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Polyline Annotation for AI, GIS & Mapping Teams

Polyline Annotation &
Lane Detection Labeling

Precise BPO provides outsourced polyline annotation services for lane, road, GIS boundary and utility datasets, from pilot batches to high-volume production. Our in-house team of 540+ annotators in Pune, India has labeled 810M+ images across all annotation types since 2008. Our processes are aligned with ISO 27001, HIPAA and GDPR.

Precise BPO polyline annotation — dashcam and aerial imagery converted into lane and road line data
99.8% Accuracy Rate QC-validated
38M+ Polylines Labeled Since 2017
810M+ Images Processed All annotation types
540+ In-House Annotators In-house & NDA-bound
24–48h Turnaround Standard batch
18+ Years in Business Est. 2008
On This Page

Security, Compliance & Working Terms

🔒Compliance-aligned Workflows
📋NDA on Every Engagement
🧪Free Pilot Available
🔧Platform Agnostic
PL

What is Polyline Annotation?

Polyline annotation is the process of tracing linear structures (lanes, roads, boundaries, cables, pipelines and pathways) in images or video, using sequences of connected points called vertices. Each polyline records the path, direction and class of a line, so a model knows where it runs and what it connects to.

It is one of the core techniques in computer vision data labeling, used heavily for lane detection, GIS mapping, utility inspection and autonomous driving. A bounding box encloses an object; a polyline follows the path of a line, which is why it suits navigation, geospatial and infrastructure models.

Polyline outputs are ordered lists of vertex coordinates, usually delivered as GeoJSON, Shapefile, COCO-style JSON or a custom schema, so the geometry loads directly into GIS tools, training frameworks and path-planning systems.

Lane Detection
Polylines trace road lanes, curbs and drivable paths to train autonomous driving, ADAS perception and HD map models on road geometry.
GIS & Mapping
Rooflines, property boundaries, river courses and infrastructure features annotated from aerial and satellite imagery for mapping AI.
Utility Inspection
Powerlines, cable routing, pipeline traces and structural evaluation from aerial and drone imagery for predictive maintenance AI.
Output Formats
Delivered in the format your GIS tools and training pipelines expect.
01

How We Handle Polyline Annotation

Track Record
18 Years. 810M+ Images. One In-House Team.
9+
Years of polyline annotation, since 2017
▲ Since 2017
38M+
Polylines labeled across all projects
▲ Lanes, GIS, utilities & more
540+
In-house annotators, all NDA-bound
▲ Dedicated domain teams
99.8%
Accuracy rate, three-tier QA validated
▲ Geometry & topology checks
24–48h
Standard turnaround for batch annotation jobs
▲ Enterprise SLA
NDA

Since 2017, our team has delivered polyline annotation as part of our data labeling services, covering lane extraction in mobility AI, roofline tracing for utilities and GIS platforms, aisle mapping for retail and route annotation for logistics. The work is done from our delivery center in Pune, which runs around the clock to cover US, UK and EU time zones, and every dataset is built to your model specification.

Our annotators specialize in line-based geometry. They apply vertex spacing standards, continuity rules and topology checks so each dataset can go straight into training. We handle footage from dashcams, drones and CCTV, and imagery from satellites and aerial surveys. We work in your annotation platform and deliver in your output schema.

For autonomous vehicle programs, we label lane boundaries at volume with frame-accurate polylines, covering solid lines, dashed markings, merge zones and complex intersections. Mobility teams can move from a pilot to full production without building their own labeling operation.

GIS and geospatial teams use us for polyline labeling of utility corridors, property boundaries and hydrological features in aerial and satellite imagery. We take on long-running mapping programs and one-off infrastructure surveys, and we work inside your existing tools.

We also take on online data entry and data conversion work under the same NDA.

🚀
Dedicated Domain Teams
Annotators trained on line-based work, organized into dedicated teams for mobility, GIS and utility projects.
📐
Vertex Precision & Geometry Standards
Every polyline is checked against vertex density and continuity rules, then passes three-tier QA with topology and alignment checks, measured at 99.8% accuracy on our internal QC data.
🔐
Security and Compliance
Secure access control, NDA-bound workflows and audit trails aligned with international data governance standards.
02

Industries Using Polyline Annotation

Polyline datasets are used wherever a model has to follow a line rather than find an object: mapping, mobility, utilities, inspection and retail analytics.

