Your Partner in AI Projects

about

At Sparkbit, we help companies move from “this could be useful” to “this is up and running.”

Whether you're exploring a new AI initiative or struggling to get real value from existing tools, we help you identify, validate, and scale AI use cases without the hype, fluff, or black-box thinking.

We focus on operational AI: tools and systems that solve real problems inside real organizations. From computer vision to AI-powered interfaces, our job is to make sure what you build actually works, and actually matters.

What we de - discovery

AI use case discovery & validation (AI Navigator)

We start with a structured discovery process to help you answer the most important question early:

Is this use case worth building, and if so, how should we build it?

We work with both business and technical stakeholders to:

  • Identify high-value use cases tied to business outcomes
  • Assess feasibility based on your data, infrastructure, and constraints
  • Define the system architecture, development roadmap, and risks
  • Provide clarity on costs, timelines, and technical challenges

You’ll leave this phase with a concrete go/no-go recommendation, and everything you need to make an informed decision.

Want to test an idea without wasting months or budget? This is where to start.

Explore AI Navigator
What we do - scaling

AI system design and development

Once validated, we help you turn the idea into a working system. We cover the full stack, from backend to AI model integration to frontend UX—based on what your users and infrastructure require.

Depending on the use case, this can include:

  • AI model selection or development (LLM, CV, structured ML)
  • System architecture and API design
  • Data pipeline setup and integration
  • Interface design and user flows
  • Ongoing model iteration and feedback loop setup

We don’t just “add AI”. We build systems around it. Ones that scale, adapt, and integrate cleanly into your operations.

Set up a discovery call
success stories

Selected clients we've already helped:

Our client is a Swiss company that makes high-precision instruments for chemical analysis. They wanted to create an AI assistant to help field technicians with problem detection and troubleshooting. The plan was to combine a large language model (LLM) with a retrieval-augmented generation (RAG) system to quickly search their document database and improve the accuracy of chatbot responses. We held extensive user workshops and built a pilot version of the solution, which proved both feasible and valuable for the client’s support team. We're now working on developing the full-scale solution.
PoC & workshopsfora swiss manufacturing company
The client is a Polish company that provides media monitoring services. Their platform analyzes media publications and broadcasts to give sponsors insights into their projected advertising value. They approached us to upgrade their deep learning solution to recognize sponsor logos in video broadcasts. Our AI team conducted user workshops and system prototyping, followed by developing a proof of concept (PoC). We provided the client with recommendations on system architecture, a project roadmap, and a cost structure document. They are now exploring financing options for the proposed solution.
PoC & workshopsfora media monitoring company
A Switzerland-based startup specializing in brand protection approached us with a unique idea: to develop an AI-based mobile app capable of recognizing and authenticating labels printed with their innovative ink, which acts as a "fingerprint" to detect counterfeits. We built a proof of concept (PoC) to test the most technically challenging part of the project - computer vision. After experimenting with different devices, lighting conditions, and vision algorithms, we had limited success. In the end, we concluded the project wasn’t feasible, which ultimately saved the client from unnecessary expenses.
PoC developmentfora Swiss startup

To learn more about our approach to building AI systems, read our blog

Title
SM1You need these six building blocks to start AI in your organization successfully (I)SM4Don't start AI without Proof of ConceptSM9Your AI won’t be perfect, but it can be good enoughSM10Investing in AI - Bespoke system vs SaaSSM25Choose the right AI use case first
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the team

Meet our AI team