01Images
Images and satellites
Locate differences across an area and help track changes in cover and vigor.
RGB / multispectral / indices / resolution— Nexa M1
Nexa M1 — AI for the physical world.
A multimodal platform that ingests data from satellites, drones, cameras, sensors and weather stations, interprets the environment with AI, and delivers diagnosis, recommendation and an executive report in minutes.
- Satellites
- Weather
- Sensors
- AI
- Reports
- Alerts
- History
- Coverage
- 100% of the area
- Report
- In minutes
- History
- 12 months
Operational flow
From signal to diagnosis.
Nexa M1 connects observation, context and interpretation to turn scattered signals into operational understanding.
- 01
Capture
Collects available operational observations.
Satellite / drone / sensor / weather / camera - 02
Context
Places each reading in space and time.
History / location / season / operation - 03
Interpretation
Relates evidence and compares signals.
Models / correlation / comparison / language - 04
Diagnosis
Organizes findings and points requiring attention.
Anomalies / patterns / priorities - 05
Recommendation
Turns analysis into suggested next steps.
Suggested action / follow-up - 06
Report
Communicates the reading with context and references.
Executive summary / traceability
CaptureNexa M1Report
Nexa M1
Combines imagery, sensors, weather, historical data and operational context into a single multimodal interpretation, transforming different signals into diagnosis and decision support.
Multimodal intelligence
A reading that combines different sources.
Nexa M1 creates value by relating data. Images, environmental signals, previous records and operational context become part of a single interpretation.
01Images
02Weather
03Sensors
04History
05Location
06Operational context
Nexa M1
Technical diagnosis
Operational recommendation
Alerts
Executive report
- 01
Relates observations from the same area and period.
- 02
Compares current signals with available records.
- 03
Organizes evidence to support technical assessment.
From sensor to decision, in a single pipeline.
Nexa M1 combines computer vision, language models, spatial reasoning and domain-specialized knowledge to understand operations in the physical world. Images, sensor series and business context go in; diagnosis, recommendation and report come out.
The same architecture that flags an anomaly in a field inspects an ore stockpile, tracks construction progress, or guides an autonomous robot. What changes is the context; the intelligence stays the same.
Input signals
The system observes more than one dimension of the field.
Each source adds a perspective. The analysis depends on available data and operational integrations.
02Weather
Weather and environment
Add the environmental conditions in which an observation was recorded.
Rain / temperature / humidity / wind03Sensors
Sensors and telemetry
Bring local signals and equipment records into the analysis.
Time series / local readings / frequency04History
History and context
Place current conditions in relation to earlier observations and land use.
Area / period / season / operationConceptual diagram · sources depend on integration
- Satellites
- Multispectral imagery from public constellations with a revisit of a few days, and multi-year history per area.
- Drones
- High-resolution scheduled flights for detailed inspection, orthomosaics and plant-level reads.
- RGB and multispectral cameras
- Fixed and robot-mounted cameras, with visible, red-edge and near-infrared bands.
- IoT sensors
- Soil moisture, conductivity, level, vibration, consumption and machine telemetry, as continuous time series.
- Weather stations
- Local data from Atmos and public networks: temperature, rainfall, wind, radiation, humidity and pressure.
- Smartphones
- Field photos sent by the technical team, geotagged and analyzed through the same pipeline.
- Video
- Drone and robot inspection footage, with frame sampling, tracking and counting.
- Enterprise systems
- ERPs, farm management systems and operational databases integrated via API for business context.
What the platform can see, measure and predict.
Composable capabilities: a single analysis can detect a pest outbreak, quantify the affected area, compare it with the previous week and project the productivity impact.
- Disease and pest detection
- Vegetation indices
- RGB analysis
- Thermal analysis
- Hotspots and anomalies
- Temporal comparison
- Segmentation
- Classification
- Counting
- Yield estimation
- Automatic reports
- Data fusion
Disease and pest detection
Identification of foliar symptoms, infestation hotspots and spatial progression, with a confidence level per detection.
Vegetation indices
NDVI, NDRE, GNDVI, SAVI, EVI, NDMI, NDWI and NBR for vigor, chlorophyll, moisture, water and degraded areas.
RGB analysis
Color, texture and shape reading for planting gaps, weeds, soil cover and application quality.
Thermal analysis
Surface temperature maps for water stress, animal thermal comfort and equipment overheating.
Hotspots and anomalies
Automatic location of critical zones within the area, ranked by severity and extent.
Temporal comparison
Historical series per field, comparisons across dates, seasons and similar areas, with change detection.
Segmentation
Pixel-level delimitation of fields, water bodies, roads, stockpiles, structures, animals and machinery.
Classification
Crop typing, phenological stage, land use, surface type and infrastructure condition.
Counting
Counting of plants, gaps, animals, vehicles, pallets and assets, with tracking across video frames.
Yield estimation
Models that combine spectral indices, climate and history to project yield and internal variability.