🚗

Autonomous & ADAS Systems

Lanes, curbs, road edges, barriers and drivable paths, tracked consistently from frame to frame for AV perception and navigation models.

01
🗺️

GIS & Mapping Platforms

Rooflines, boundaries, pipelines, utilities, river courses and contour lines from satellite and aerial imagery. Outputs load into QGIS, ArcGIS and cloud mapping engines.

02
⚡

Utilities & Energy

Powerlines, cables and pipelines traced from aerial and drone imagery for predictive maintenance, grid management and asset-integrity models.

03
🏪

Retail & Commercial Spaces

Aisles, walkways and customer movement paths traced from CCTV footage, used for flow modeling, heatmaps and store layout analysis.

04
🏗️

Infrastructure & Construction

Road network updates, asset tracing, excavation outlines and progress measurement from drone and ground-level imagery, for civil engineering and infrastructure monitoring models.

05
🚛

Logistics & Transport

Warehouse pathways, route constraints, waypoints and yard movements, used for route optimization, fleet management and last-mile delivery models. We also handle e-BOL data entry and driver's log data entry for logistics clients.

06
🌾

Agriculture

Field boundaries, irrigation lines and farm paths traced from drone imagery, for precision farming, yield prediction and land-use monitoring.

07
🏥

Medical & Healthcare AI

Vessel centerlines, nerve pathways and other tubular structures in medical imaging, for radiology, surgical navigation and clinical decision support models. We also handle medical claims data entry for healthcare clients.

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Polyline vs Bounding Box vs Semantic Segmentation: When to Use Which

The right annotation type depends on what you are labeling and what the model has to output. Here is how the three compare for line-based features. If bounding boxes are on your shortlist, our guide to common bounding box mistakes shows where they tend to go wrong.

Criteria Polyline Annotation Bounding Box Semantic Segmentation
Shape Multi-point connected line / path Rectangle (axis-aligned or rotated) Pixel-level mask per class
Best for Linear structures — lanes, roads, cables, rivers, field boundaries Bounded objects — vehicles, people, products, animals Scene understanding: roads, sky, buildings
Annotation Speed Fast — path-tracing workflow Fastest — single drag Slowest — pixel-by-pixel
Cost Efficiency High — scales well with volume Highest — minimal effort per object Lowest — intensive per image
Boundary Precision Follows the line vertex by vertex Object-level (includes background) Pixel-level
Video / Temporal Strong — frame-by-frame path tracking Fast frame tracking Very high effort per frame
Common Use Cases Autonomous driving, GIS mapping, utilities, agriculture, medical Retail, ADAS, medical, sports analytics, surveillance Urban scene parsing, surgical vision, land cover mapping
Precise BPO Service This page — Polyline Annotation Bounding Box Annotation → Semantic Segmentation →
← Swipe to see full comparison →

Not sure which annotation type fits your project? Talk to our specialists — we'll recommend the right approach based on your feature types, model architecture and dataset requirements.

04

Our Capabilities

We label lane boundaries, GIS features, road edges and route paths, and we are set up for high-volume, multi-class datasets where topology accuracy matters.

✓
High-Accuracy Line TracingPrecise vertex placement for lanes, boundaries, utilities and paths, following your vertex density and spacing rules.
✓
Video and Frame-by-Frame TrackingWe track polylines frame by frame in dashcam, CCTV and drone video, keeping lines continuous through occlusions so models can learn from motion.
✓
Semantic Attribute TaggingEach line is tagged with class and attributes such as line type, direction, marking type and surface condition, following your schema.
✓
Topology & Continuity ValidationAutomated and manual checks enforce vertex continuity and junction logic, so broken lines are caught before delivery.
✓
Three-Tier QAThree QA tiers cover topology, alignment, vertex sampling and reviewer sign-off on every batch.
✓
Flexible Export SchemasOutput follows your schema, structured for direct use in your GIS and training tools.
✓
Model-Assisted Pre-LabelingAnnotators use model-assisted pre-labeling to speed up tracing, and a reviewer checks every line the tool produces.
✓
Custom GuidelinesWe write the guidelines around your use case: vertex rules, line topology, class hierarchy, continuity logic and edge-case handling.
Send Us Your Dataset Brief →
Precise polyline annotation showing lane boundary tracing and road edge labeling for autonomous driving and GIS datasets
LANE_L · 0.98 LANE_R · 0.96 CENTER · 0.99 vtx: 295,136 spacing: 55px topo: ✓ cont: ✓ conf: 0.98 · QC ✓ VTX 0.98 ANNOTATED 3 POLYLINES / FRAME SAMPLE FRAME

Our Workflow

Every project follows the same six steps, from requirements through to delivery.

