Custom Real-World Video and Sensor Data for AI Systems.

Starting with high-entropy urban environments in Bangladesh, we collect and structure targeted real-world data for autonomous systems, robotics, and computer vision teams.

  • Existing Bangladesh urban traffic data with structured segments and continuous source recordings
  • GPS, IMU, route, time, weather, device, scene, and quality metadata
  • Custom collection projects designed around your missing scenarios and deployment environment

Working pipeline from field capture to quality control, traceability, and buyer-ready delivery.

Explore the Free Sample →

Inspect the data first, then discuss a pilot built around your missing scenarios.

Operational Proof

A Real-World Data Pipeline Already in Operation

Before expanding into new collection projects, we built and validated a complete end-to-end workflow using real-world urban traffic data from Bangladesh.

Real Field Collection

Continuous first-person recordings captured in dense urban environments, together with GPS, IMU, route, time, weather, and contextual metadata.

Engineering Pipeline

Raw recordings are segmented, validated, quality checked, linked to source recordings, and packaged into engineering-ready datasets.

Scalable Custom Collection

Start with a focused evaluation batch, then expand through dedicated collection instructions, repeat delivery cycles, and project-dependent field capacity as requirements grow.

Actual Data Preview

See the Data, Ground Truth, and Metadata We Actually Deliver

Real examples from our Bangladesh urban traffic pipeline—showing source footage, human-verified annotations, and structured metadata prepared for engineering workflows.

16,000+
Structured Segments
5,300+
Source Recordings
100+
Hours Collected

Collection continues through our active field pipeline and can expand according to project scope, target environments, and delivery requirements.

Source Video

Real-World Traffic Footage

Dense, lane-less movement captured in operational urban environments with pedestrians, motorcycles, buses, cars, and informal transport sharing the same space.

Human-verified bounding-box annotations for dense Bangladesh urban traffic Human GT

Human-Verified Ground Truth

Frame-level bounding boxes reviewed for complex object overlap, heavy occlusion, mixed vehicle classes, and dense multi-agent interaction.

Structured Engineering Metadata

Clip-level engineering metadata including timestamps, GPS, IMU, scene context, capture properties, object statistics, privacy processing, quality indicators, and delivery status.

timestamp GPS IMU scene device quality delivery

Preview fields are simplified for readability. Click the metadata card to inspect the complete website-safe JSON record. Final schemas and delivery formats are defined against each project’s technical requirements.

What We Deliver

Engineering-Ready Real-World Data

We deliver more than raw recordings. Every collection is prepared for engineering evaluation, model development, and long-term data operations.

Targeted Scenario Collection
Real-world environments collected around your deployment challenges, missing edge cases, and failure scenarios.
Video & Sensor Capture
Smartphone-based video capture with GPS, IMU, timestamps, route context, device information, and recording metadata linked to each source recording.
Engineering-Ready Delivery
Quality-controlled datasets packaged with documentation, validation, metadata, and delivery formats ready for engineering workflows.
Typical Commercial Delivery Package 
dataset_package/

├── clips_blurred/
│   └── *.mp4
│
├── human_gt_preview/
│   └── *.jpg
│
├── metadata/
│   └── *.json
│
├── buyer_catalog.json
├── buyer_sample_index.csv
├── sample_index.csv
├── dataset_manifest.json
├── release_context.json
│
├── README.md
├── DATASET_CARD.md
├── QUALITY_ASSURANCE.md
├── PRIVACY_REPORT.md
├── RELEASE_CERTIFICATE.md
├── LICENSE.md
│
└── SHA256SUMS.txt

Typical commercial deliveries include engineering documentation, structured metadata, validation records, licensing information, privacy documentation, release certificates, and integrity verification. Some annotation assets and project-specific deliverables are provided according to the agreed project scope.

Failure Scenarios

High-Entropy Conditions That Expose Model Weaknesses

We specialize in dense, unstructured urban environments where formal traffic rules, clean lane geometry, predictable agent behavior, and controlled visibility cannot be assumed. These long-tail conditions are often underrepresented in simulation and public datasets.

Ambiguous Human Intent

Hesitation, informal negotiation, incomplete signals, and unpredictable decisions that challenge intent prediction and planning systems.

Dense Multi-Agent Interaction

Pedestrians, motorcycles, vehicles, and other agents moving through overlapping space with minimal separation and frequent occlusion.

Informal Traffic & Degraded Geometry

Weak lane boundaries, mixed transport modes, roadside activity, irregular road geometry, low visibility, and rapidly changing right-of-way.

Engineering Delivery

Deliverables Built for Engineering Workflows

Each dataset is packaged with the media, metadata, documentation, validation records, and integrity files required for technical evaluation and commercial delivery.

Available

Core Data & Sensor Records

Primary data assets linked through stable clip and source identifiers for traceable engineering use.

  • Video files — MP4
  • Structured metadata — JSON
  • Dataset indexes — CSV
  • GPS and route records
  • IMU sensor records
Pack or Project Dependent

Human GT & Annotation Formats

Human-verified ground truth is included only where specified. Annotation exports are defined against the project scope.

