You will get a custom Claude AI agent built, deployed and documented
Project details
An AI agent is only useful if it does real work, reliably, without you babysitting it. That's the standard I build to.
I design and ship agents that take a goal, use your tools and APIs, handle errors, and come back with a result: processing documents, triaging and drafting communications, extracting and filing data, answering questions from your knowledge base (RAG), automating multi-step workflows.
How I work:
• Approval gates by default: no outbound action (email, submission, payment) fires without your sign-off
• Deterministic where it should be, agentic where it must be. Not everything needs an LLM
• Model-agnostic: Claude first, swappable by config, never locked in
• Written documentation: what it does, how to run it, how to change it
• Already have an agent that never reached production? I audit it first: what's salvageable, what should be rebuilt
Proof: I designed and run Plaidoria, a legal AI SaaS in production where a multi-agent pipeline drafts court documents and verifies every citation against official sources. Zero tolerance for silent failure.
Tell me what the agent should do, and I'll confirm scope, price and timeline before we start.
I design and ship agents that take a goal, use your tools and APIs, handle errors, and come back with a result: processing documents, triaging and drafting communications, extracting and filing data, answering questions from your knowledge base (RAG), automating multi-step workflows.
How I work:
• Approval gates by default: no outbound action (email, submission, payment) fires without your sign-off
• Deterministic where it should be, agentic where it must be. Not everything needs an LLM
• Model-agnostic: Claude first, swappable by config, never locked in
• Written documentation: what it does, how to run it, how to change it
• Already have an agent that never reached production? I audit it first: what's salvageable, what should be rebuilt
Proof: I designed and run Plaidoria, a legal AI SaaS in production where a multi-agent pipeline drafts court documents and verifies every citation against official sources. Zero tolerance for silent failure.
Tell me what the agent should do, and I'll confirm scope, price and timeline before we start.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging FaceAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$495
|
Standard
$950
|
Advanced
$1,900
|
|---|---|---|---|
| Delivery Time | 7 days | 14 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 - 5:41 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 (written)
I review your answers, ask clarifying questions in writing, and confirm scope, price and timeline before any code is written.
Architecture & build
I design the agent (tools, approval gates, error handling, model-agnostic setup) and build it in your repository, with your API keys. Deterministic where it should be, agentic where it must be.