1

Requirements Review

We agree annotation goals, line classes, vertex spacing rules, topology standards, continuity logic and output schemas with your AI or GIS team before any labeling begins.

Class taxonomy Vertex rules Continuity logic SLA setup
2

Data Intake & Setup

Images and video arrive through encrypted transfer, are converted to standard formats, geo-referenced where required and split into batches, all under NDA.

Encrypted transfer NDA protection Access control Geo-referencing
3

Data Labeling

Specialized annotators trace polylines with client-defined vertex density, spacing standards and class rules, using annotation platforms of your choice or our internal tooling. Video data is annotated frame by frame for temporal continuity.

Line tracing Vertex placement Frame-level tracking Class tagging
4

Quality Check

Every batch passes three QA tiers: peer review first, then automated geometry and topology checks, then an expert audit and reviewer sign-off.

Topology check Alignment audit Sampling Reviewer sign-off
5

Client Review

Annotated batches go to you for review. Feedback goes into structured revision rounds, and guideline changes are tracked so later batches match the corrected ones.

Batch submission Feedback loop Revision cycles
6

Final Delivery & Support

Outputs are delivered in your required format through secure transfer. We also support active learning pipelines, retraining cycles and longer annotation engagements.

Secure delivery Ongoing support
Security & Working Terms
🔒 Encrypted transfer and permission-scoped access
📋 NDA on every engagement
🔧 Platform-agnostic delivery
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Use Cases for Polyline Annotation

Here are polyline projects we have delivered across lane detection, aerial mapping, utility inspection, retail layout and infrastructure.

🚗 Autonomous Driving · US

ADAS Lane & Curb Detection

Client Need: A US ADAS supplier was training perception models on footage shot in varied light and weather. They needed lane lines, curbs and road edges traced as polylines that held together from one frame to the next.
Solution: We wrote the lane-continuity rules together with their team, applied each region's marking conventions and tracked every polyline frame by frame, with topology checks on each batch. More than 1.2M frames were annotated.
  • Polylines held up across light and weather conditions
  • 38% fewer frame-continuity errors
  • All 1.2M+ frames delivered on schedule
🗺️ GIS & Mapping · EU

Aerial Roofline & Boundary Extraction

Client Need: A European mapping company wanted building rooflines and parcel boundaries extracted from drone imagery, so it could automate property assessment and urban planning work.
Solution: Annotators traced each image in tiles to an agreed vertex spacing, and we checked every layer separately before exporting GeoJSON. The project covered 50K+ aerial images.
  • Output loaded straight into the assessment pipeline
  • 50K+ drone images annotated and delivered
⚡ Utilities & Energy · UK

Powerline & Pipeline Tracing

Client Need: A UK energy company was building predictive-maintenance models from drone footage. Overhead power cables and pipeline corridors had to be traced reliably, even where the lines are very thin.
Solution: We wrote separate thin-line guidelines, trained a group of annotators on cable and pipeline imagery and reviewed their work at high zoom. Files went out in the export schema the client specified.
  • Thin cables and pipelines traced at high zoom
  • Delivered in the client's specified export schema
🌾 Agriculture · APAC

Field Boundary & Irrigation Mapping

Client Need: An agri-tech platform in APAC needed field boundaries and irrigation channels traced from drone imagery to feed its crop yield prediction models.
Solution: Annotators who work on agricultural imagery followed field-edge vertex rules and an irrigation class taxonomy we set up with the client. We delivered the finished boundaries across large drone survey datasets.
  • Yield model trained on cleaner boundary data
  • Delivery covered large drone survey datasets
🏙️ Infrastructure · Middle East

Road Network & Route Annotation

Client Need: A transport authority in the Middle East needed road edges, lanes and routes brought up to date for the GIS platform covering its metro region.
Solution: We traced the road network with geo-referencing, ran multi-layer QC and topology validation, and exported vectors in the formats their live GIS systems use.
  • Faster GIS platform update cycle
  • Topology-checked vectors delivered
🛒 Retail AI · Global