Automatic reports
Full technical and executive reports generated without manual drafting, on demand or on a schedule.
Data fusion
Imagery, sensor, climate and history combined into a single contextual diagnosis instead of isolated readings.
Analysis architecture
Layers that organize understanding.
From data input to communication, each layer adds structure to the analysis. The interface provides access to this operational interpretation process.
- 01
Data input
Available sources, location and observation period define the starting point.
- 02
Preparation and normalization
Quality, organization and temporal alignment make records comparable.
- 03
Multimodal interpretation
Models and context help relate patterns, changes and limitations.
- 04
Decision and communication
Findings are organized into priorities, recommendations and team reports.
Six modular layers, integrable via API.
- 01
Ingestion layer
Connectors for satellite, drone, camera, IoT, weather and external systems. Async queue, payload validation and versioning of every capture.
- 02
Geospatial processing layer
Reprojection, geometry-based cropping, mosaicking, cloud masking, index calculation and zonal statistics per area of interest.
- 03
Model layer
Detection, segmentation, classification and regression served via API, with domain-based routing and execution in the cloud or at the edge.
- 04
Reasoning layer
Language models with a sector-specialized SLM architecture translate numbers into diagnosis, probable cause and recommendation.
- 05
Data layer
Geospatial and time-series database with complete history per area, with access control by organization and user.
- 06
Delivery layer
Dashboard, interactive maps, alerts, export, PDF reports and a public API for integration with client systems.
A complete operating environment.
Dashboard
Consolidated view of monitored areas, indicators, latest analyses and open operational items.
Interactive maps
Polygon drawing, location search, per-index layers and pixel-level reading of the area.
History
Every analysis archived by area and date, with a full report download for each run.
Alerts
Email notifications when an index crosses a threshold, an anomaly appears, or weather changes the operational window.
Export
Executive PDF, georeferenced images, CSV series and layers for use in GIS.
API
Endpoints to trigger analyses, query results and integrate the platform with the client's system.
Multi-user
Organizations, teams and role-based permissions, with shared areas and access trails.
Data governance
Per-organization isolation, row-level access control and audit logging of operations.
MOBILE APP
Nexa M1 goes with you into the field.
Areas, alerts, weather, reports and history are available in the Convex mobile app, with the same data as the web platform.
- Areas
- Monitored areas and the status of every analysis in your hand.
- Alerts
- Anomaly warnings, index thresholds and operational window changes.
- Weather
- Field conditions, forecast and agrometeorological indicators.
- Reports
- Maps, indices and PDFs available in the browser and in the app.
Less uncertainty, more operational control.
The platform turns physical-world data into actionable information, reducing reliance on manual interpretation and shortening the cycle from observation to decision.
Unified view
Satellites, drones, cameras, sensors and interactive maps feed a single panel, without information silos.
Early detection
Anomalies are identified before they become visible problems, widening the window for action.
Scale and standardization
The same analysis criteria apply to one area or thousands of hectares, without depending on the field team.
Automated decisions
Diagnosis, probable cause and recommendation are generated automatically, cutting the time between collection and action.
Instant reports
Technical and executive reports ready in minutes, with maps, charts and a natural-language summary.
Operational memory
Complete history per area, comparable across seasons, dates and units, for continuous management improvement.
Operational outputs
Interpretation is only the beginning.
A useful reading reaches the team in a format that supports review, prioritization and follow-up.
Technical diagnosis
Findings, signs of change and points of attention alongside analysis context.
Operational recommendation
Suggested next steps to guide team assessment and planning.
Alerts
Relevant occurrences and changes for follow-up, according to configured rules.
Executive report
An organized summary to consult, share and revisit over time.
Delivery
From diagnosis to executive report.
The report brings together observations, context and follow-up priorities. Maps and references keep the summary connected to the evidence.
01
Reading summary
An organized summary to consult, share and revisit over time.
02
Priority observations
Findings, signs of change and points of attention alongside analysis context.
03
Suggested follow-up
Suggested next steps to guide team assessment and planning.
04
Sources and period
Origin, area and time of observation.
Nexa M101 / 01
Area / date / context
Executive report
For technical reviewReading summary
Review the highlighted zone and compare it with previous records.
01
Priority observations
Review the highlighted zone and compare it with previous records.
02
Suggested follow-up
Relate the observed change to field conditions before deciding on an action.
Sources and periodArea / date / context
Every analysis becomes a complete technical document.
Individual per-layer maps, distribution charts, temporal evolution and a natural-language summary, ready for the technical team and for leadership.
- RGB map
- NDVI map
- Hotspots
- Water stress
- Severity
- Executive PDF
RGB map
True-color image of the area on the analysis date, the visual base for every other layer.
NDVI map
Vegetative vigor classified into ranges, with the percentage of area in each class.
Hotspots
Critical zones highlighted and ranked by severity, ready to route the field visit.
