Top Computer Vision Annotation Companies In 2027

Meta Description: Compare the top computer vision annotation companies for 2027, with HQ, LiDAR and 3D capability, tool stacks and security credentials for each provider.

Computer vision models are only as reliable as the labels they learn from. A perception stack for an autonomous vehicle, a picking robot or a drone survey pipeline needs millions of consistent bounding boxes, polygons, segmentation masks, keypoints and 3D cuboids, and it needs them to hold up on the rare frames where the model is most likely to fail. Most teams reach a point where in-house labeling cannot keep pace with data collection, and the question becomes which outside partner can take on the work without lowering quality.

This guide covers managed annotation service providers that specialize in image, video, LiDAR, 3D point cloud and sensor-fusion data. It is written for computer vision engineers, perception leads, robotics and AV teams, and founders who need a labeling partner rather than another software license. If you are looking for a broader view of the market, including text, audio and LLM data vendors, see our list of data annotation and labeling companies. Pure annotation software platforms are covered separately.

Each company below was checked against its own website and, where needed, reputable third-party sources. We looked at headquarters and delivery locations, workforce model, named annotation types, 3D and multi-sensor capability, the tools each team works in, and stated security certifications. Where a detail could not be confirmed, we left it out.

Computer Vision Annotation Companies At A Glance

Company	Headquarters	Best For	Core Services
Keymakr	New York City, USA	Teams wanting an in-house, on-site annotation workforce	Image, video and 3D point cloud annotation, semantic segmentation, edge-case annotation
Label Your Data	Wilmington, Delaware, USA (EU office in Nicosia, Cyprus)	Flexible pricing and a free pilot before committing	Bounding boxes, polygons, keypoints, 3D cuboids, LiDAR, video tracking
Deepen AI	Santa Clara, California, USA	AV and robotics programs with multi-sensor data and calibration needs	LiDAR, camera and radar fusion labeling, point cloud segmentation, tracking, sensor calibration
Mindkosh	New Delhi, India	Multi-sensor robotics and AV data with accuracy SLAs	LiDAR and point clouds, sensor fusion, cuboids, segmentation, tracking
Digital Divide Data	Cambodia, Kenya and USA operations	Regulated AV work needing SOC 2 Type 2 and ISO 27001	2D and video annotation, LiDAR, radar, multisensor fusion
Humans in the Loop	Sofia, Bulgaria (founding location)	Impact-driven teams working in established labeling tools	Bounding boxes, polygons, keypoints, segmentation, 3D cuboids
Sama	San Francisco, California, USA	Large-volume ADAS programs needing proven 3D workflows	2D image and video, 3D LiDAR and radar point clouds, fused sensor labeling
Objectways	Arizona, USA and Tamil Nadu, India	Security-conscious enterprises using partner platforms	Bounding boxes, polygons, segmentation masks, keypoints, LiDAR, video tracking
AnnotationBox	Kolkata, India, Deerfield Beach, USA and London, UK	LiDAR projects that must run in a specific third-party tool	3D cuboids, point cloud segmentation, sensor fusion, HD map construction
Infolks	Mannarkkad, Kerala, India	Cost-conscious 2D and 3D labeling from a dedicated Indian team	2D and 3D bounding boxes, cuboids, polygons, keypoints, LiDAR

Best Computer Vision Annotation Companies In 2027

1. Keymakr — In-House Annotation Teams With Four Layers Of QA

Keymakr was founded in 2015 by Arie Zilberman, Michael Seldin and Michael Abramov, and lists its office at 99 Wall Street in New York City. The company built its reputation on computer vision work and says its annotation teams work together on-site, led by experienced team leaders and managers, rather than through a crowd marketplace. The in-house model helps keep guidelines consistent over long programs.

