your current system, content, customers, and priorities.
Problem and evidence
Choose a narrow task and collect representative, permitted examples including difficult cases.
Pilot implementation
Build a small working flow with model calls, data access boundaries, validation, and traceable outputs.
Human oversight
Define which outputs need approval, what happens when confidence is low, and how errors are corrected.
MVP decision
Assess quality, failure patterns, security, cost, and maintenance needs before proposing a wider release.
AI & data innovation
Prove the workflow before widening the product.
A proof of concept should answer a specific business question with representative data. We map the task, define acceptable errors, build a narrow pilot, and inspect the cases where it fails. That evidence informs whether an MVP is useful and what controls it needs.
Bring to the conversation
What helps us scope it
- One defined workflow and decision owner
- Permitted sample data and edge cases
- Current tools and integration constraints
- Success and review criteria
A useful handover
What the work can produce
- Scoped pilot workflow
- Evaluation notes and failure categories
- Human review and access design
- MVP recommendation and next-step scope
Before we begin
Questions worth asking.
Do we need a large dataset?
The answer depends on the task. We first need representative and permitted examples to understand the workflow, test output quality, and identify failure cases.
Can the pilot connect to our systems?
Potentially. We review data permissions, APIs, and security needs before deciding whether a live connection or an isolated sample is appropriate.
What happens if the model makes mistakes?
The pilot includes validation and a human review path. We record error patterns and decide whether the workflow is suitable for wider use.
The goal
A result your team
can use.
Evidence from a bounded pilot and a clearer decision about whether and how to build an MVP. We agree the scope and success measures with you before work begins.