The market for AI training data outsourcing companies in India keeps growing - not because it is trendy, but because it is necessary. Models need clean datasets, clear labeling rules, and steady quality control. Without that, training turns into guesswork.

This article covers the best companies in the AI training data outsourcing in India segment - from large operational providers to teams focused on human-in-the-loop work and evaluation sets. The outlook is simple: more models, more data, more verification.

1. NeoWork

NeoWork operates as a staffing and operations partner that helps teams scale without having to build every function internally first. Sometimes a client needs one extra teammate. Sometimes it’s a whole workflow that has to run every day, without drama. Our model covers both - individual contributors who join an existing team, and managed service teams where we take responsibility for reporting, quality checks, and day-to-day execution. Different setup. Same goal. Keep work moving.

A clear part of our offering is AI training, and this is where AI training data outsourcing becomes very tangible. We support projects that need data labeling, supervised fine-tuning support, evaluation sets, and reinforcement learning from human feedback. It’s the kind of work where instructions change, edge cases pile up, and consistency matters more than speed. We also provide AI training data outsourcing in India, and for some customers it’s the most practical way to extend their training pipeline without hiring locally.

Our team's competitive advantages: a 91% annual employee retention rate and a 3.2% candidate selection rate demonstrate stability and a more stringent selection process than most recruitment models - meaning clients experience less turnover, re-onboarding, and loss of context when work should be running smoothly and without interruptions.

Key Highlights: 

• Staffing support can be paired with managed operations, depending on how hands-on a client wants to be
• AI training work is covered, including labeling, evaluation datasets, and human feedback workflows
• Familiar business tools are used, which makes it easier to fold outsourced work into everyday routines

Services: 

• AI training data labeling and annotation for text, image, audio, and video
• Supervised fine-tuning support through human review and structured data preparation
• Evaluation sets creation and model output grading for iteration cycles
• Reinforcement learning from human feedback workflows and response comparisons
• Customer experience operations support for data-heavy or tool-driven environments
• Virtual assistants for admin-heavy tasks that sit around model and product teams

Contact Information:

• Website: www.neowork.com
• Facebook: www.facebook.com/neoworkteam
• LinkedIn: www.linkedin.com/company/neoworkteam
• Instagram: www.instagram.com/neoworkteam

2. Teleperformance

Teleperformance is a large-scale outsourcing provider that has expanded beyond classic customer operations into structured data work for machine learning. In AI training data delivery, the company tends to sit where volume meets process - long queues, clear rules, steady throughput. A typical engagement revolves around labeling and annotating text, images, audio, or video, plus the unglamorous tasks that make training sets usable: cleaning, deduping, formatting, and QA. There’s also overlap with trust and safety style workflows, which matters when datasets include user-generated content and require consistent judgment calls. For teams that need human-in-the-loop execution without building an internal labeling operation from scratch, this is the kind of provider that can run the work as a managed process.

What Makes Them Stand Out:

• Human-in-the-loop annotation for text, image, audio, and video datasets
• Operational setup built for high-volume queues and repeatable QA routines
• Experience with policy-led review work that often pairs with training data programs
• Workflow discipline around data handling, access control, and review traceability

Core Offerings:

• Data labeling and annotation for multimodal training sets
• Quality review and consensus checks for labeled data
• Dataset preparation - cleaning, normalization, and formatting
• LLM evaluation support - prompt-response review and output grading

Contact Information:

• Website: www.tp.com
• Email: connect@teleperformance.com
• Facebook: www.facebook.com/TeleperformanceGlobal
• Twitter: x.com/Teleperformance
• LinkedIn: www.linkedin.com/company/teleperformance
• Instagram: www.instagram.com/teleperformance_group
• Phone: 91 124 422 1050

3. Cognizant

Cognizant runs AI training data work as a structured service line, closer to an engineering-supported production process than a one-off labeling gig. The focus is often on turning messy, mixed-format data into something models can learn from, while keeping governance and oversight in view. Annotation is part of it, but so is curation, validation, and the kind of workflow design that keeps quality from drifting over time. It suits programs where the dataset evolves, the instructions change, and the operation has to keep up without collapsing into chaos.

