02 · Service

Digital innovation and product

From validated idea to product in production, with no wasted time or capital.

The problem

Business areas that need to validate digital opportunities before committing to a full engineering roadmap.

How we solve it?

We combine discovery, design and technical prototyping to take ideas to product in short, verifiable cycles.

Deliverables

What you get.

  • Opportunity discovery with hypotheses, success metrics and a validation plan
  • Clickable prototypes and functional MVPs in 4 to 8 weeks
  • UX/UI design with design system and technical handoff
  • Applied AI experimentation (LLMs, RAG, agents) on real use cases
  • Scaling plan if validation is positive
Capabilities

What we bring.

  • Product discovery and roadmap definition
  • Service design and journey mapping
  • UX/UI and design systems
  • Rapid prototypes in code
  • LLM integrations (OpenAI, Anthropic, local models)
  • Data pipelines and RAG over proprietary knowledge

Stack

Figma React · Next.js · Astro Python for ML LangChain · LlamaIndex OpenAI · Anthropic · Ollama Vector DBs (Pinecone, pgvector) Vercel · Cloudflare
Engagement models

How we plug in?

  1. 01

    Discovery sprint

    4 to 6 weeks to validate an opportunity with a prototype and an actionable delivery plan.

  2. 02

    MVP to production

    8 to 16 weeks to bring a validated product to real users with adoption metrics.

  3. 03

    Continuous lab

    Recurring capacity to experiment with new opportunities and bring them to roadmap.

Validate before building

The most expensive way to build software is to build it twice. That’s why we always start by validating the problem, the audience and the value mechanism before investing in full delivery.

We combine product discovery practices (hypotheses, interviews, metrics) with real technical prototyping so the experiment looks like the final product. The deliverable is an informed decision: move forward, pivot or stop.

Applied AI with judgment

We apply generative models when they add value: search over proprietary knowledge, internal copilots, automation of repetitive processes. We always design with guardrails, continuous evaluation and fallback mechanisms. AI doesn’t replace product judgment; it amplifies it when used with discipline.

FAQ

Frequently asked questions.

Is digital innovation the same as an AI project?
No. AI is one capability, not an end. We start from the business opportunity, define success metrics, and only apply AI when it's the right tool. Sometimes the best solution is a simple workflow without generative models.
How do you measure if innovation worked?
Each experiment has success metrics defined upfront (adoption, conversion, time saved, NPS, etc.). If they're not met, we recommend not investing further and we move to the next hypothesis.
Can you work with confidential data?
Yes. We design on-prem or private-cloud pipelines, with local models if needed. We work with NDAs and basic security practices (least privilege, data minimization, audit logs).

Let's talk about digital innovation and product.

Tell us context and need. We respond with an actionable plan.

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