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Meet Jean-Pierre & Michelle.
AI That Actually Works.

Two purpose-built agents that automate project reporting and analytics intelligence.
They connect to your tools, learn your domain, validate every answer, and get measurably smarter every week.
100% on your machine. Zero cloud dependency. Free pilot.

Savings Privacy Platforms Cost

AgentOS — AI Command Center Dashboard

25+
hrs/week saved per team
3 sec
from question to verified answer
9
anti-hallucination layers
$0
pilot cost — measurable ROI first

What They Automate For You

Jean-Pierre

🎩 Jean-Pierre

Project Reporting Automation

Connects to GitHub, Jira, and Slack — turns chaotic project data into a real-time command center with proactive risk scoring and 24/7 background monitoring.

Status reports generated automatically — no manual gathering
Risks detected 2-3 weeks early via real-time scoring (0-100)
Executive reports in one click — CTO / CFO / PMO variants
All projects ranked by risk across your portfolio
Evolution Memory — learns your preferences, never re-explain
~10 hrs/week saved per project manager
Explore Jean-Pierre →
Michelle

🔬 Michelle

Analytics Intelligence

Connects to your databases — anyone can ask data questions in plain English with verified, source-cited answers backed by a 9-layer anti-hallucination architecture.

Plain English → verified results in 3 seconds
Exact SQL + source tables shown — full auditability
Learns from corrections — never makes the same mistake twice
Shared Brain — knowledge retained, governed via PR review
KPI engine with cron schedules, thresholds, sparklines
~15 hrs/week freed per data team
Explore Michelle →

See the Results

🎩 Jean-Pierre

Project Reporting Automation

From chaotic project data → real-time command center with proactive risk scoring, fleet intelligence, and executive-ready reports.

📊Living Dashboard — 24 bento cards, always current
🚨Risk Scoring — 0-100 scale with 2-3 week early warning
📋Fleet View — all projects ranked by risk
📝One-Click Reports — CTO, CFO, PMO variants
🧠Evolution Memory — learns your style, never re-explain
🔬 Michelle

Analytics Intelligence

From ad-hoc SQL requests → self-service analytics with verified answers, interactive dashboards, and automated KPI tracking.

💬Plain English → verified SQL in 3 seconds
📈9 Chart Types — pins, gauges, sparklines, drill-downs
🎯KPI Engine — cron schedules, thresholds, automated alerts
🔍Source Citations — clickable provenance on every number
🧠Shared Brain — team knowledge retained, governed via PRs

Why They Don't Hallucinate

🧬

They Evolve

Generic AI resets every session. These agents get measurably smarter every week.

6-namespace Evolution Memory — errors, corrections, tool wisdom, user adaptation, performance, knowledge
Same question, better answer next week — tracked and verifiable
Shared Brain: GitHub-backed, PR-reviewed → when your analyst leaves, knowledge stays

They Validate

Every answer comes with proof — exact SQL, source tables, execution evidence.

9-layer anti-hallucination architecture: Triage → Generate → Execute → Learn
Clickable [source:N] provenance badges on every data answer
Business glossary + rules enforced architecturally — not just in prompts
🔒

They Stay Private

100% on your machine. Nothing — not data, not queries, not memory — leaves.

Single binary, no Docker, no cloud — runs locally on macOS, Linux, Windows
Supports Ollama for fully local AI — zero API calls possible
GDPR-native, air-gap ready, no telemetry, no tracking

9 Layers of Anti-Hallucination

Anti-Hallucination Pipeline

Text-to-SQL tools generate queries. Michelle generates queries, validates them, learns from corrections, shares knowledge across your team, and gets measurably more accurate every day.