Aisle Path & Customer Flow Mapping

Client Need: A global retail chain wanted customer movement paths marked on CCTV footage to drive heatmap analytics and store layout decisions.
Solution: Annotators followed each path with sequential polylines, labeled the flow patterns and applied layout-based path classes across 200+ stores. Output came as timestamped CSV and JSON. The process behind this work is in our guide to retail data annotation workflows.
  • Customer paths labeled consistently across stores
  • Timestamped CSV and JSON delivered
  • 200+ store locations covered

Tools, Formats & Secure Transfer

We work inside your existing tools, or we can recommend a stack for your project. Our annotators are trained on CVAT, Labelbox, SuperAnnotate, V7, Label Studio and other major platforms, so there is no lock-in and no re-tooling.

🖥️Annotation Platforms
CVAT (Computer Vision Annotation Tool) Labelbox Roboflow Annotate SuperAnnotate Label Studio V7 Darwin Custom / In-house Tools
📁Export Formats
GeoJSON (geometry & attributes) Shapefile (ESRI format) COCO-style JSON CSV LabelMe JSON TFRecord (TensorFlow) YOLO (TXT) Custom schema on request
🤖ML Frameworks & GIS Tools
PyTorch / TorchVision TensorFlow / Keras OpenCV pipelines QGIS / ArcGIS compatible
🔒Secure Transfer
Encrypted SFTP AWS S3 (private bucket) Google Cloud Storage Azure Blob Storage Secure client portals Encrypted email delivery
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Why Choose Precise BPO

We have labeled polylines for AI teams since 2017, covering autonomous driving, HD maps, GIS vector mapping, utility cable tracing and medical contour work. All of it is done by our in-house team.

Start a Free Pilot →
⚡
18 Years in Business

Nine of those years have gone into line-based work, from simple lane markings to complex multi-vertex GIS polylines.

👥
540+ In-House Annotators

Our annotators are our own employees, not crowd workers, and they work in dedicated teams.

🔒
Security and Compliance

Access is permission-scoped, every annotator signs an NDA, and security audits run across project environments.

🎯
99.8% Accuracy

Three-tier QA combining topology validation, vertex-precision checks, peer review and expert audit, so geometry errors are caught before delivery.

💰
40–60% Below US/UK Rates

Delivery from India runs 40–60% below US/UK rates, and you get a free pilot batch before you commit.

🔧
Works in Your Tools and Formats

We annotate within your preferred tooling (CVAT, Labelbox, V7, SuperAnnotate) and deliver in whatever schema you define.

Annotators at work on polyline labeling at Precise BPO

Three-Tier QA Pipeline: How We Reach 99.8%

Every batch passes three QA tiers before delivery. The system catches vertex placement, topology and path continuity errors early, so defects do not carry through into your training data.

Tier 1 Annotator + Peer
→
Tier 2 Geometry Validation
→
Tier 3 Expert Audit + Delivery
T1

Annotator Self-Check & Peer Review

The annotator checks their own work first, then a senior peer cross-checks it. This catches vertex placement errors, path discontinuities, class mismatches and guideline deviations before any automated scoring.

Annotator reviews vertex placement, path continuity and class assignment against project guidelines before submitting
Senior annotator cross-checks: topology consistency, line direction and multi-class label correctness across the batch
Batches that fail the T1 threshold are returned for correction before advancing to T2
T1 Exit Accuracy Target95%+
Path Continuity Compliance97%+
T2

Automated Geometry Validation & Topology Check

Automated checks validate polyline geometry and vertex density, flag broken or overlapping paths and surface statistical outliers for human re-review.

Geometry scoring is run against reference annotations, with vertex precision checked against project-specific tolerance thresholds
Topology validation: broken paths, self-intersecting polylines and dangling nodes flagged and returned for correction
Statistical outlier scan: anomalous vertex density, path length or class distribution flagged for human review
T2 Exit Accuracy Target98%+
Mean geometry score vs. reference (0–1; separate from accuracy)0.97
T3

Expert Audit, Client Review & Delivery

The QA lead conducts random sampling, plus full-batch review, on high-stakes projects. Client feedback loops are built in: corrections are applied and re-verified before final sign-off and delivery.