Water stress
Moisture and surface water layers for irrigation, traffic and operational windows.
Severity
Intensity map of the detected problem, with affected area and progression between dates.
Executive PDF
A minimalist document with branded cover, essential indicators and a to-the-point summary.
Operational confidence
Where each reading comes from matters.
Source, period and context help review an interpretation. History provides continuity and allows teams to revisit what informed each reading.
- 01
Source
Origin, area and time of observation.
- 02
Processing
Organization of the records used in the analysis.
- 03
Interpretation
Findings related to available context.
- 04
Record
A documented reading for consultation and comparison.
Nexa M1Area / date / contextRecord
Multisector applications
The same intelligence model, different operational contexts.
Nexa M1 is not just an agriculture assistant: it is a contextual intelligence layer over data, documents, procedures and operations.
01
Agriculture
- Interpreting reports and Sentinel-2 data
- Weather and monitored-area history
- Anomaly analysis and technical support
- Contextual recommendations per area
02
Food industry
- Procedures, HACCP and SOP lookup
- Inspection records and non-conformities
- Audit support
- Interpreting events observed by Nexa Vision
03
Mining
- Operational indicators and productivity
- Shift and occurrence comparison
- Bottlenecks and flow-metric reading
04
Industry and maintenance
- Manual and procedure lookup
- Work orders and failure history
- Contextual technical assistance
05
Logistics
- KPIs, delays and occupancy
- Routes and exceptions
- Operational reports
Nexa Vision observes and structures; Nexa M1 interprets.
Perception organizes what happens; intelligence gives the data context and meaning. Each product also operates independently.
Conceptual diagram · sources depend on integration
One platform, six operational contexts.
01
Agriculture
Full-cycle monitoring: vigor, water stress, nutrition, weeds, planting gaps and yield variability, with prescription maps for site-specific application.
02
Livestock
Animal counting, weight estimation, behavior, thermal comfort, pasture assessment and water resource monitoring.
03
Forestry
Forest inventory, biomass estimation, detection of gaps, stress and burned areas, with multi-year stand tracking.
04
Mining
Stockpile volume estimation, tailings and slope monitoring, conveyor inspection and operational risk detection.
05
Construction
Progress compared against schedule, structural inspection, earthworks control and material and traffic management.
06
Robotics
Perception and context for aerial and ground robots: obstacles, trajectory, task prioritization and diagnosis-guided action.
Traditional method vs. Nexa M1.
| Criterion | Traditional method | Nexa M1 |
|---|---|---|
| Area coverage | Point sampling on foot | 100% of the area, pixel by pixel, on every revisit |
| Frequency | Sporadic visits, dependent on staff | Continuous monitoring and scheduled automatic analyses |
| Time to diagnosis | Days between collection and report | Minutes between capture and a finished report |
| Early detection | Problem already visible to the naked eye | Spectral and thermal signals before the symptom appears |
| Standardization | Depends on the evaluator's experience | Same criteria, same metrics, across the whole operation |
| History | Notebooks, spreadsheets and scattered notes | Complete time series per area, comparable across seasons |
| Recommendation | Manual interpretation by a technician | Automatic recommendation with probable cause and priority |
| Scale | Limited by team size | Thousands of hectares and multiple units in parallel |
Common questions about the platform.
Do I need a drone or robot to use Nexa M1?+
No. The platform runs on satellite imagery and climate data alone, and gains resolution as drones, cameras and sensors are added.
How long until I get the first report?+
After drawing the area on the map, the analysis runs and the full PDF report is ready in a few minutes, with every layer and chart.
Which crops and environments are supported?+
The platform is crop- and sector-agnostic: the spectral indices and vision models apply to fields, pastures, forests, mines, construction sites and industrial assets.
Is data isolated per client?+
Yes. Each organization has its own data, areas and reports, with role-based access control and an audit trail.
Can it integrate with the systems we already use?+
Yes. The platform exposes an API to trigger analyses and query results, and exports layers and series for use in GIS and management systems.
Does Nexa M1 work without internet in the field?+
Critical inference can run at the edge, embedded in the drone or robot, and results sync with the platform once the connection returns.
How does weather factor into the analysis?+
Data from Atmos and weather networks is cross-referenced with imagery to explain variations, predict risk and flag operational windows.
Is there a limit on monitored area?+
The plan defines the contracted area in hectares. The architecture processes multiple units and thousands of hectares in parallel.
Technical answers
Indices, probable causes and thresholds explained in plain language.
Operational context
Answers account for area, crop, weather and operational history.
Decision support
Action priority, operational window and the suggested next step.
Ask Nexa M1.
Convex's contextual assistant. Technical responses focused on robotics, drones, agriculture and regulation. For real operations, get in touch with us.
Suggestions
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See Nexa M1 running on your operation.
Pick a real area, draw the polygon, and get the first complete report with maps, indices, diagnosis and recommendation.