The service catalog covers image annotation, video annotation, 3D point cloud labeling and semantic segmentation, along with data creation, collection and validation. On its video service pages, Keymakr lists bounding boxes (including rotated boxes for angled subjects), 3D cuboids, polygons, keypoints with skeletal annotation, semantic and instance segmentation, lane and bitmask annotation, and object tracking across frames with interpolation. For autonomous driving, it offers edge-case annotation and review of LiDAR and camera fusion scenarios. Industries listed include automotive, aerial, agriculture, manufacturing, retail, robotics and security.

Projects run on Keylabs, a proprietary annotation platform that Keymakr originally created for internal use and later released as a separate product. Keymakr describes four layers of quality assurance and precision levels up to pixel-perfect, with task distribution based on annotator performance. Its site does not list formal security certifications, so teams with strict compliance needs should ask for documentation during scoping. Keymakr suits perception teams that want a stable, managed in-house workforce and are comfortable working inside the vendor’s own tool.

2. Label Your Data — Free Pilot And Per-Object, Hourly Or Project Pricing

Label Your Data was founded in 2020 by Daria Leshchenko and is now led by CEO Karyna Naminas. The company lists regional offices in Wilmington, Delaware for the US and Nicosia, Cyprus for the EU, three offices in total, and a team of roughly 1,000 people across 22 countries. That setup allows coverage beyond standard working hours.

For computer vision, the company works with bounding boxes, polygons, keypoints, 3D cuboids, skeleton annotation for pose estimation, object segmentation and tracking, and LiDAR and 3D point cloud data, including LiDAR and radar labeling for vehicle, pedestrian and road sign detection. Sectors include mobility, robotics and physical AI, geospatial, manufacturing, retail and defense tech. The team works within a client’s preferred annotation platform and also offers its own platform for computer vision tasks.

Label Your Data states that it is ISO 27001 certified and describes GDPR-compliant workflows. Pricing is one of its clearest differentiators: it quotes hourly, per-object, per-task or project-based rates, with no setup fees and no long-term contracts. It also runs a no-cost pilot on the client’s own data, and its 3D service page says the first annotation is free. That combination makes Label Your Data a practical choice for startups and mid-size teams that want to validate quality and cost on real data before signing a larger volume agreement.

3. Deepen AI — Multi-Sensor LiDAR, Camera And Radar Labeling With Calibration

Deepen AI is headquartered in Santa Clara, California and was founded in 2017. Unlike most firms on this list, it pairs a managed service with its own perception data tooling: “Annotate as a Service” provides SLA-backed labeling, while Deepen Annotate and Deepen Calibrate cover multi-sensor labeling, QA workflows and sensor calibration, including a targetless calibration approach.

The company’s annotation coverage is built around autonomous systems. It lists 2D and 3D bounding boxes, point cloud labeling, multi-class point cloud segmentation, pixel-accurate semantic scene parsing, and multi-object detection and tracking. It labels fused LiDAR, camera and radar data, which is the core requirement for most ADAS and robotaxi programs. Its stated markets are automotive and ADAS, robotics and embodied AI, and energy and industrial infrastructure. In 2025 it launched a unified annotation, calibration and validation suite.

Security and standards are a clear strength. Deepen lists ISO 27001, SOC 2 Type II and TISAX, and describes GDPR compliance, ASPICE readiness and UNECE R155 alignment. It is also an author of the ASAM OpenLABEL standard, which is relevant for teams that want labels in an interoperable automotive format. Deepen AI is best suited to AV, ADAS and robotics teams that need sensor-fusion labeling and calibration from a single vendor with automotive-grade compliance.

4. Mindkosh — LiDAR, Thermal And Depth Labeling With Free Re-Labeling Below SLA

Mindkosh AI was founded in 2020 and is based in New Delhi, operating as Mindkosh Technologies Private Limited. It offers both a labeling platform and fully managed annotation services, with the managed side focused on data from multi-sensor setups such as LiDAR, thermal cameras, depth cameras, RGB and multi-camera rigs.

The managed service lists more than 2,000 experienced labelers, each trained on AV and robotics data, and reports over 300,000 hours of labeling work in 2025. Supported work includes LiDAR and 3D point cloud labeling, sensor fusion with cuboid projection from 3D to 2D, cuboids, semantic segmentation, polylines, polygons, keypoints, bounding boxes, 3D mesh and object tracking across frames in both point clouds and images. It also offers PII removal for faces and license plates.