Random sampling audit: the QA lead reviews 10–20% of frames per batch (100% on GIS / safety-critical polyline projects)
Client sample review: 50–100 annotated images or frames delivered for client acceptance before the full batch proceeds
Iterative feedback: corrections applied, re-scored through the T2 pipeline and re-delivered with a full audit trail
Final Delivery Accuracy99.8%

Accuracy Benchmarks

Precise BPO (internal QC data)99.8%
Typical vendor average (our estimate)~94%
Crowd-sourced platforms (our estimate)~82%

Capacity & Volume

Images / Day (Peak)200K+
Polylines Labeled (Total)38M+

In-House Team vs. Generic BPO vs. Precise BPO

A side-by-side look at the three ways teams usually handle polyline labeling, for AI leads, ML engineers and procurement teams making the case for outsourcing. In-house and generic BPO figures are our own estimates.

Criteria In-House Team Generic BPO Precise BPO
Annotation Accuracy 85–92% (fatigue, limited geometry QC) 90–94% (inconsistent topology checks) ✔ 99.8% — 3-tier geometry pipeline
Setup Time 6–10 weeks (hire, train, tool) 3–5 weeks ✔ Pilot-ready in days, no hiring or tooling
Scalability for Surge Volumes ❌ Fixed headcount, slow ramp ⚠ Limited, delays common ✔ 540+ team, fast ramp-up
Cost vs US/UK Rates Baseline (salary + infra) 25–35% savings ✔ 40–60% Below US/UK Rates
Security & Compliance ⚠ Varies by team ⚠ Varies by vendor ✔ Compliance-aligned workflows, NDA on every engagement
GIS & Geo-Referenced Output ⚠ Limited capability ⚠ Often not specialized ✔ Geo-referenced delivery in GIS formats
Video / Frame Polyline Tracking ⚠ Possible but slow ⚠ Varies by vendor ✔ Full temporal polyline tracking
Free Trial / Pilot No equivalent Varies ✔ Free pilot batch, no commitment
← Swipe to see full comparison →
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Pricing and Engagement Models

Pricing depends on volume, line complexity and timeline. There are no platform fees, and every engagement starts with a free pilot batch.

🖼️
Best for: Standard image batches
Per Image

Pay per annotated image. Suits fixed datasets and one-off projects where you want a predictable per-unit cost.

e.g. aerial mapping, lane detection datasets, GIS vector projects
🎬
Best for: Video annotation
Per Frame

Priced per video frame. Suits autonomous driving, CCTV movement tracking and dashcam lane work, where frame count is the natural unit.

e.g. ADAS lane datasets, retail path tracking, surveillance footage
⏱
Best for: Complex / dense data
Per Hour

Hourly pricing for high-complexity work such as thin powerline tracing, dense vertex requirements and multi-layer GIS features, where a per-image rate would not reflect the effort involved.

e.g. utility cable tracing, high-vertex GIS mapping, satellite imagery
🔄
Best for: Ongoing pipelines
Monthly Retainer

A dedicated polyline annotation team at fixed monthly capacity. Suits enterprises and AI labs with continuous labeling needs, active learning pipelines or production GIS update workflows.

e.g. active learning pipelines, HD map updates, quarterly model retraining
Volume discounts available from 50K+ images/month. White-label pricing for BPO partners.
All models include an NDA and a free pilot batch before you commit.
Get a Polyline Annotation Quote →

Delivery Across Time Zones

Our Pune delivery team works around the clock, so we can match your working hours in the US, UK, Europe, APAC, the Middle East, Australia, Canada and LATAM.

🇺🇸
North America
USA · Canada
EST/PST working hours
🇬🇧
United Kingdom
England · Scotland · Wales · Northern Ireland
GMT working hours
🇦🇺
Australia & NZ
Australia · New Zealand
AEST/NZST working hours
🇪🇺
Europe
Germany · France · Netherlands · Nordics
CET working hours
🌏
Asia-Pacific
Singapore · Japan · India · Southeast Asia
IST/SGT working hours
🌍
Middle East & Africa
UAE · Saudi Arabia · South Africa
GST/SAST working hours
🌎
Latin America
Brazil · Mexico · Argentina · Colombia
EST/CST working hours
🌐
Remote & Custom
Any region, any time zone

What Our Clients Say

What AI, GIS and mobility teams say after working with us.

★★★★★

"Precise BPO now runs lane annotation for our ADAS training data end to end. Lines stay continuous from frame to frame and the vertices land where they should. When we need more volume they add people without any fuss, and batch quality has held steady."

