Co-creation
Models and products designed around the client's real context.
Your Data has a Voice
scapder actionable intelligence from real data
We transform complex data into actionable intelligence so teams, operations, and products can make better decisions with AI models applied to the real world.
01
We do not sell generic software.We build capability.
We co-create with our clients and transfer knowledge to their teams. The goal is not technological dependence: it is for the organization to turn its own data into decisions.
Knowledge transfer
Technical knowledge turned into the team's own operating practice.
Internal capability
Systems that support decisions without creating dependence.
Applied experience in
- construction
- legal information
- retail
- logistics
- maritime transportation
02
Documented cases with real data.
The cases show a pattern: start with fragmented data, model applied intelligence, and return actionable decisions.
Prioritizing prospects with scientific probability.
scapder processed 20,000+ records across 300 behavioral variables and identified 5 real buyer profiles using dynamic clustering.
- 20,000+records processed
- 300variables analyzed
- 5profiles identified
Legal context to reduce failed searches.
Contextual machine learning algorithms reduced searches with no results from 76% to a documented operational minimum.
- 5%failed searches
- 4.7/5accessibility
- 4.4/5relevance precision
Geolocated demand without coding.
scapder turned complex technical data into intuitive visual interfaces and processed 6,000 clients per month.
- 15exact lists
- 6,000clients per month
- 100%visual autonomy
Predicting dynamic routes with artificial intelligence.
Billions of historical and operational records were transformed into intelligent routing and safe dynamic trajectories.
- 1,435Msatellite records
- 20Mrecords every 15 min
- Optimalfuel consumption in transit
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Team
The team that turns complex data into actionable intelligence.
Harold Sthid Piravaguen
CEO
Lisset Velasquez
Legal Advisor and Human Resources
Hugo Ardiles
CTO
Joshua De La Pava
Product and Business Development
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Three platforms.One mission: turning data into decisions.
Computer vision · Edge AI
Edge Sight AI
Operational perception for physical video infrastructure.
- On-premises
- Real time
- Data sovereignty
Synthetic audiences · AI agents
Sentientum
by scapderForty synthetic voices debate a decision and the screen shows how they reach a conclusion.
- 40 synthetic voices
- 5 steps
- Sample data
MLOps · AI governance
ML SPEC Kit
An operating system for scaling ML models.
- Lab to production
- Automated governance
- Agents and humans
05
Your video infrastructure already exists.Now it can think.
Edge Sight AI runs on existing cameras, processes data at the edge, and detects events in milliseconds without data leaving the perimeter.
- Existing cameras
- Local Edge AI
- Computer vision
- Detection in ms
- Autonomous response
Fixed camera on a construction site. The same video is shown raw and with bounding boxes on the people and the machinery. The people count drops from 5 to 4, then to 3, and an excavator enters the scene. The scene is generated for illustration; the detection is real and is performed by a vision model.
The vertical bar reveals the version with detection.
Illustrative demo: generated scene, real detection by a vision model (YOLOv8) running on the video.
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A repeatable model for turning information into capability.
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01
Understand data and context
Map signals, constraints, and the decisions the business needs to make.
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02
Model applied intelligence
Build models fitted to real data, not generic abstractions.
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03
Integrate into real operations
Bring intelligence into the flow where teams decide, detect, or prioritize.
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04
Transfer capability
Support the team so the system becomes an internal practice.
07
Turn your data into an internal decision-making capability.
If your organization has fragmented data, existing infrastructure, or models that need to move into operations, scapder can help you turn them into actionable intelligence.