Supralab From processes to operational AI systems.

Operational AI · Product delivery

Transform your business processes into operational AI systems in few weeks, not months.

We design and deliver real systems that automate workflows and increase team productivity - built from your problem, shipped with a modern stack and AI where it earns its place.

Discuss your use case How the sprint works

AI Product Sprint · 4 - 6 weeks · Working software, not slide decks

The gap

Most teams do not need another strategic audit. They need systems that run.

If day-to-day work still depends on copy-paste, inbox archaeology, or one-off prompts, AI stays a side experiment instead of operational leverage.

Manual processes

Critical work trapped in spreadsheets, email chains, and repetitive clicks. Errors scale with volume.

Inefficient workflows

Handoffs between tools and teams add latency. Nobody has a single view of what “done” means.

Underused AI

Models are available, but not embedded in guardrails, data access, or review steps - so adoption stalls.

Vendor dependency

Black-box tools and endless change requests. You need ownership of the logic and the stack.

How we work

AI Product Sprint: from problem to production-shaped delivery

Four phases. One objective: a system your team can use immediately - scoped to a real business problem, not a generic pilot.

Problem framing

We map the workflow, constraints, and success metrics with your stakeholders. Outcome: a crisp scope and acceptance criteria.

System design

Architecture, data flows, human-in-the-loop points, and tool choices - chosen for maintainability, not trends.

Implementation

We build the working system: integrations, AI components, automation, and observability you can operate.

Delivery

Handover, documentation, and a path to extend. Your team owns the codebase and the roadmap - not a dependency on us for every tweak.

What you get: executable software and clear boundaries - not recommendations buried in a PDF.
If it cannot be run by your team in your environment, it is not done.

Examples

What “operational AI” looks like in practice

Illustrative patterns from real delivery work - problem, build, and measurable outcome.

AuthorKit (open source)

Problem
A collective of writers needed a structured writing environment, integrated into their workflow, with AI capabilities - without relying on proprietary platforms.
Solution

Design and development of an AI-powered writing workspace inside VS Code:

  • AI-assisted writing API
  • Dedicated VS Code plugin
  • Content structuring (chapters, narrative consistency)
  • LLM integration for contextual assistance
Result
  • Fully operational writing environment
  • Open source and fully controlled by users
  • Scalable foundation for other use cases

Source code: github.com/SupraLab/authorkit

AI video processing pipeline

Problem
A major training organization had hundreds of hours of video content that were hard to exploit (no transcription, no structure, low engagement).
Solution

Implementation of an automated AI pipeline:

  • Transcription (Whisper)
  • Smart summarization
  • Automatic quiz generation
  • Audio translation (ffmpeg + ElevenLabs)
  • Workflow orchestration (n8n)
Result
  • Passive content turned into interactive learning material
  • Significant acceleration of content production
  • Reduced manual workload

API auto-documentation pipeline (ERP)

Problem
During an ERP refactoring, API documentation was fragmented in Confluence and difficult to maintain, leading to inconsistencies between documentation and actual implementation.
Solution

Design of an automated documentation and validation system:

  • XML extraction from Confluence
  • Automatic Swagger generation
  • Test execution via Newman
  • Response validation
  • Metadata extraction (headers)
  • Dynamic mock server feeding
Result
  • Always up-to-date documentation
  • Reduced integration errors
  • Faster development and testing cycles

AI Product Sprint

Duration: 4 - 6 weeks (scoped to one primary workflow or system)

Fixed focus, tight feedback loops, and a delivery you can run - not a six-month “transformation programme.”

Delivered

  • Working system deployed in your context (or production-shaped staging)
  • Design and implementation grounded in your business problem
  • Modern stack with AI only where it clearly improves outcomes
  • Documentation and handover so your team can operate and extend

Who it is for: SMEs and mid-sized companies, product teams, engineering leaders, and founders who want automation and execution - not training courses or slide-only consulting.

Supralab

Product mindset. Execution first.

  • Product mindset - We treat your sprint like shipping a product: scope, trade-offs, and user-visible value every week.
  • Execution focus - Fewer meetings, more build. You get code, pipelines, and systems - not generic maturity models.
  • Real-world experience - We have shipped AI-assisted workflows, integrations, and tooling in environments where reliability and clarity matter.

Let’s discuss your project

Tell us the workflow or bottleneck. We’ll reply with whether a sprint fit makes sense and what a concrete first slice could look like.

Email hello@supralab.fr