Sol Naciente is a research and development startup in information science and applied technology. We do not build mobility apps: we build autonomous decision algorithms, signal processing, and predictive analytics, and we use a set of live applications (Axis Helios Suite, Encuentrame, and BI Dashboard) as the ground where those systems run every day on real people's data and transactions.
That ground currently supports three lines of applied research, each with its own revenue.
In Axis Helios Suite, our fleet and logistics app, a dispatch agent consults Gemini on every new order to decide which driver should get priority. Every decision is written to an auditable log, and the system runs on three layers of resilience (model reasoning, local heuristic, base priority) so operations never stop if the external API fails. It is a system making a real business decision, with real money attached, every time a driver receives a new order.
In Encuentrame, our consumer-facing app, two more systems operate on the same principle. One sends video and audio from a safety incident to Gemini to generate a structured incident report in seconds. The other is our own earthquake detector: we process the accelerometer and gyroscope signal from each phone with a sustained-energy algorithm (STA/LTA, the same method used by professional seismic networks), filter the relevant frequency band, and require geographic correlation across several independent devices before treating an event as real, never a single phone's reading. Perceived intensity is calculated with the same empirical relation the US Geological Survey uses for its shake maps (Wald et al., 1999). The system runs today on real field sensors while it accumulates the events needed to calibrate its thresholds against empirical evidence, the same process any new seismic network follows before publishing results with a known margin of error.
Our third line, BI Dashboard, applies the same data analysis principles to outside clients. It has already billed its first service to a third party with no relation to the project: a group of university students hired us to process and chart the data from a physical therapy study.
Our business model combines two verifiable revenue sources. Axis drivers earn real income for every order they accept, in a region where formal employment alternatives are limited. In parallel, we are closing implementation and monthly contracts with businesses and fleets that want to integrate our tools, the same model companies like Grab and iFood used to scale from a single city to a regional operation.
We chose Entrepreneurship and Job Creation because driver income is our most verifiable impact today. The underlying thesis, though, is broader: the same body of algorithms (autonomous AI decision-making, signal processing, geospatial correlation, predictive analytics) can be applied to very different domains (logistics, personal safety, geophysics, business analytics) without losing rigor. That capacity to generalize, for us, is the company's real asset, beyond any single application. That same body of work is already in conversation with public science and technology institutions to apply it to risk projection, real-time diagnostics, and territorial planning, areas where usable data infrastructure is still scarce.
We measure our own stage with the same precision we apply to everything else. The earthquake detector is labeled beta inside the app itself while it completes field calibration. BI Dashboard's revenue is still modest, twenty-four dollars, but it comes from a real client with no relation to the team. The Axis dispatch agent went live in production this week, and its decision log starts accruing real entries as drivers use it from today forward. That is what we are presenting: an applied-research lab already generating revenue across more than one line at once, with the discipline to document every decision it makes.
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