Decroo  /  Strategic Intelligence
Live Prototype
Confidential — PE Investment Thesis

The Tech-Enabled Buyout
of a Critical Infrastructure Operator

AeroNode Integrators runs 24/7 aviation-grade mechanical systems for major UK airports. The business is defensible, recurring, and deeply specialized. It is also trapped in a linear labor model. We are here to change that.

£1.2M
Current EBITDA
3–5×
Target Multiple Expansion
4
Agentic Use Cases Identified

AeroNode's moat is real: certified control engineers, terminal relationships, and zero-downtime SLAs that competitors cannot replicate overnight. But every new terminal contract requires another expensive hire. Our thesis is that Agentic AI can decouple revenue growth from headcount—compressing weeks of senior engineering work into minutes, and transforming the unit economics of the business before a structured exit.

Operational Leverage
Four Bottlenecks. Four Agents.
01 / PRE-SALES
Generative Estimation
Vision Model → BOM + Labor Quote
Ingests 500-page mechanical PDFs and autonomously produces Bills of Materials and costed labor schedules—replacing weeks of manual quantity surveying.
Phase 2
02 / DATA NORMALIZATION
Semantic Tagging Engine
LLM → Ontology Mapping
Translates thousands of cryptic legacy data points into a standardized aviation ontology—with human review reserved for ambiguous edge cases only.
Phase 1 — Today's Demo
03 / QUALITY ASSURANCE
Automated Commissioning
Agent → Physical Equipment Test
Deploys autonomously during off-peak windows to validate gate-level equipment performance—no engineer on-site required for routine sign-off.
Phase 2
04 / FIELD SERVICE
Predictive Dispatch
Alarm Intercept → Root Cause → Pre-Order
Intercepts live system alarms, diagnoses probable failure modes, and pre-orders parts before a technician rolls a truck—compressing mean time to resolution.
Phase 3
Phase 1 Focus — Maximum Time-to-Value

Today: The AeroGraph Data Refinery

When AeroNode takes over an existing terminal, they inherit thousands of cryptic, undocumented data tags— T3_ZN4_AC2_VLV—with no reference documentation. Today, a £85k/year senior engineer spends up to three weeks mapping these points manually before any modern software can read the building.

AeroGraph ingests the raw tag exports and architectural blueprints simultaneously, applies spatial context reasoning to map every point to our proprietary aviation ontology, and surfaces only the genuinely ambiguous edge cases for a thirty-second human review.

15 min
End-to-end tag normalization vs. 3 weeks of senior engineering time
Environment initialised  ·  Prototype ready