"We can see the information. We just cannot see it together." That idea came up repeatedly in our early-access conversations. The teams we spoke with were already collecting imagery, inspection notes, sensor readings, maintenance records, and other operational data. Their problem was not a lack of information. It was the work required to connect the information before they could make a decision.
We asked early-access users to walk us through how they monitor asset portfolios today, including what happens before an inspection, after an inspection, and when something changes unexpectedly. We heard different versions of the same three themes: too many disconnected tools, too much time spent correlating data manually, and no dependable answer for what happened between inspection visits.
These conversations shaped how we think about monitoring. They also gave us a useful boundary. The teams we spoke with were not asking us to replace every system they already used. They were asking for a clearer way to bring the relevant information into one view and understand what changed over time.
Too many disconnected tools
Most of the teams we spoke with had built their workflows around several tools, each serving a reasonable purpose. One system held asset records. Another stored inspection findings. Satellite imagery lived somewhere else. Sensor data arrived through a separate dashboard, while spreadsheets and email filled the gaps between formal systems.
In practice, a monitoring question often became a search exercise. An operations team might notice a change in a site and then open an asset database to confirm the location, a document repository to find the last inspection, an imagery platform to compare conditions, and a spreadsheet to check whether maintenance had already been scheduled. The team could eventually assemble the context, but only after moving between tools that were not designed to work as one workflow.
We heard this described less as a complaint about any single product and more as a problem with the space between products. Every system on the stack was doing its job. The difficulty was that the team had to do the connecting, usually by hand.
The cost of manual correlation
The second pattern was the amount of time spent turning separate observations into a usable account of what happened. Users described matching locations, dates, asset identifiers, images, and notes before they could decide whether a change was meaningful or required action.
Consider a team monitoring a group of distributed facilities. A new image may show a changed surface condition at one location. Before escalating it, someone may need to confirm that the image covers the correct asset, compare it with an earlier image, review the latest inspection report, check recent sensor readings, and look for an open maintenance ticket. If those records use slightly different names or date ranges, the review takes longer. If the person who knows the history is unavailable, the process can slow further.
This was not usually a story about difficult analysis. It was a story about preparation. The team knew what it wanted to compare, but the comparison required repeated downloads, filters, screenshots, spreadsheet updates, and conversations with colleagues who had seen an earlier part of the record.
One operations lead put it this way: the team was spending too much of its day proving that two pieces of information belonged to the same place and time. That work is necessary for a reliable decision, but it does not create much additional understanding. It is the administrative cost of assembling the evidence.
What happened between inspection visits?
The third theme was the gap between scheduled inspections. A site might receive careful attention during a visit, then go weeks or months before the next planned review. When a later inspection found a problem, teams often had limited visibility into when the condition first appeared or how it developed.
We heard a common scenario: an inspector arrives at an asset and documents damage or a changed condition. The team then looks backward through the available records to determine whether the issue was present in an earlier visit, whether imagery showed an earlier signal, or whether sensor data suggested a change between the two inspections. Sometimes the evidence exists. Finding it and placing it in sequence can still take substantial work.
The gap-between-visits problem is not simply a data problem. It is a visibility problem. A scheduled inspection provides a point-in-time account. It does not automatically explain the period before the visit or show the moment when a condition began to change.
We did not hear users asking for more scheduled inspections as the only answer. They wanted a better way to understand the time between them, so that an inspection could begin with context rather than start the investigation from scratch.
What users did not ask for
One of the more useful parts of these conversations was what we did not hear. Nobody asked for another destination for a larger pile of raw data. Nobody described a desire to open more dashboards or manually reconcile more feeds. The request was usually the opposite: less searching, fewer handoffs, and a clearer relationship between an asset, its history, and its current condition.
We also did not hear that existing tools were universally failing. Teams had developed practical workarounds, and those workarounds often reflected real operational knowledge. Spreadsheets contained decisions that were not recorded elsewhere. Inspection notes carried context that a structured field could miss. Email threads sometimes explained why a team chose one action over another.
That nuance mattered to us. The answer could not depend on pretending that current systems had no value. It had to help teams work across the systems and records they already rely on, while making the combined picture easier to follow.
How those conversations shaped Larx
Larx is our answer to the three patterns we kept hearing. For disconnected tools, we built a unified spatial workspace that brings asset context and related information into one place. For the time spent correlating records manually, we built a single indexed stream that organizes observations and events so teams can review them together. For the gap between inspections, Larx supports continuous monitoring using satellite and sensor data.
These capabilities came directly from listening to teams explain where their current workflows slow down. We did not start with the assumption that monitoring needed another isolated application. We started with the observation that teams were already doing the work, but much of it happened in the spaces between their tools, records, and scheduled visits.
Our honest takeaway is that monitoring teams do not need to be told that their assets are changing. They need a practical way to see what changed, place it in context, and understand what happened before the next inspection. That is the problem we set out to address with Larx.