Data fusion
Satellite data, IoT sensors, weather, and your own records, connected via API and MCP. The system reads your sources where they live, with no forced migrations or manual downloads.
Decision support systems
A modern DSS does more than show data: it recommends. We build systems that fuse satellite data, sensors, and your own sources, model the decision, and return an actionable recommendation, with a human in charge and auditable decisions.
decision intelligence · first Gartner Magic Quadrant in 2026
Beyond the dashboard
The industry made it formal: in 2026 Gartner published its first Magic Quadrant for decision intelligence platforms, and it expects that by 2027 half of business decisions will be augmented or automated by AI agents. The "static dashboard" is now the counter-model everyone cites. A modern DSS has three pieces:
Reasoning layer
Models (and, where they help, LLMs and agents) that turn data into an actionable recommendation: what to do, when, and with what evidence behind it.
Decision governance
A human in the loop, explicit criteria, and auditable decisions. Autonomy is earned in stages, with clear criteria from day one.
The pattern already working in Europe
We start from public operational systems that apply exactly this pattern: source fusion, a model, and an actionable alert. We build the same thing at the scale of your company:
Flood early warning
The European flood warning systems fuse satellite data, hydrological models, and stations to alert authorities in advance: the canonical example of an operational environmental DSS.
GloFAS: forecasts up to 30 days
Ground motion (EGMS)
The European Ground Motion Service uses Sentinel-1 InSAR to watch dams, bridges, railways, and buildings across Europe: the base of a DSS for infrastructure maintenance.
millimeter accuracy · annual update
Digital twins (DestinE)
Destination Earth enters its operational phase in 2026, with more than 5,400 users. In agriculture, digital twins are moving from papers to first deployments: we treat them as an early-stage capability.
Phase 3: operational transition (07-2026)
From recommending to executing, in stages
The industry is heading to the closed loop: systems that not only recommend but also trigger actions. Trust, however, runs behind: two thirds of organizations name security and risk as the main barrier to scaling autonomy (McKinsey, 2026). Our position is the one the evidence supports: governed autonomy, with a human in the loop.
We start with recommendations backed by evidence. Automatic execution comes only where the criteria are explicit, the decisions are audited, and you set the limit.
THE FLOW, IN SHORT
- 01 · The decision: what is decided, by whom, and how often.
- 02 · The sources: satellite, sensors, and your systems, via API and MCP.
- 03 · The decision model, evaluated before it is trusted.
- 04 · Actionable recommendation and, where it fits, governed execution.
Where we have done it before
The four most recent projects by the team that today forms T3 AISAT are, at their core, decision support systems:
TELENITRO
Real-time nitrogen management DSS for Mediterranean horticulture (PRIMA program).
LINOMAR
Crop and microalgae production DSS for biostimulants (EAFRD, Mar Menor and La Albufera).
Olive Carbon Balance
Remote sensing studies and carbon balance DSS for olive groves (Spanish operational group).
MICROMAR
DSS and satellite monitoring of vegetable crops with biostimulants (Biodiversity Foundation).
Which decision in your operation still depends on a glance at a panel?
Tell us which one. We will design the system that recommends it with data, explicit criteria, and human control.
Talk to the team