Product Overview · May 2026

MAOS

The operating system for enterprise data.
Every tool. One query. Deterministic answers.

Modular Agent Operating System
Confidential — Do Not Distribute
The Problem

Your data is trapped

💳
📊
🎫
📈
🗄️
💼
📋
🔧
☁️
📦
130+ Average SaaS apps per enterprise — each a walled garden
45% Of analyst time spent on data preparation, not analysis
£3.1T Lost annually to poor data integration across industries
Why Not Just RAG?

AI chatbots hallucinate on business data

RAG / Chatbot
"What's our Stripe MRR vs last quarter?"
"Based on available documentation, revenue trends depend on multiple factors including churn rate and seasonal patterns. For precise figures, consult your finance dashboard."
No live data accessed
Vague, hedging language
Tells you to check another tool
Non-deterministic output
MAOS
"What's our Stripe MRR vs last quarter?"
"Current MRR is £84,200 (up 12.3% from £74,960 last quarter). 847 active subscriptions. Growth driven by Professional tier upgrades (+23%). Net revenue retention: 118%."
Queried Stripe API in real time
Ran SQL for historical comparison
Exact figures, fully sourced
Deterministic & reproducible
How We Compare

Every approach tried. Only one works.

Capability Plain RAG Agentic RAG Docs in Prompt Fine-tuned LLM MAOS
Live data access ✕ None Via tool use ✕ None ✕ None ✓ All sources
Cross-source queries ✕ No Possible, brittle ✕ No ✕ No ✓ Native fan-out
Hallucination risk Reduced, still present Compounds per hop Lower, context-limited Lower, not eliminated ✓ None in core mode
Deterministic output ✕ No ✕ No ✕ No ✕ No ✓ Yes
Confidence scoring ✕ None ✕ None ✕ None ✕ None ✓ Weighted
Contradiction detection ✕ None ✕ None ✕ None ✕ None ✓ Automatic
Latency 2–8s 5–30s 3–10s 1–5s ✓ <1s typical
Cost per query Token-based High (multi-call) Very high Token-based ✓ Fixed, no tokens
LLM dependency Required Required Required Required ✓ Optional enhance
📋
RAG reduces hallucination
Retrieves relevant docs, but the LLM still interprets and can misrepresent. No live data.
🤖
Agentic RAG adds tool use
Can call APIs, but each LLM hop adds latency, cost, and compounding error risk.
MAOS skips the guesswork
Queries the actual API, runs the actual SQL. Structured data in, structured answer out. LLM optional.
The Solution

One query. Every source. Real answers.

Query Natural language
Classify Rule engine
Execute Parallel fan-out
Aggregate Merge + verify
Answer Deterministic
AI without hallucinations Cross-source queries Contradiction detection Sub-second latency Confidence scoring
Ecosystem

13 connectors + 6 databases

Every connector ships with demo mode — zero API keys needed to evaluate.

💳Stripe
🐙GitHub
🟠HubSpot
📊Mixpanel
☁️Salesforce
🎫Zendesk
📝Notion
📋Jira
📈Amplitude
📉GA4
☸️Kubernetes
🔶AWS
🔥Prometheus
🗄️+ 6 DBs
+∞

PostgreSQL · MySQL · Snowflake · BigQuery · Databricks · AlasQL

Live Pipeline

Watch a query flow through

maos-pipeline
Compare Stripe revenue with HubSpot pipeline
┌ Classifyintent: comparison (2ms)
├ Route[stripe, hubspot] parallel fan-out
├ Stripe✓ 3 records (142ms)
├ HubSpot✓ 5 records (89ms)
├ Aggregateconfidence: 94% 0 contradictions
└ ✓ Stripe MRR £84.2K (+12%). HubSpot pipeline £340K across 23 deals. 246ms
246ms
Total latency
2
Live sources
94%
Confidence
0
Hallucinations
Market Opportunity

A £54B enterprise problem

£54B
TAM
Enterprise AI + Data Integration market by 2028
£14.8B
SAM
AI-powered data integration & BI platforms
£680M
SOM
Deterministic orchestration for mid-market & enterprise

Every company with 5+ SaaS tools is a potential customer.

Engineering Traction

Production-ready today

192
Tests passing
13
SaaS connectors
6
Database adapters
0
Hallucinations
Enterprise-grade codebase 192 automated tests Real-time dashboard JSON logging Plugin architecture Demo + Live modes

Stop asking chatbots.
Start querying your data.

MAOS turns every enterprise tool into a queryable, deterministic API.
One runtime. Every source. Real answers in milliseconds.

Contact
steven.fernandez@neurasoft.ltd
MAOS — Confidential · May 2026