Technology / IoT & Multi-Source Data
We connect sensors, machines, third-party feeds and operational data into AI-ready systems — for industrial production, infrastructure, and intelligence work.
Most factories are digital. Almost none are intelligent.
The sensors are there. The PLCs are wired. There are dashboards in three different rooms with three different colours. There are spreadsheets nobody opens, telemetry no one reviews, and quality issues that get diagnosed two shifts late by someone walking the line.
The same is true outside manufacturing. Sites, fleets, infrastructure, intelligence operations: a lot of digital, very little intelligence about what the digital is actually telling you.
We help organisations make the second jump — from instrumented to self-aware.
The work is mostly the same shape across industries. The signals change. The discipline does not.
Sensors, PLCs, SCADA, ERP, third-party feeds, market data, public datasets — get the wiring right before anyone draws a model. Most AI projects in industry stall at this step. We treat it as the project.
A vibration reading is not a temperature reading. A floor sensor is not a market feed. Each source is typed, sourced, time-stamped, and given a confidence — the same Knowledge Object discipline as the rest of the stack.
Multi-source intelligence is not a stack of tables. It is a graph: sensors that confirm each other, anomalies that ladder into events, signals that should age out, signals that should escalate. We build the graph and we keep it honest.
Output is structured for both humans and downstream agents. The shop-floor lead sees what they need to act. The agentic layer sees a clean Knowledge Object stream. Same source of truth, two interfaces.
Digitalisation moves processes onto screens. Intelligence makes processes self-aware: they know where they are, what they should be doing, where they are slipping, and who needs to know. The goal is the second one.
Industrial floors, water infrastructure, intelligence work. Same architecture, different signals.
Production-line agent
Tauran maps existing production lines and turns them into AI-powered operational tools. It builds AI-ready dashboards that make industries AI-ready — production control, quality control, machine monitoring, execution telemetry — and exposes the resulting signal as a structured stream the rest of the stack can act on. The aim is not just better visibility. The aim is a production system that knows about itself.
Industrial sensor integration
Sensors embedded in dam gates and water-management infrastructure, connected through Poseidon Connect and Floodlink. Real-time telemetry fused with environmental and operational context — turning isolated readings into early-warning signals and decision-ready alerts. A small example of the same architecture: connect, type, fuse, surface.
Client engagements
Bridging operational technology (sensors, PLCs, SCADA, MES) with information technology (ERP, CRM, knowledge bases) and third-party feeds. We design the integration, define the schemas, and build the layer that lets AI act over the resulting data without losing the context the data came in with.
Intelligence & research
For intelligence and research clients: fusing OSINT, telemetry, structured datasets and expert testimony into a single auditable analytical surface. Same architecture as the industrial work, applied to a different kind of signal.
A reading from a sensor on a production line is not just a number. It comes in typed, sourced, time-stamped, with provenance — the same way every other Knowledge Object on the stack is treated.
sensor_event.py
from dataclasses import dataclass
from datetime import datetime
from typing import Literal
SignalKind = Literal[
"vibration", "temperature", "pressure",
"flow", "level", "current", "event",
]
@dataclass(frozen=True)
class SensorEvent:
id: str
source_id: str # device or feed
kind: SignalKind
value: float
unit: str # SI, validated
timestamp: datetime
# context — never optional
asset_id: str # machine, gate, line
site_id: str
confidence: float # device-reported
# fusion graph relationships
confirmed_by: list[str] # other event ids
contradicts: list[str]
escalates_to: list[str] # event types it triggersline_query.py
# Tauran exposes the line as a queryable surface. line = tauran.line(id="extrusion-A2") line.state(at=clock.now()) # current operating mode, throughput, anomalies line.quality(window="last_8h") # defect rate trend, root-cause clusters line.bottlenecks() # inferred from telemetry + KO graph line.escalations(severity=">=warning") # events the system thinks a human should see line.optimisation_hints() # where the math says throughput could move
More control over what is happening on the floor and in the field, in near real time.
More data, but typed, sourced and de-duplicated — not just more dashboards.
More insight on where to optimise production, energy, throughput, quality.
A bridge between sensors, machines, sites and the rest of the AI stack.
An organisation that is not only digital, but actually intelligent about itself.
If you have sensors, machines, or third-party feeds that should be contributing to decisions and aren't, we run targeted IoT and multi-source data audits. From there we design the integration and the AI layer above it.