Mindkosh lists ISO 27001 and ISO 9001 certifications. Every batch goes through at least two levels of quality checks, and the company states accuracy of 96% or higher across labeling tasks. Its contracts include SLAs, and if delivered labels miss the agreed accuracy, the entire batch is re-labeled free of charge. Each engagement has a dedicated engagement manager, and a free pilot is available for teams evaluating a switch from another vendor. Mindkosh fits robotics and AV teams with unusual sensor stacks that want contractual accuracy guarantees.

5. Digital Divide Data — SOC 2 Type 2 Multisensor Fusion Annotation From Impact-Sourced Teams

Digital Divide Data (DDD) opened in Cambodia in 2001, co-founded by Jeremy Hockenstein, and is now led by CEO Sameer Raina. It pioneered the impact sourcing model of employing people from underserved communities, and its site says its workforce is 50% women and 95% economically and socially marginalized individuals. Delivery locations include Cambodia, Laos, Kenya and Madagascar, with client engagement teams in North America, Europe and Asia.

DDD’s computer vision practice covers bounding boxes, polygons, semantic and instance segmentation, keypoints, 3D cuboids, object tracking and 3D point cloud labeling. Data formats include images, video, LiDAR, radar, depth and fused multisensor data. Focus areas include ADAS, in-cabin AI, robotics, humanoids, defense, agriculture and geospatial intelligence. The company describes itself as platform agnostic and integrates with the client’s preferred technology stack, which removes the need to migrate data into a vendor tool.

Security is a strong point for regulated programs. DDD states that it is ISO 27001 certified and SOC 2 Type 2 certified, describes GDPR and HIPAA compliance, and says its framework aligns with TISAX requirements for automotive work. On quality, it describes multi-layer QA, inter-annotator agreement checks, validation sampling and performance metrics tied to model outcomes, combined with AI-assisted workflows. DDD suits automotive and defense-adjacent teams that need audited security controls, multisensor capability and a partner with a long operating history.

6. Humans in the Loop — Conflict-Affected Workforce Labeling In SuperAnnotate, V7 And Segments.ai

Humans in the Loop was founded in 2017 in Sofia, Bulgaria as a social enterprise that provides digital work to conflict-affected and displaced people. The company says it has employed more than 1,400 conflict-affected people and served over 120 AI companies, with active annotator teams in 12 countries. Named locations include Bulgaria, Iraq, Afghanistan, Lebanon, Turkey, Yemen, Ukraine, Moldova, Kenya and the Democratic Republic of the Congo.

Its computer vision services include bounding boxes, polygons, keypoints, semantic segmentation, video annotation and 3D annotation. For 3D work, the company uses cuboids as the most common method and voxels for semantic segmentation, and its automotive use cases include tracking vehicles in LiDAR point cloud data. It also offers medical annotation through its affiliated Doctors in the Loop team. Other sectors include geospatial, industrial, agriculture and retail.

A useful strength for engineering teams is tool flexibility. Humans in the Loop lists technical partnerships with Alegion, Diffgram, Hasty, Human Lambdas, Kili Technology, Lightly, Manthano, Segments.ai, SuperAnnotate and V7, so teams already using one of those platforms can bring the workforce to their existing setup. Its site does not list formal security certifications, so buyers should confirm data handling terms early. Humans in the Loop suits teams that want an ethical sourcing story and plan to keep labeling inside their own platform.

7. Sama — 3D LiDAR And Radar Point Cloud Labeling With Fused Sensor Support

Sama was founded in 2008 by Leila Janah and is based in San Francisco, operating as Samasource Impact Sourcing, Inc. It is a certified B Corp and says it has built careers for over 15,000 associates. In April 2026, Sama announced more than 1,000 job cuts at its Nairobi operation after Meta ended a contract, so buyers should ask about current delivery capacity and team continuity for their program.