🧠
Triage
Can it be answered from context?
Generate
SQL with full schema context
▶️
Execute
Run with provenance tracking
📈
Learn
Record into evolution memory
Adaptive Schema — Tiered injection handles 100+ tables
Business Glossary — Your definitions, injected per prompt
Business Rules — "Exclude deleted" enforced architecturally
Validated Examples — Approved NL→SQL as few-shot training
Evolution Memory — 6 namespaces that persist across sessions
Semantic Joins — LLM-assisted FK discovery beyond constraints
Provenance Tags — Clickable [source:N] citations on every answer
Test Harness — Validate accuracy before deploying to team
Multi-Model Routing — Right model per task: SQL, Q&A, enrichment

"But I Already Have Claude…"

Claude is brilliant at conversation. But conversation isn't operations. Here's what changes when you move from a generic AI chatbot to purpose-built operational agents.

Capability Claude / ChatGPT + MCP Jean-Pierre & Michelle
Schema knowledge ⚠️ MCP can fetch — no tiered caching ✅ Auto-synced with adaptive tiered injection
Persistent memory ❌ Session resets — SKILLS.md is static ✅ 6-namespace Evolution Memory
Query validation ❌ You validate manually ✅ Triage → Generate → Execute → Learn
KPI tracking ❌ No persistence, schedules, thresholds ✅ KPI engine with cron, history, alerts
Team knowledge ❌ Each user starts from zero ✅ GitHub-backed Shared Brain + PR review
Data sovereignty ❌ Data goes to Anthropic / OpenAI ✅ Local binary, air-gap capable
Interactive dashboards ❌ Text + static artifacts ✅ Charts, pins, KPI gauges, drill-downs
Proactive monitoring ❌ Reactive — you ask, it answers ✅ Background daemon with risk scoring
Business glossary ⚠️ Static SKILLS.md — no enforcement ✅ Dynamic glossary + rules, enforced per query
Improves over time ❌ Same accuracy week 1 and week 52 ✅ Measurably smarter every week

The Bottom Line

Claude is a brilliant conversationalist. Jean-Pierre and Michelle are purpose-built operational systems — they live on your machine, learn your domain, validate every answer, and get smarter every week. One is a tool. The other is a teammate.


The Time You're Losing Today

Every week, your team loses 25+ hours to manual reporting, ad-hoc data requests, and knowledge gaps. Here's where the time goes.

PMs: 45 min of chaos every morning

Open Jira → Slack → GitHub → Spreadsheet. Context-switching just to answer "where do things stand?" — and the data is already stale.

Data teams: buried in lookups

15+ ad-hoc SQL requests/week, each 30-60 min. That's 1-2 analysts doing glorified lookups instead of real analysis.

Knowledge walks out the door

Your best analyst leaves → metric definitions, query patterns, business context: all gone. New hires spend 3 weeks just learning what "active customer" means.

AI chatbots hallucinate numbers

You tried ChatGPT / Claude for analytics. It invents data, forgets corrections next session, and every team member starts from zero. The issue isn't AI. It's stateless AI.


The Time You Get Back

Side-by-side: what your team does manually today vs. what the agents automate.

Before
~3 hrs/week
Status gathering
After
10 sec
Automated
Before
~4 hrs/week
Meeting prep + reports
After
One click
Instant generation
Before
30-60 min each, 15/week
Ad-hoc data requests
After
3 sec
Self-service
Before
3 weeks onboarding
New hire learns definitions
After
Instant
Shared Brain has it all
~25 hrs/week saved
$110,000+ in annual productivity gains
Plus: risks caught 3 weeks early · institutional knowledge retained · eliminated team dependencies · measurably improving accuracy

How the Free Pilot Works

Zero risk. We automate one workflow in 2-3 weeks — you measure the results before spending anything.

1

Pick One Workflow

Which reporting workflow wastes the most time? Weekly status? Ad-hoc data requests? Executive reports? We start with the one that hurts most.

2

We Automate It

In 2-3 weeks, we deploy the agent on your machine, connect it to your tools, configure your Shared Brain, and automate the workflow end-to-end.

3

You Measure Results

No cost. No commitment. You measure real time saved, accuracy scores, and evolution learning rate. If it works — we expand. If not — zero money wasted.