R
Rohan M.
ML Lead · Autonomous Vehicle Startup, US
★★★★★

"We gave them roofline and boundary annotation for about 300K satellite images. The files dropped straight into our GIS pipeline with no format problems, it came back quickly, and the quality was excellent."

L
Laura T.
GIS Director · Mapping Platform, EU
★★★★★

"Our retail movement models improved noticeably once annotation moved to Precise BPO. On our CCTV footage the paths came back as clean polylines with the right class tag on every frame, which is exactly what we needed."

J
James K.
Head of Computer Vision · Retail AI Platform, UK
★★★★★

"Powerline tracing across 120K drone images for our grid maintenance AI was a big job. Their thin-line guidelines made the difference, and the work arrived accurate and on schedule."

A
Anna S.
Data Science Lead · Energy Grid Company, Canada
★★★★★

"They work inside our own platform like part of our team. Four years in, they still hit their SLAs, and their accuracy is the best we have had from any outsourced annotation provider."

K
Kevin H.
CTO · AI Tooling Company, Australia
★★★★★

"We have relied on Precise BPO for HD map polyline updates for a long time. Accuracy holds steady, the cost works for us, and our mapping pipeline depends on them."

P
Pavel C.
Head of Data · HD Mapping Company, LATAM

Frequently Asked Questions

Answers to the questions teams usually ask before starting a project.

Polyline annotation is used to label linear features such as roads, lanes, boundaries, cables and pipelines in images or video. It is mainly used to train models that need to follow a line: lane detection in driving systems, boundary extraction in mapping, and cable or pipeline tracing in utilities. For the broader picture, our guide to data labeling covers the fundamentals.

We label aerial and satellite imagery, drone footage, dashcam video, CCTV footage and scanned maps. Any source that contains linear features such as lanes, edges, borders or paths can be annotated, although thin features like cables need high-resolution imagery.

Lines labeled with direction and connectivity let a model learn where a road continues, where a lane ends and how routes join. That feeds path planning, map generation and routing in autonomous driving, GIS platforms and mobility analytics.

We split large datasets into batches, each labeled under the same written rules and reviewed before the next one starts. This lets you scale volume, add data in stages and retrain over several months without the labels drifting.

Most of our polyline work comes from autonomous driving, digital mapping, utilities, infrastructure monitoring, logistics, agriculture and retail layout analysis.

Before labeling starts we agree written guidelines covering vertex rules, spacing and class definitions. Reviewers then check alignment, continuity and topology on samples from every batch, so similar features are labeled the same way throughout. See our annotation governance framework for how we enforce these standards on every project.

We typically deliver GeoJSON, Shapefile, CSV, COCO-style JSON or a custom schema. These load into QGIS and ArcGIS and into PyTorch and TensorFlow training pipelines, and the structure lets your team validate geometry before using the data.

Pricing depends on data volume, line complexity, vertex density and how deep the review needs to be. We quote per image, per frame, per hour or as a monthly retainer, and our rates typically run 40–60% below US/UK providers. If you are comparing providers, our comparison of data annotation companies puts pricing and strengths side by side. You can also see our data labeling pricing guide or request a tailored quote.

Our workflows are aligned with ISO 27001, HIPAA and GDPR. Every annotator signs an NDA before getting project access, roles are permission-scoped, and automated security audits run across all project environments.

Related Guides & Resources

Guides on annotation quality, pricing, vendor selection and industry workflows, for AI engineers, ML teams and data leads.

GET STARTED

Ready to Try a Free Pilot?

Start with a free pilot batch before you commit. Pilot and standard-size batches typically come back in 24–48 hours.

14

Tell Us About Your Project

Our in-house team handles polyline work for lane detection, GIS mapping, utilities and infrastructure AI. We can also run the wider data labeling work under the same engagement. Send us a request below for a free pilot or a project quote.

📞
Phone & WhatsApp
📍
Office
B3, Sant Namdev Marg, Varundavan Colony, Walhekarwadi, Gurudwara Colony, Nigdi, Pimpri-Chinchwad, Pune 411035, India

Request a Free Pilot

Get a response within 8 hours — no commitment required.

NDA on every engagement

🎯

Thank you. We have received your request.

Our team will review your requirements and respond within 8 hours.