Sama’s computer vision services cover 2D image annotation for object detection, localization and segmentation, 2D video annotation with object tracking and temporal attributes, and 3D point cloud annotation for LiDAR, radar and other 3D data. On the 3D side, it applies cuboids in both 2D and 3D, labels moving objects across multiple frames with linear interpolation, and supports multiple sensors with a fused sensor architecture. Features include pre-annotations, automatic ground detection and 3D-to-2D projection. Industries include ADAS and autonomous vehicles, food and agriculture, retail and e-commerce, and robotics and manufacturing.

The company reports a 99% first-batch client acceptance rate and describes an in-house workforce with average annotator tenure of more than two years, organized into industry-specific teams. It cites ISO-certified delivery centers and a biometric-secured platform. Sama is best for enterprise AV and robotics programs that need high-volume 3D throughput and established fused-sensor workflows.

8. Objectways — SOC 2 Type II And TPN Gold Annotation Across Partner Platforms

Objectways was founded in the United States in August 2018 by Ravishankar Rajalingam, who serves as CEO. It lists US offices in Phoenix, Mesa and Scottsdale, Arizona, and Indian offices in Karur, Coimbatore and Chennai in Tamil Nadu. The company reports more than 2,200 trained annotators and AI specialists and has stated plans to grow past 3,000.

For computer vision, Objectways lists bounding boxes, polygons, segmentation masks, keypoints for pose and motion tracking, line and spline annotation, LiDAR 3D point cloud labeling for spatial and object detection, and video annotation for object tracking and activity recognition. It also handles medical imaging and depth datasets for 3D scene understanding. Before full production, it runs a sample annotation pass to estimate output quality and finalizes guidelines with the client.

Objectways describes itself as tool-agnostic. Its listed partners include Encord, Amazon SageMaker, Segments.ai, Dataloop, Kili, Kognic and Databricks, which is useful for teams already committed to one of those environments. Its security credentials are among the broadest on this list: SOC 2 Type II, ISO 27001:2022, ISO 27701:2019, TPN Gold, GDPR and HIPAA. The company states accuracy above 99% backed by QA workflows. Objectways suits enterprises that need strong audit credentials and want annotators who can work directly inside an existing partner platform.

9. AnnotationBox — LiDAR Sensor Fusion Labeling In CVAT, Labelbox And Segments.ai

AnnotationBox lists offices in Kolkata, India, Deerfield Beach, Florida and London, UK, and describes its labelers as trained internal specialists rather than outsourced freelancers. Its 3D practice is where it stands out, with a detailed menu of point cloud services.

On the 2D side, it lists bounding boxes, polygons, polylines, 3D cuboids, keypoints and semantic segmentation, plus video object tracking, activity recognition and temporal segmentation. Its LiDAR service covers 3D cuboid annotation, semantic, instance and panoptic segmentation, point-level annotation, polyline and polygon annotation, object tracking across frames, sensor fusion with 2D and 3D linking between LiDAR and camera or radar data, and HD map construction.

Tool flexibility is a practical advantage. AnnotationBox names CVAT, Label Studio, Labelbox, Segments.ai and BasicAI as point cloud platforms it works in, offers its own 3D annotation platform on request, and exports to formats such as COCO, YOLO and PASCAL. It describes itself as a SOC 2 Type 1 organization, with GDPR-compliant workflows, HIPAA-compliant workflows for medical data and TLS 1.2+ encryption. Pricing is custom, based on complexity, volume and deadline, and projects start with sample labeling for client review. AnnotationBox suits LiDAR teams that have standardized on a specific open-source or commercial tool and need a workforce that can operate it.

10. Infolks — 2D And 3D Cuboid Labeling From A Kerala-Based Team

Infolks is an Indian data labeling company based at Infolks Tower in Mannarkkad, Kerala, founded in 2016 by Mujeeb Kolasseri, who serves as managing director and CEO. The company grew out of image annotation work for autonomous driving clients in Europe and the United States.