Your Data Stays on Your Machine

100% Local Data Privacy
🔒 Nothing leaves. Ever.
❌ Never
Project Data (Commits, PRs, Tickets)
❌ Never
Database Queries & Results
❌ Never
Chat Conversations
❌ Never
Agent Memory & Knowledge
❌ Never
API Keys & Credentials
Using **Ollama**? Even your AI conversations stay 100% on your machine. Zero cloud, zero tracking. [Learn more about our security model →](security.md)

Built For Your Role

🏗️

CTOs & VP Engineering

Portfolio risk visibility → proactive decisions, not fire drills

👔

Engineering Managers

Automate status gathering → real-time project health

📊

Data & Analytics Leaders

Eliminate ad-hoc request backlog → self-service in 3 sec

📋

Technical PMs

3 report variants → one click, CTO/CFO/PMO in seconds


Free AI Automation Pilot

No Cost. No Commitment. Just Results.

We pick ONE workflow — the one that wastes the most time — and automate it in 2-3 weeks. You see a real, working result before spending anything. Our only ask: honest feedback.

✅ Full deployment on your machine
✅ Connected to your tools — GitHub, Jira, Slack, or databases
✅ Shared Brain configured with your team's KPIs & glossary
✅ Measurable results — time saved, accuracy score, evolution rate
✅ No cost, no commitment — zero risk

📧 info@unicolab.ai · 💬 DM us on LinkedIn · 🌐 unicolab.ai


For Technical Teams: Under the Hood

The AgentOS Platform

AgentOS is a composable, local-first AI agent engine built in Go 1.23 with a React 19 frontend. Each copilot is a specialized "pack" — a domain configuration that includes tools, dashboards, AI persona, and workflows.

Architecture:

┌─────────────────────────────────────────────────────┐
│                   AgentOS Engine                     │
│                                                      │
│  ┌──────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ Sidebar  │  │   Dashboard  │  │   Copilot    │  │
│  │          │  │   (Bento)    │  │   (Chat)     │  │
│  │ Modules  │  │   24 cards   │  │  Streaming   │  │
│  │ per pack │  │  per pack    │  │  + Tools     │  │
│  └──────────┘  └──────────────┘  └──────────────┘  │
│                                                      │
│  ┌────────────────────────────────────────────────┐  │
│  │         Intelligence Layer (Go)                │  │
│  │  Adaptive Memory · Knowledge Graph · Steering │  │
│  │  Shared Brain · Error Patterns · Tool Wisdom  │  │
│  │  Performance Tracker · User Adaptation         │  │
│  └────────────────────────────────────────────────┘  │
│                                                      │
│  ┌────────────────────────────────────────────────┐  │
│  │          Tool Gateway                          │  │
│  │  GitHub · Jira · Slack · Database · Shell     │  │
│  │  Gmail · Calendar · Docs · HTTP · Web Search  │  │
│  └────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────┘

Available Packs:

Pack Persona Domain Status
aiflow-pm 🎩 Jean-Pierre Project Management ✅ Production
michelle 🔬 Michelle Analytics Intelligence ✅ Production
edith 🥐 Édith Sales Intelligence / CRM ✅ Available
freelancer 💼 Yvette Freelancer PM ✅ Available
office 🏢 Office Productivity ✅ Available
retail-ops 🏪 Retail Operations ✅ Available

AI Providers:

Provider Type Cost
Ollama 100% local Free
OpenAI Cloud API Per-token
Anthropic Cloud API Per-token
Google Gemini Cloud API Per-token

Tech Stack: Go 1.23 · React 19 · TypeScript · Vite 7 · SQLite · SSE Streaming · Glassmorphism UI

Quick Install:

curl -fsSL https://unicolab.github.io/agentos/install.sh | sh
agentos serve
# Open http://localhost:18080

Full Documentation → API Reference →


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AI automation consulting — Paris, France
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