A noticeable detail is the “human + tech” setup - people doing judgment-heavy tasks, with tooling used to route work, enforce checks, and measure consistency. That combination can matter for enterprise teams working with sensitive domains where label definitions are nuanced and edge cases are the norm. The work can also extend into model-support tasks like evaluation datasets, error analysis inputs, and feedback loops that inform retraining. For organizations building or tuning models at scale, this tends to look like a continuing pipeline rather than a short sprint.

Why They’re Worth A Look:

• Technology-enabled annotation workflows with human review built in
• Emphasis on validation steps and repeatable quality measurement
• Support for dataset curation and refinement as requirements change

Services Include:

• Multimodal data annotation and labeling operations
• Training dataset curation, filtering, and enrichment
• Gold set creation and QA auditing for label accuracy
• Model evaluation data preparation and feedback loop support

Contact:

• Website: www.cognizant.com
• E-mail: inquiry@cognizant.com
• Facebook: www.facebook.com/Cognizant
• Twitter: x.com/cognizant
• LinkedIn: www.linkedin.com/company/cognizant
• Instagram: www.instagram.com/cognizant
• Address: 18th to 21st floors, Pragya II, Block 15, C1 - Zone #1, Road No.11, Processing Area, GIFT SEZ, GIFT CITY, Gandhinagar, Pincode – 382 355. Gujarat India
• Phone: 1800 208 6999

4. Capgemini

Capgemini positions data labeling as an end-to-end operational capability rather than a narrow microtask layer. The way it usually shows up in real work is straightforward: set up labeling workflows, define quality gates, then run production with enough control that outputs stay consistent week to week. Not fancy. Just steady. For AI training data outsourcing, that “process-first” approach is useful when multiple teams touch the same dataset and nobody wants ambiguity about what “correct” means.

A big part of the value is how the work is organized: task design, reviewer structure, escalation paths, and documentation that doesn’t vanish after kickoff. That matters when labeling rules keep evolving, especially with multimodal sets where edge cases multiply fast. The delivery can include both initial labeling and ongoing maintenance, which is often where projects quietly succeed or quietly fail. If a client needs the work to be auditable and repeatable, the service model leans in that direction.

There’s also a practical emphasis on readiness - turning raw inputs into training-ready assets, not just pushing labels onto files. That includes handling different data types, keeping datasets structured, and maintaining quality checks that don’t depend on a single “hero reviewer.” In day-to-day operations, the goal is predictability: defined steps, measured quality, and clean handoffs to model teams. Simple, but not trivial.

Standout Qualities:

• End-to-end labeling operations with defined workflows and quality gates
• Process design that supports scale without losing labeling consistency
• Support for ongoing dataset upkeep as guidelines and edge cases evolve

What They Offer:

• Data labeling operations across text, image, audio, and video assets
• Workflow setup for annotation, review, and exception handling
• Quality control design - sampling, adjudication, and validation checks
• Dataset preparation support - structuring, cleaning, and training-ready packaging

Contact Information:

• Website: www.capgemini.com
• Facebook: www.facebook.com/Capgemini
• LinkedIn: www.linkedin.com/company/capgemini
• Instagram: www.instagram.com/capgemini
• Address: Divyasree TechPark SEZ (B4, B5, A5 & A6) IT/ITES, Doddanakundi Post Kundalahalli, Whitefield, Bengaluru, Karnataka– 560037, India
• Phone: +33 1 47 54 50 00

5. Wipro

Wipro operates as a broad IT and business services provider, and its training-data work usually shows up as structured data annotation delivered through managed operations. The practical focus is straightforward: take raw inputs (text, images, audio, video), apply consistent labels, then run quality checks so the dataset can actually be trusted. Some programs lean heavily on computer vision tagging, others sit closer to language work - classification, entity marking, intent labeling, and the kind of repetitive review that quietly determines whether a model learns the right thing. There’s also a “pipeline” mindset here: cleaning, normalization, and governance steps often sit next to the annotation itself, so the output is training-ready rather than just “labeled.” It’s not a one-person craft setup. It’s process work, run at scale, with room for change requests when guidelines inevitably get rewritten midstream.

