Sophia Antipolis | Engineering-first | GDPR-native

Applied AI. Integrated into real workflows.

I design and implement AI systems that reduce operational friction — not demos, not experiments, but working processes embedded in real environments.

View Applied Projects Email me →

Document Intelligence · Workflow Automation · Controlled AI Systems

agent_audit_trace.log
10:42:01 input_received source="RFP_Project_Alpha.pdf" size=4.2mb
10:42:02 initializing_context mode=retrieval_augmented
10:42:04 Data boundary: No external egress (local execution)
10:42:05 risk_detected: clause=3.4 • type=Ambiguous SLA • severity=HIGH
"Vendor shall provide reasonable support."
-> Flagged for Legal review.
10:42:06

Built on rigorous standards

Mistral AI LangGraph Docker Python Azure n8n

Modes of Engagement

Two ways to apply AI to complex workflows — depending on your stage.

Engineering Service

Studio

Design and implementation of AI-assisted workflows. Workflow analysis → architecture design → controlled deployment. Focus on operational clarity, reproducibility, and measurable impact.

Workflow mapping & system design
AI-assisted process implementation
Guardrails & traceability
Production deployment support

Structured scope · Measurable outcomes · Working systems

Strategic Guidance

Advisory

Strategic guidance for teams integrating AI internally. Focus on feasibility, governance, and long-term maintainability.

Workflow feasibility evaluation
Architecture & responsibility boundaries
Risk and governance design
Team enablement

Architecture-first · Risk-aware · Sustainable systems

Deeply integrated with your stack.

We don't replace your tools. We orchestrate them.

J
Jira / Linear
G
GitHub / GitLab
S
Slack / Teams
N
Notion / Wiki
P
Postgres / SQL
G
Google / O365
API
Custom Webhooks
P
PDF / Docx

Applied Projects

Selected implementations translating workflow architecture into operational systems.

E-commerce Infrastructure

StikrAI

AI-driven commerce platform converting prompts into physical merchandise.

Problem

Fragmented infrastructure between concept and production.

Approach

AI generation → structured product pipelines → fulfillment automation.

Live product
AI Generation Webhooks Stripe
Infrastructure Layer

MCP Gateway

Infrastructure layer enabling AI systems to access external tools safely.

Problem

AI systems isolated from execution environments.

Approach

Validation layers, permission boundaries, traceable execution.

Active development
Protocol Design Security Monitoring
Document Intelligence

clarify.run

Document intelligence system extracting structured knowledge from complex documents.

Problem

Critical information locked in unstructured formats.

Approach

AI analysis → structured extraction → validated outputs → workflow triggers.

Pilot-stage
NLP Extraction Validation

Building Direction

Focus areas shaping the next phase of applied AI systems.

Document Intelligence

Extracting structured knowledge from unstructured documents and triggering downstream workflows.

-> clarify.run

Agent Infrastructure

Building safe interfaces between AI systems and external tools with validation and monitoring.

-> MCP Gateway

Workflow Automation

Connecting AI generation to operational pipelines with quality gates and fulfillment systems.

-> StikrAI

Control & Observability

Measure impact, reduce risk, keep humans in the loop.

Implementing guardrails, audit trails, and traceability layers for deterministic AI operations.

-> Cross-project pattern

Core Principles

How I approach building reliable AI systems inside real organizations.

01

Ground Truth First

Systems are constrained to existing data and documented schema. No fabrication. When uncertainty exists, we surface ambiguity alerts rather than generating assumptions.

02

Controlled Automation

Automation must be measurable, observable, and reversible. Every action generates an audit trail. Every decision can be traced back to its inputs and reasoning path.

03

Human Oversight

AI systems operate within guardrails and clear responsibility boundaries. Critical decisions require human approval. Escalation paths are explicit, not emergent.

04

Operational Impact

The goal is reduced friction and improved decision speed — not technical novelty. We measure success by time saved, risks caught, and operational confidence gained.

Deployment & Security Specs

LLM Inference
Cloud Pilot: OpenAI / Anthropic (EU)
On-Premise / VPC: Local (Llama 3 / Mistral)
Data Storage
Cloud Pilot: Managed Vector DB
On-Premise / VPC: Self-hosted Qdrant/Pgvector
Connectivity
Cloud Pilot: Public API
On-Premise / VPC: Private VPC / VPN
Audit Logs
Cloud Pilot: 7 Days Retention
On-Premise / VPC: Unlimited / Export to SIEM

Engineering FAQ

How do you prevent hallucinations in technical specs?
We use a "Ground Truth First" architecture. The agent is explicitly forbidden from generating new facts. It only extracts and compares data against your existing documentation or schema. If it's unsure, it flags an "Ambiguity Alert" rather than guessing.
Does our code or data leave our infrastructure?
For the On-Premise tier, absolutely not. We deploy the entire stack (LLM, Vector DB, Orchestration) on your servers or private cloud (Azure/AWS VPC). We build on open-source models to ensure zero dependency on external APIs.
What is delivered in the 2-Week Sprint?
Day 1-3: Analysis of one specific painful workflow (e.g., RFP analysis).
Day 4-8: Development of a custom LangGraph agent connected to your data.
Day 9-10: Deployment, testing, and handover of the "Risk Report" and source code.
What is the difference between AI Agents and traditional RPA?
Traditional RPA follows rigid, rule-based scripts that break when inputs change. AI Agents use Large Language Models to understand context, handle variations, and make judgment calls. They can process unstructured data (documents, emails, logs) and adapt to edge cases that would crash RPA bots.
What is deterministic AI and why does it matter?
Deterministic AI produces consistent, predictable outputs for the same inputs. Unlike chatbots that can give different answers each time, our agents follow explicit state machines (using LangGraph) where every decision is traceable, auditable, and reproducible. This is critical for engineering workflows where mistakes are expensive.
Which LLM models do you use?
For Cloud Pilot deployments, we typically use GPT-4 Turbo or Claude 3 with EU data residency. For On-Premise deployments, we deploy open-source models like Llama 3.1 70B or Mistral Large 2. Model selection depends on your latency, accuracy, and compliance requirements.
How do you ensure AI outputs are trustworthy?
Three mechanisms: (1) Guardrails validate inputs and outputs against defined schemas. (2) Confidence scoring flags uncertain decisions for human review. (3) Audit logging records every state transition and LLM call, creating a complete trace for compliance and debugging.
What happens when the AI Agent is unsure?
Our agents include Human-in-the-Loop checkpoints. When confidence falls below a threshold (typically 85%), the agent pauses execution, presents its analysis with reasoning, and requests human confirmation before proceeding. This prevents silent failures and builds trust.
Can AI Agents integrate with our existing tools?
Yes. We build integration layers for Jira, Linear, GitHub, GitLab, Slack, Teams, Notion, Confluence, and custom APIs. Our agents don't replace your tools—they orchestrate them, enriching workflows without disrupting existing processes.
Is aixagent.io GDPR compliant?
Yes. We're based in Sophia Antipolis, France, and operate under EU jurisdiction. For Cloud Pilot, we use EU-based infrastructure with data residency guarantees. For On-Premise, all processing happens within your infrastructure with zero data egress.

We are opinionated.

We don’t build chatbots for chit-chat.
We don’t automate blindly without guardrails.
We don’t move fast and break production systems.

"Engineering decisions deserve engineering-grade AI."

Bring one document, incident, or release
you regret.

Start Assessment & Book Sprint

Technical screening required.