Its listed annotation techniques include 2D and 3D bounding boxes for object detection and localization, cuboids, polygons and contours, polylines, ellipses, keypoints and semantic segmentation. Data types include image, video and LiDAR annotation, alongside text, audio, product categorization and AR and VR labeling. Its industry list runs from automotive and retail to livestock, waste management and geospatial, which suits teams working outside the AV mainstream.

Infolks does not publish security certifications or a list of supported tools on its site, so teams should confirm both during scoping, along with data residency and the QA process for their project. Infolks is best for startups and mid-size teams that need dependable 2D and 3D labeling at a competitive cost and are willing to run a pilot to set quality expectations.

How To Choose A Computer Vision Annotation Company

Does The Vendor Handle The Annotation Types And 3D Data You Need?

Start with your data, not the vendor’s brochure. A provider that labels bounding boxes well may have little experience with panoptic segmentation of point clouds or with linking 3D cuboids to camera frames. If your stack includes LiDAR, radar, thermal or depth sensors, ask for examples of multisensor work and how the team handles calibration offsets, occlusion and sparse points at long range. Vendors such as Deepen AI, Mindkosh, DDD and AnnotationBox explicitly describe sensor-fusion capability; others focus mainly on 2D and video. Data collection is a separate discipline, so if you also need new footage, compare dedicated video data collection companies alongside your labeling shortlist.

Can They Work In Your Annotation Tool?

Moving data into a vendor’s proprietary tool can be fast to start, but it adds export work and lock-in. If your team already uses a platform such as CVAT, Labelbox, Segments.ai, SuperAnnotate, V7 or Encord, prioritize providers that can operate inside it. Ask which ontologies and export formats they have delivered before. If you do use the vendor’s tool, confirm you will receive labels in a standard format such as COCO, YOLO or ASAM OpenLABEL.

How Do They Measure Quality And Handle Edge Cases?

Accuracy claims of 96% or 99% mean little without a definition. Ask how accuracy is measured (per object, per attribute or per frame), how many review layers each batch receives, and whether they track inter-annotator agreement. Edge cases are where perception models fail, so ask how annotators escalate ambiguous frames, how quickly guidelines are updated, and whether there is a re-labeling commitment if a batch misses the agreed threshold. A vendor that offers a contractual SLA with free re-work gives you leverage when quality slips.

What Should A Pilot And Pricing Model Look Like?

Run a paid or free pilot on a representative slice of your hardest data, not your cleanest. Pricing usually follows one of three models: per object or per annotation, per hour of annotator time, or per frame or task. Per-object pricing is easier to forecast for dense scenes with known class counts, while hourly pricing is often fairer for complex 3D segmentation where effort varies widely. Ask for quotes in two models and compare them against pilot results before you sign a volume commitment.

What Security And Data Residency Controls Are In Place?

Camera and LiDAR datasets can include faces, license plates, private property and pre-release product designs. Ask for current certificates rather than badges: ISO 27001 and SOC 2 Type II are common baselines, while TISAX matters for many European automotive programs and TPN for media work. Confirm where annotators sit, whether data leaves your cloud environment and how access is revoked at project end. If you operate under GDPR or other regional rules, get data residency commitments in writing.

Conclusion

The right computer vision annotation partner depends on the shape of your data and how your team works. LiDAR-heavy AV and robotics programs will find the deepest multi-sensor capability at Deepen AI, Mindkosh, Digital Divide Data, Sama and AnnotationBox. Teams that want an in-house managed workforce and flexible commercial terms should look closely at Keymakr and Label Your Data, while Objectways stands out for audit credentials and partner-platform work. Humans in the Loop and DDD combine solid technical coverage with impact sourcing, and Infolks offers a cost-focused option for 2D and 3D labeling.

Whichever provider you shortlist, test it on your hardest frames, agree on how quality is measured before production begins, and confirm security controls in writing. A careful pilot will tell you more about a vendor’s real capability than any accuracy figure on its homepage.