What They’re Good At:

• Managed data annotation designed for repeatable production work
• Quality checks that reduce label drift across large datasets
• Support for multiple data formats including text and multimedia inputs

Services Cover:

• Multimodal labeling for model training datasets
• Annotation quality review and adjudication workflows
• Dataset cleaning, normalization, and formatting support
• Human-in-the-loop review for edge cases and ambiguous items

Contact Information:

• Website: www.wipro.com
• E-mail: info@wipro.com
• Facebook: www.facebook.com/WiproLimited
• LinkedIn: www.linkedin.com/company/wipro
• Instagram: www.instagram.com/wiprolimited
• Address: Doddakannelli, Sarjapur Road, Bengaluru - 560035
• Phone: +91 (80) 61427999

6. Tata Consultancy Services

Tata Consultancy Services is known for large enterprise delivery, and its training-data support tends to connect to wider AI and data programs rather than living as a standalone “labeling shop.” The work often revolves around building dependable workflows: task design, review layers, escalation rules, and measurement, so annotation stays consistent as volumes climb. Short version: fewer surprises. More control.

Another angle is the way annotation can sit beside responsible AI routines, where the same discipline used for model governance also applies to how datasets are built and checked. That matters when labels involve judgment calls, not just drawing boxes. In some cases, the effort looks like evaluation data creation - curated sets, gold standards, and structured review of model outputs - because training data and testing data usually grow up together. It’s a steadier, systems-oriented style of outsourcing, less “gig queue” and more “managed production line,” for better or worse.

Why People Choose Them:

• Workflow design that keeps annotation rules consistent over time
• Support for evaluation datasets and structured human review cycles
• Processes that align with governance and oversight expectations
• Ability to combine data work with broader AI delivery programs

What They Offer:

• Annotation workflow setup with review and escalation steps
• Human review for nuanced labeling and policy-led decisions
• Model output evaluation datasets and grading frameworks
• Data preparation support for training-ready dataset packaging

Contact:

• Website: www.tcs.com
• Email: india.marketing@tcs.com
• Facebook: www.facebook.com/TataConsultancyServices
• Twitter: x.com/TCS
• LinkedIn: www.linkedin.com/company/tata-consultancy-services
• Instagram: www.instagram.com/tcsglobal
• Address: MIHAN New, Nagpur, Telhara, Maharashtra 441108, India

7. Infosys

Infosys, through its process and digital operations side, puts a clear emphasis on managed data annotation services aimed at freeing up internal data science teams. That framing says a lot. The expectation is ongoing work, not a quick labeling sprint. And yes, it usually includes a mix of annotators plus subject-matter input when labels get tricky.

A common pattern is coverage across computer vision and language workloads. Think image tagging and bounding decisions on one end, then text classification, entity marking, and document-style labeling on the other. Different shapes of data, same core problem: consistency. Small differences in interpretation add up fast when a model sees millions of examples.

What also stands out is the attention to production hygiene. Setup, guidance, QA routines, and rework loops are treated as part of the service, not as an afterthought. That’s helpful when datasets change over time, because they always do. New edge cases appear. Old labels stop making sense. The work needs to keep moving anyway.

Standout Qualities:

• Managed annotation delivery supported by structured QA routines
• Coverage across computer vision, language, and classification tasks
• Ability to set up projects with clear guidelines and review flow

Their Focus Areas:

• Data annotation for text, images, audio, and video inputs
• Quality validation, spot checks, and consensus review steps
• Dataset curation and refinement for training readiness
• Human-in-the-loop evaluation sets for model testing and iteration

Contact Information:

• Website: www.infosys.com
• Facebook: www.facebook.com/Infosys
• Twitter: x.com/Infosys
• LinkedIn: www.linkedin.com/company/infosys
• Address: Plot No. 44/97 A, 3rd cross, Electronic City, Hosur Road, Bengaluru - 560 100
• Phone: +91 80 2852 0261

8. eSparkBiz

eSparkBiz works as a software engineering partner with a visible tilt toward AI buildouts, especially when a product needs extra hands for the messy middle part of model work. Not just model code - the data work around it. The company’s setup often looks like staff augmentation or managed delivery, where data annotation specialists can be added alongside ML engineers and QA to keep datasets consistent as requirements shift. For training data programs, the practical emphasis is on preparation and readiness: cleaning inputs, shaping schemas, defining label rules, and building review loops so labels do not drift over time. When teams are moving fast, this kind of support can prevent the “labels are fine, probably” problem that shows up right before training starts. Small detail, big impact.