  • Human-verified Ground Truth
  • Bounding-box annotations
  • COCO export — project-dependent
  • YOLO export — project-dependent
  • Custom class definitions
Commercial Delivery

Documentation & Integrity

Buyer-facing documentation and verification files support review, reproducibility, and controlled dataset handoff.

  • Dataset Manifest
  • Data Dictionary
  • QA Report
  • SHA-256 Checksums
  • Commercial delivery package
Enterprise Delivery Controls
Privacy Processing
Face and license plate blurring where required.
QA & Validation
Documented quality review before release.
Source Traceability
Segments linked to source and collection context.
Licensing & Integrity
Commercial terms, manifests, and SHA-256 checksums.

Privacy scope, permitted use, annotation formats, custom schemas, delivery cadence, and licensing terms are confirmed before release.

Engineering workflow for real-world AI data collection

How We Work With AI Teams

Every project follows a simple engineering workflow—from identifying missing scenarios to delivering structured real-world datasets.

Built for AI Teams Working On
Autonomous Driving & ADAS Robotics Physical AI Computer Vision Smart Mobility

Step 1 — Define the Bottleneck.
We begin by understanding where your model struggles, your deployment environment, and the real-world situations your existing data does not represent.

Step 2 — Design the Collection.
Together we define the collection scope, target environments, metadata requirements, quality standards, and delivery format before field work begins.

Step 3 — Collect, Validate, Deliver.
Our operating pipeline manages field collection, quality review, metadata generation, traceability, documentation, and engineering-ready dataset delivery.

Define clearly.
Collect precisely.
Deliver with confidence.

Why AI Teams Work With Us

Teams can begin with an existing Bangladesh evaluation sample, define the scenarios their current data does not represent, and expand into project-specific collection with agreed metadata, QA criteria, annotation scope, and delivery controls.

Pilot Options

Start Small. Scale With Confidence.

Start with a focused evaluation batch, expand into project-specific collection, and continue through repeat delivery cycles as deployment requirements evolve.

Every engagement is structured to support iterative data acquisition, scheduled delivery batches, and long-term engineering programs—not only a one-time dataset handoff.

Option 01

Evaluation Batch

Validate whether a focused real-world scenario exposes meaningful weaknesses in your current evaluation, perception, or training workflow.

  • Focused scenario scope
  • Structured metadata
  • Quality-controlled delivery
  • Clear expansion path
Option 02

Custom Collection

Build a field collection project around your deployment environment, target behaviors, capture instructions, metadata, and quality requirements.

  • Project-specific capture instructions
  • Environment-specific collection
  • Flexible delivery structure
  • Repeatable collection workflow
Option 03

Ongoing Data Partnership

Continue collecting, validating, and refining real-world data as your model, deployment environment, and engineering roadmap evolve.

  • Iterative collection cycles
  • Scheduled delivery batches
  • Project-dependent capacity expansion
  • Long-term data operations
Explore the Free Sample ↗ Discuss a Pilot →

Explore a free sample first, then discuss a pilot tailored to your deployment goals. Scope, timeline, metadata, quality requirements, licensing, and delivery cadence are confirmed before collection begins.

Frequently Asked Questions

Questions AI Teams Usually Ask

Most conversations begin with practical questions about collection, quality, metadata, and delivery.

Can you collect data outside Bangladesh?

Yes. Our current operational pipeline is validated in Bangladesh, and the same workflow can be expanded to new environments based on project requirements.

Can you collect custom scenarios?

Yes. We define collection around your deployment environment, target behaviors, metadata, and engineering objectives instead of a fixed catalog.

What is included besides video?

Depending on the project we can deliver GPS, IMU, timestamps, scene context, quality indicators, documentation, and structured metadata.

Can we start with a small pilot?

Yes. Most projects begin with a focused evaluation batch before expanding into larger collection programs.

How is quality managed?

Every delivery follows documented quality review, metadata validation, traceability, and engineering-ready packaging before release.

How are privacy, licensing, and data integrity handled?

Privacy processing is defined for each delivery and may include face and license plate blurring where required. Commercial usage rights, permitted use, redistribution restrictions, delivery scope, and project-specific licensing terms are confirmed before release. Buyer packages can also include QA documentation, source traceability, dataset manifests, and SHA-256 checksums for integrity verification.

Can you support enterprise security requirements, NDAs, and controlled delivery?

Yes. Project-specific confidentiality terms, NDA requirements, delivery access controls, file integrity checks, and buyer-approved handoff procedures can be defined before collection begins. Security scope and operational responsibilities are confirmed during project planning.

Can capture specifications, sensors, metadata, and annotation formats be customized?

Yes. Camera settings, recording instructions, GPS and IMU requirements, metadata fields, quality thresholds, annotation classes, and delivery formats can be defined against the project scope. Specialized sensors or formats are provided only when technically available and explicitly agreed before collection.

Start a Conversation

Tell Us What Your AI Model Is Missing.
We’ll Help Define the Right Real-World Data.

Share your deployment environment, failure scenario, or missing data requirement. We’ll review feasibility and recommend the most practical next step: an evaluation batch, custom collection, or ongoing data partnership.

Discuss Your Data Requirement →

Scope, timeline, metadata, quality requirements, pricing, and licensing are confirmed before collection begins.