Reflekt Lab

Quelques mots pour décrire

🧠 Reflekt Lab

"Let AI Do the Overtime For You"

Reflekt Lab builds private AI pipelines that transform messy, real-world data (audio notes, PDFs, Excel files, images, and unstructured documents) into structured, reliable, machine-actionable outputs.

We focus on end-to-end automation, from ingestion to delivery, deployed in controlled environments where privacy, reproducibility, and maintainability matter.

🔧 Our Deployed Use Cases

We automate processes for finance teams, schools, retailers, and inventory platforms across Europe & the Middle East:

- Retail (ME) — Voice-to-Order Pipelines
Transcribe, extract, classify, and generate structured orders from WhatsApp audio.

- WineTech — Inventory Cleanup & Normalization
Automatic de-duplication, normalization, and structuring of wine inventories.

- Fractional Finance — Excel Automation
Take bank exports → categorize lines → update models → generate explanations.

- Education — Administrative Automation
Turn PDFs, photos, forms, and attachments into structured objects for ERPs.

These pipelines replace hours of manual work and reduce error rates.

🏗️ How We Build (Infra & Engineering DNA)

Our stack is designed for reliability, portability, and privacy:

- Private LLM deployments (Llama 3.3, SLMs)
- Hybrid RAG combining embeddings, structured retrieval, compression & mapping
- Containerized services orchestrated for reproducibility
- GitHub Actions-powered CI/CD for automatic testing & deployment
- Terraform-based cloud provisioning (GCP, on-prem setups)
- Separation of concerns between ingest, transform, validate, and export stages
- Stateless services with explicit checkpoints and logs
- Minimal external dependencies to protect customer data

Nothing leaves your environment unless explicitly configured to.

👥 Team & Culture

We are a team of engineers, ML practitioners, and builders, working across France, Singapore, and the Middle East.

Our culture is simple:
build systems that solve real problems, run reliably, and respect users’ data.

---