Key Points:

• Flexible engagement models that fit ongoing labeling operations
• Support for integrating data annotation roles into delivery teams
• Focus on data preparation steps that make datasets usable for training

What They Offer:

• Data annotation support through managed teams or staff augmentation
• Labeling guideline setup and iteration as datasets evolve
• Data preparation - cleanup, normalization, and formatting
• Human review layers for edge cases and inconsistent labels

Contact Information:

• Website: www.esparkinfo.com
• E-mail: career@esparkinfo.com
• Facebook: www.facebook.com/esparkbiz
• Twitter: x.com/esparkbiz
• LinkedIn: www.linkedin.com/company/esparkinfo
• Instagram: www.instagram.com/esparkbiz
• Address: 1001 - 1009, 10th floor, City Center 2, Science City Rd, Sola, Ahmedabad, Gujarat - 380060
• Phone: +91 9023728517

9. Genpact

Genpact approaches training data work through its broader data and AI services, with an operations-heavy style that leans on process control and repeatability. A lot of the value sits in the unglamorous parts: getting data into workable shape, defining review steps, and keeping quality stable when volumes spike. In practice, that can mean large annotation queues for text, images, or audio, plus structured validation so the output does not collapse into noise. It can also extend into trust and safety style review work, which tends to overlap with training datasets when content is sensitive or subjective.

Another thread is human review for model performance, where the work shifts from “label this” to “judge this output” and document what is going wrong. That’s useful for evaluation datasets, RLHF-style feedback, and ongoing tuning cycles where rules keep changing. The company also talks about human-in-the-loop approaches in generative AI delivery, which fits well when automated extraction needs a real person to confirm accuracy. It’s a pragmatic model: people, workflows, measurements, and a steady feedback loop.

Why They’re Worth A Look:

• Operations-led delivery suited for large, rule-driven labeling workflows
• Support for human review and evaluation datasets tied to model tuning
• Experience with content-led review processes that often pair with training data
• Structured methods for data readiness, validation, and quality control

Services Include:

• Data labeling and annotation for structured training datasets
• Human review and output grading for model evaluation cycles
• Dataset curation, cleaning, and quality validation routines
• Trust and safety style review support for sensitive or subjective content

Contact Information:

• Website: www.genpact.com
• E-mail: DPO.Genpact@Genpact.com
• Facebook: www.facebook.com/ProudToBeGenpact
• Twitter: x.com/genpact
• LinkedIn: www.linkedin.com/company/genpact
• Address: Tower 5A, 1st Floor Pritech Park SEZ Bellandure Village, Outer Ring Road Varthur, Bangalore - 560037 Karnataka
• Phone: +91 80 39467601

10. HCLTech

HCLTech frames training data outsourcing as part of AI engineering delivery, where annotation is treated like a real production capability, not a side task. On its AI services pages, the company explicitly calls out annotation and labeling for creating training datasets, with coverage across different data types. That matters because the data rarely arrives in one neat format. It shows up as mixed media, partial metadata, and a pile of edge cases that do not fit the first draft of the guidelines.

Another angle is broader data engineering support sitting next to the labeling work. Some clients do not just need labels - they need pipelines that move data through preparation, review, and release without breaking every week. In that setup, annotation becomes one step in a longer chain: prepare, label, validate, package, hand off. Clean handoffs are underrated.

There is also a synthetic data thread in the AI and ML services area, positioned as a way to address training data gaps for both structured and unstructured inputs. That can be relevant when real-world samples are limited, sensitive, or too expensive to collect at scale. Even then, synthetic data still needs checks and clear definitions, or it becomes its own source of noise. So the work ends up being part engineering, part operations, part careful judgment.

