Spatial and visual dataset operations
Manage field data from request to AI-ready dataset.
Polar helps business owners, startup founders, and data teams scope spatial, visual, and operational data needs, validate incoming deliveries, and ship clean datasets into business or AI workflows.
01
Scope Data Need
Define the site, asset, format, source, and quality bar.
02
Validate Delivery
Review files, metadata, provenance, notes, and acceptance criteria.
03
Ship Dataset
Package approved data for analysis, automation, or model workflows.
- Mission briefAOI, asset type, source, deadline
- Dataset snapshotIce: verified, reusable state
- Delivery packageSledge: files, manifests, metadata
- Model handoffFit: train/eval-ready splits
- Developer layerAPI, webhooks, manifests, SDK
Best for
Owners · Founders · Dev teams
Outputs
Snapshots · Manifests · Splits
One workspace for the full data mission.
Polar turns a vague data need into a scoped request, a validated delivery, and a dataset package that can move safely into analytics, automation, evaluation, or training.
Define exactly what needs to be captured
Teams describe the asset, geography, collection source, delivery format, quality thresholds, and deadline before data is collected, purchased, uploaded, or imported.
Receive indexed, explainable outputs
Every delivery becomes a private catalog with files, notes, QA status, provenance, searchable metadata, and export-ready records.
Acquisition channels
Delivery management without spreadsheets
Manage data coming from vendors, internal teams, field partners, existing storage, or customer uploads, then review every delivery against the same acceptance criteria.
The operating system for AI‑ready infrastructure data.
Teams building with spatial, visual, and operational data need fresh, trusted ground truth. The hard part is not only search or labeling; it is turning messy field evidence into data that people and models can safely use.
Polar connects requests, deliveries, QA, metadata, manifests, and exports in one workspace so the result is searchable, auditable, transferable, and reusable.
Precise requests
Replace loose emails with structured requests: AOI, asset, source, format, deadline, budget, and acceptance criteria.
Validated supply
Track vendor, field partner, internal, or imported data deliveries with review status, evidence, notes, and approval history.
Developer-ready datasets
Turn one-off captures into private catalogs with export manifests, API access, and webhooks for downstream systems.
Repeat monitoring
Convert inspections and field updates into repeatable dataset versions for routes, sites, assets, and seasonal change detection.
Built for owners, founders, and data teams.
The best version of Polar is not a generic computer vision API. It is the system of record for managing, validating, transferring, and preparing proprietary spatial and visual data.
For owners and founders
Request Workspace
Create a data brief, mark the area of interest, define acceptance criteria, and track the request from draft to approved delivery.
- Structured data requests
- Budget, deadline, and location
- Acceptance and approval status
For vendors and internal teams
Delivery Intake
Collect uploads, links, notes, metadata, and source context from any acquisition channel, including field partners when capture is needed.
- Vendor and team handoffs
- Upload and review status
- Delivery notes and source context
For developers and AI teams
Data Workspace
Organize delivered files into searchable catalogs with notes, quality status, metadata, provenance, webhooks, and export-ready records.
- Private visual data rooms
- QA, notes, and provenance
- API, webhook, and manifest delivery
Ice module
Freeze datasets
Freeze verified data deliveries into secure, immutable dataset snapshots for audit, reuse, and training provenance.
- Verified dataset snapshot
- Attached provenance trail
- Audit-ready dataset state
Sledge module
Move deliveries
Move large files, manifests, and metadata into the storage, warehouse, or AI tools your team already uses.
- Large-file handoff model
- Manifest and metadata movement
- Destination-ready delivery
Fit module
Prepare for training
Package frozen datasets into train/eval-ready splits with provenance attached for model training and evaluation.
- Train/eval split model
- Dataset lineage attached
- Model workflow handoff
1. Scope
Brief
Describe the asset, geography, source, format, deadline, budget, and delivery requirements.
2. Source
Intake
Collect files, links, notes, metadata, and source context from the right acquisition channel.
3. Validate
QA
Review uploads, notes, status, and acceptance criteria before confirming completion.
4. Reuse
Catalog
Keep every approved dataset searchable for analysis, comparison, automation, and model workflows.
Scope the need.
Ship the dataset.
Polar keeps the core data operations loop in one place: define requirements, receive field or visual data, validate the delivery, then package it for the tools your team already uses.
Manage the data request
Create a data mission for a route, work site, claim, field, store, asset, or customer workflow. Define the area, source, format, quality bar, deadline, and expected output.
- Create a structured request with deadline and scope
- Describe source, geography, and output format
- Route work to internal teams, vendors, or field partners
- Confirm delivered data against acceptance criteria
Validate and prepare delivery
Use Polar to collect files, links, notes, manifests, and QA context from any source. Approved deliveries become clean dataset packages developers and operations teams can reuse.
- Capture upload links, notes, and source metadata
- Review quality, provenance, and completeness
- Generate manifests for downstream systems
- Prepare exports for APIs, storage, or model workflows
Draw zones.
Get exactly that data.
For spatial datasets, mark zones directly on a map or source preview. Each zone defines the evidence, format, and quality target that a delivery must satisfy.
- 01
Scope the asset
Start with a site, route, address, image, or GPS coordinates. The workspace anchors the dataset to the right place and context.
- 02
Define data zones
Mark capture areas, inspection corridors, or asset groups. Keep the operational boundary attached to the dataset record.
- 03
Set data requirements
Assign source types and outputs per zone, such as "RGB imagery", "thermal scan", "NDVI", "LiDAR", or "defect annotations".
- 04
Review delivery
Review uploaded files, links, notes, and metadata before the delivery becomes an approved dataset.
01
Scope
Team defines asset, geography, source, format, deadline, budget, and acceptance criteria.
02
Intake
Files, links, notes, and metadata arrive from vendors, field partners, internal teams, or existing systems.
03
Validate
Review quality, provenance, completeness, and delivery notes against the original request.
04
Package
Freeze the approved files, metadata, QA state, and manifest into a reusable dataset version.
05
Ship
Move the dataset to analytics, storage, automation, or model workflows with provenance attached.
Launch your first managed dataset workflow.
Use Polar to scope the request, intake delivery, validate quality, and turn the result into a private dataset your team can inspect, export, and connect to your stack.