You will get an automated document pipeline: transcribe, classify, extract, file
Project details
Documents arrive constantly: emails with attachments, PDFs, reports, voice notes, meeting recordings. Someone has to read them, name them, file them and act on them. That someone doesn't have to be you.
I build document pipelines that run unattended:
• Speech-to-text: meetings and voice notes transcribed (Whisper-class models), summaries generated
• Classification: each document identified, typed and routed by its content
• Extraction: the fields you care about pulled into your spreadsheets or systems
• Filing: correct naming, right folder, every time, with an audit trail
• Scheduled or event-driven: runs on its own, fails safely, tells you when a human is needed
I've built this class of system in demanding environments: a production legal SaaS (Plaidoria) where every output is verified against its source, and document/transcription pipelines for a government administration under strict data-sovereignty constraints. I also step in on pipelines that were started and never shipped.
If your data is sensitive, everything can run on infrastructure you own. If your pipeline classifies or scores people, I'll flag upfront whether the EU AI Act applies.
I build document pipelines that run unattended:
• Speech-to-text: meetings and voice notes transcribed (Whisper-class models), summaries generated
• Classification: each document identified, typed and routed by its content
• Extraction: the fields you care about pulled into your spreadsheets or systems
• Filing: correct naming, right folder, every time, with an audit trail
• Scheduled or event-driven: runs on its own, fails safely, tells you when a human is needed
I've built this class of system in demanding environments: a production legal SaaS (Plaidoria) where every output is verified against its source, and document/transcription pipelines for a government administration under strict data-sovereignty constraints. I also step in on pipelines that were started and never shipped.
If your data is sensitive, everything can run on infrastructure you own. If your pipeline classifies or scores people, I'll flag upfront whether the EU AI Act applies.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, Automatic Speech Recognition, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Hugging FaceAI Models
ChatGPT, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$395
|
Standard
$850
|
Advanced
$1,600
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code |
Frequently asked questions
About Ridenn
AI Engineer | AI Agents, RAG & LLM Integration | Business Automation
Maurecourt, France - 11:17 pm local time
What I have shipped:
• Plaidoria (plaidoria.fr) — a legal-tech SaaS I built end to end and run in production: a multi-agent pipeline that anonymizes case data before any LLM call, retrieves French case law through official public APIs, drafts court-ready legal briefs, and verifies every citation against official sources. Zero-hallucination tolerance by design.
• For a French government administration: a sovereign queue-management application replacing a commercial product (deployed across multiple sites), a speech-to-text + automated meeting-report pipeline, a room-booking tool with built-in AI assistance, and on-prem LLM/RAG building blocks — all under strict data-sovereignty constraints.
• AI feasibility audits for decision-makers: scoping, risks, ROI, deployment plan.
How I work:
• AI-native delivery: I orchestrate state-of-the-art AI across the whole build cycle and review everything with an algorithmic eye — fast delivery, no loss of rigor.
• Proof over promises: clear scoping, measurable outcomes, real-world adoption.
• GDPR-aware by default: anonymization, data minimization, compliance built in.
Best fit: AI agents and automation builds, LLM/RAG integration (API or on-prem), AI audits and consulting. Async/written collaboration preferred — English or French.
Steps for completing your project
After purchasing the project, send requirements so Ridenn can start the project.
Delivery time starts when Ridenn receives requirements from you.
Ridenn works on your project following the steps below.
Revisions may occur after the delivery date.
Scoping on your real files
I review your answers and a few sample files, confirm the flows and the naming/filing rules in writing, and lock scope, price and timeline before building.
Build the pipeline
Ingestion, transcription (Whisper-class models) and/or classification, extraction, naming and filing rules, audit trail. Scheduled or event-driven, fails safely, flags what needs a human.