What They Focus On:

• Annotation and labeling capabilities described as part of AI engineering services
• Support for training data across varied formats and data types
• Links between labeling work and data engineering practices for stability

Service Areas:

• Training dataset annotation and labeling across multiple data types
• Quality checks, validation routines, and review workflows for labeled data
• Data preparation support to keep datasets structured and training-ready
• Synthetic data generation and related training data readiness activities

Contact Information:

• Website: www.hcltech.com
• Facebook: www.facebook.com/HCLTechOfficial
• Twitter: x.com/hcltech
• LinkedIn: www.linkedin.com/company/hcl-technologies
• Instagram: www.instagram.com/hcltech
• Address: Technology Hub, SEZ Plot No. 3A, Sector 126 | Noida – 201304, India

11. IBM

IBM works across enterprise AI software and services, and its training-data support often comes through a mix of tooling, consulting delivery, and managed workflows that keep labeled data consistent. The company publishes detailed guidance on data labeling and human-in-the-loop practices, which shows up in real projects as structured annotation rules, review layers, and feedback loops. Some teams use IBM’s platforms to organize text annotation and evaluation tasks, especially when the work involves entities, relationships, and domain-specific language. It can look very practical. Define the schema, label the data, validate it, then package it so model teams can train without second-guessing every file. When model outputs need human scoring or comparison, that same operational approach can extend into evaluation datasets and iterative tuning cycles.

What They Focus On:

• Human-in-the-loop workflows for labeling and review work
• Tools and processes that support consistent text annotation projects
• Structured approaches to training data preparation and validation

Services Cover:

• Training data workflow design and annotation operations support
• Text annotation setup for entities, relations, and domain terms
• Human review cycles for model output assessment and refinement
• Dataset preparation - cleaning, structuring, and quality validation

Contact Information:

• Website: www.ibm.com
• E-mail: rccindia@in.ibm.com
• Twitter: x.com/ibm
• LinkedIn: www.linkedin.com/company/ibm
• Instagram: www.instagram.com/ibm
• Address: IBM India Pvt Ltd No.12, Subramanya Arcade, Bannerghatta Main Road, Bengaluru India - 560 029
• Phone: +91-80-4011-4047

12. Anolytics

Anolytics is built around data annotation as a core service, with a menu that maps neatly to what model teams usually need: image, video, text, and audio labeling, plus classification work that keeps datasets tidy. The company also lists content moderation and data processing as part of its offering, which often matters when raw data comes with noise, duplicates, or sensitive material. The overall feel is service-first. Get a dataset in, apply rules, run checks, and keep moving.

On the generative AI side, Anolytics talks about work that fits model tuning tasks, including RLHF-style human feedback and red teaming support. That’s a different rhythm than classic bounding boxes. Shorter prompts, more judgment, more edge cases. The company also points to industry-specific annotation, like medical data labeling and product categorization, which suggests the workflows are not limited to one generic template.

Why They Stand Out:

• Service catalog covers image, video, text, and audio annotation
• Data processing and moderation tasks offered alongside labeling work
• Generative AI support described through human feedback and testing work
• Industry-oriented workflows such as medical labeling and catalog taxonomy

What They Offer:

• Image annotation for computer vision training datasets
• Video labeling with frame-level review and consistency checks
• Text annotation for classification, extraction, and language datasets
• Human feedback tasks for model tuning and response evaluation

Contact Information:

• Website: www.anolytics.ai
• E-mail: info@anolytics.ai
• Twitter: x.com/anolytics
• LinkedIn: www.linkedin.com/company/anolytics
• Address: A-83, Sector-2, Noida, Uttar Pradesh 201301, India
• Phone: +1 516-342-5749

13. iMerit

iMerit focuses on turning raw inputs into structured data that model teams can actually use. That sounds simple. It rarely is. The work spans computer vision and language tasks, plus content services that handle extraction, enrichment, and validation.

A big part of the offering sits in generative AI support, where annotation shifts into judgment-heavy work like RLHF, prompt and response generation, and model evaluation. This is the stuff where guidelines change midweek and reviewers need a clear escalation path. It also includes red teaming, which is less about labeling objects and more about stress-testing behavior and documenting failures. Different muscle.

Another practical piece is the platform angle, with self-serve tooling for smaller projects and managed delivery when workloads get larger or more complex. iMerit also lists purpose-built applications for specialized annotation, including healthcare-oriented workflows. That’s usually where quality rules get tight fast. 

What Makes Them Unique:

• Coverage across computer vision, NLP, and content-focused data services
• Generative AI services include RLHF, evaluation, and red teaming workflows
• Mix of managed delivery and platform-based project setup options
• Purpose-built annotation applications for specialized domains

Core Offerings:

• Data annotation for images, video, and complex vision tasks
• Text labeling for extraction, classification, and language datasets
• Human feedback programs for model alignment and evaluation cycles
• Prompt and response data creation for fine-tuning and testing

Contact Information:

• Website: imerit.net
• E-mail: info@imerit.net
• Facebook: www.facebook.com/iMeritTechnology
• Twitter: x.com/iMeritDigital
• LinkedIn: www.linkedin.com/company/imerit
• Instagram: www.instagram.com/imeritdigital
• Address: Vishnu Chambers, 4th Floor, Block GP, Sector V, Salt Lake Kolkata 700091, India
• Phone: +91 33 4004 1559

14. Cogito Tech

Cogito Tech focuses on building training-ready datasets through managed data annotation and labeling. Simple idea, lots of detail. The work typically spans text, image, video, and audio - with different annotation methods depending on what a model needs to learn. For language projects, the emphasis leans toward tasks like sentiment tagging, intent labeling, named entity work, and other structured metadata that makes messy text usable. On the vision side, projects often involve object detection and segmentation-style labeling, where consistency matters more than speed. Cogito Tech also describes support for LLM-oriented data work, including prompt-response pairs and human rating workflows used during tuning.

What Makes Them Stand Out:

• Managed annotation across text, image, audio, and video datasets
• NLP labeling coverage across common tasks like NER, sentiment, and intent tagging
• Computer vision annotation methods including detection and segmentation styles

Core Offerings:

• Text annotation for classification, extraction, and language understanding tasks
• Image labeling for detection, segmentation, and visual model training
• Video annotation across frames with consistency checks and review steps
• Prompt-response dataset support with human evaluation for model refinement

Contact:

• Website: www.cogitotech.com
• E-mail: info@cogitotech.com
• Facebook: www.facebook.com/CogitoLimited
• Twitter: x.com/cogitotech
• LinkedIn: www.linkedin.com/company/cogito-tech-ltd
• Address: A-83, Sector-2, Noida, Uttar Pradesh 201301, India 
• Phone: +1 516-342-5749

15. Shaip

Shaip is built around the supply side of model data - collecting it, labeling it, and keeping it usable once reality gets in the way. That means text, audio, image, and video work, plus the operational routines that keep quality from drifting. A lot of programs start with collection or sourcing, then move into annotation, where guidelines, QA checks, and escalation rules become the real product.

The company also describes work that fits modern LLM pipelines, including human evaluation services and feedback loops used for tuning. Another practical piece is data de-identification, which matters when datasets include sensitive fields and cannot be handled casually. It’s the kind of vendor that tends to sit between raw inputs and the final training package, doing the careful cleanup and verification in the middle. Quiet work. Necessary work.

Why They’re Worth A Look:

• End-to-end coverage from data collection through annotation and review
• Support for multimodal datasets across text, audio, image, and video
• Human evaluation services used for tuning and model behavior checks
• Data de-identification workflows for handling sensitive training inputs

Their Focus Areas:

• Data collection programs for building domain-specific training sets
• Data annotation for NLP, computer vision, and mixed-format datasets
• Human evaluation and feedback tasks for model tuning cycles
• Text and image de-identification support for privacy-sensitive datasets

Contact Information:

• Website: www.shaip.com
• E-mail: marketing@shaip.com
• Facebook: www.facebook.com/weareshaip
• Twitter: x.com/weareShaip
• LinkedIn: www.linkedin.com/company/Shaip
• Instagram: www.instagram.com/weare_shaip
• Address: Atal-Kalam Research Park for Industrial Extension & Research (PIER), Opp. GUSEC, Ahmedabad, Gujarat 380009, India 
• Phone: (866) 473-5655

Conclusion

AI training data outsourcing companies in India have become a steady support layer for teams training and refining models. It works well, but only when delivery stays predictable. Before starting, it helps to check basics: how labeling rules are documented, who resolves edge cases, how quality is measured, how data is handled, and what happens when requirements change midstream.

The service will keep evolving because demand is rising for evaluation sets, human validation, and careful improvement loops. That is why vendor selection in this segment is not a one-week decision - it is tied to the model’s working lifecycle