Home › Data Labeling Services › Explicit Content Annotation
Trust & Safety Training Data · Image, Video & Text

Explicit Content
Annotation for Trust &
Safety Teams

Precise BPO Solution provides explicit content annotation services for AI moderation, trust & safety and NSFW detection models. Our in-house team of 540+ people in Pune, India has labeled 330M+ video frames since 2017, working under NDA with ISO 27001-aligned, HIPAA-aligned and GDPR-aligned workflows.

Get a Quote Full Data Labeling Portfolio
Explicit content annotation workflow for AI moderation, trust and safety, and NSFW detection at Precise BPO
Up to 99.8% Target Accuracy QC-validated
330M+ Video Frames Labeled Since 2017
540+ In-House Team All domains
24–48h Turnaround Standard batch
18+ Years in Business Since 2008
On This Page
01 What is Explicit Content Annotation 02 About the Service 03 Who We Work With 04 Image vs Video vs Text Moderation 05 Capabilities 06 Explicit Content We Annotate 07 Workflow 08 Recent Projects 09 Tools & Platforms 10 Why Precise BPO 11 QC Pipeline 12 Your Own Team vs Generic BPO vs Precise BPO 13 Pricing Models 14 Global Coverage 15 Testimonials 16 FAQ 17 Related Services 18 AI Annotation by Industry 19 Blog & Resources 20 Start a Free Pilot

Security and Working Terms

ISO 27001-aligned
HIPAA-aligned
GDPR-aligned
NDA on Every Project
Free Pilot Available
Works in Your Tools
The Fundamentals

What is Explicit Content Annotation?

Explicit content annotation is the manual review and classification of images, video and text to identify nudity, violence, hate symbols, NSFW material and other sensitive content. Each annotation, whether a severity tag, category label or contextual flag, gives AI moderation models the ground truth they need to detect policy-violating content and route it to the right enforcement action.

It is a core part of AI data labeling for trust and safety. The labels support moderation on social media and OTT platforms and train safe-search and brand safety models. Because the content is sensitive, reviewers have to apply context and cultural judgment consistently on borderline cases. That cuts false positives and keeps decisions defensible under platform policy.

The output is a per-item moderation record, usually delivered as JSON, CSV, XML or a custom schema, that feeds straight into enforcement systems, review queues and trust and safety dashboards.

Content Classification
Category and severity labels applied to images, video frames and text to train recognition and moderation models.
Severity & Context Tiering
Borderline, suggestive and high-severity content tiered for automated enforcement, escalation, or human-in-the-loop review.
Text & Metadata Review
Captions, usernames and metadata indicators flagged for adult or sensitive signals to support multimodal moderation.
Output Formats
Delivered as JSON, CSV, XML or a custom schema, ready to load into moderation and training pipelines.
About the Service

How We Run the Work

About Our Practice
In business since 2008. Labeling since 2017. One in-house team.
18+
Years in business
▲ Data labeling since 2017
540+
In-house team on staff
▲ Dedicated domain teams
ISO 27001-aligned GDPR-aligned NDA

Since 2017, we have labeled explicit content for social platforms, streaming and OTT services, gaming and UGC communities and AI safety teams, as part of our data labeling services. Every dataset is built to your policy specification.

Our reviewers specialize in trust and safety classification. They work from your severity taxonomy and written consistency rules, and they calibrate against each other regularly. Every batch goes through QC before delivery, and we adapt to your moderation tooling and output schema.

For trust and safety programs with high-volume review across millions of items, we label image, video and text against complex multi-policy taxonomies. Teams can ramp from pilot to production without building in-house review infrastructure. Batches arrive ready to load into your pipeline.

Some teams need ongoing annotation for continuous moderation queues; others need burst capacity during a policy update. Either way, we work inside your existing tools and there's no minimum commitment.

We can also run online data entry and data conversion in the same engagement, along with text and NLP annotation and data de-identification where the content needs it, so teams with several pipelines can work with one vendor.

Dedicated Domain Teams for Trust & Safety AI
Our 540+ in-house team includes reviewers specialized in content classification, processing millions of labels a month.
Severity & Category Accuracy
Three-tier QC with policy and consistency checks on every batch.
Security & Privacy
Permission-scoped access, NDA-bound staff and audit trails on every project.
Talk to Our Team
Industry Coverage

Who We Work With

We label moderation data for trust and safety teams, brand protection tools and AI platforms.

Social Media & UGC Platforms

Image, video and text classification datasets for content moderation, harassment detection and policy enforcement on user-generated content.

Streaming & OTT Platforms

Frame-level video review, age-rating verification and graphic-content flagging for catalog distribution and regional compliance.

Gaming & Virtual Worlds

Review of in-game chat, avatars and user-generated assets for harassment, age-appropriateness and platform safety.

E-Learning & Educational Platforms

Policy review of uploaded media and discussion threads on learning platforms.

AI & Machine Learning Companies

Training and evaluation datasets for safety classifiers and moderation products.

Advertising & Brand Safety Agencies

Datasets for ad-placement screening, brand-adjacency risk scoring and creative review, for brand safety AI that protects advertisers.

Legal, Regulatory & Compliance Bodies

Moderation records that are consistent across platforms and jurisdictions and easy to audit. We also key in case files and evidence logs through our legal document data entry service.

AI Data Vendors & Research Institutions

Multimodal image, video and text labels for trust and safety research and for vendors building datasets of their own.

Review Types

Image vs Video vs Text Moderation: When to Use Which

The review type you choose affects model performance and labeling cost. Pick the type that fits your content format, policy scope and pipeline.

Criteria Image Review Video / Frame Review Text & Metadata Review
Unit Reviewed Single still image or thumbnail Sequential frames sampled across a clip Captions, comments, usernames, tags
Best For Uploaded photos, profile media, ad creatives Streamed video, livestream clips, OTT catalog Comments, chat logs, listing descriptions
Review Speed Fastest: single-pass classification Moderate: frame-sampling workflow Fast: keyword and context scan
Cost Efficiency Highest: minimal effort per item Moderate: scales with run-time High: scales well with volume
Context Precision Item-level (single frame context) Temporal context across scene changes Linguistic & cultural context-aware
Precise BPO Service Image Moderation Annotation Video Moderation Annotation Text & NLP Annotation

Not sure which review type fits your project? Talk to our specialists and we'll recommend an approach based on your platform, content mix and policy requirements.

Capabilities

Our Capabilities

We classify and tier images, video and text for social, OTT, gaming and AI moderation pipelines, across multiple policies and high volumes.

Image Content Classification & Severity TieringWe classify images for nudity, violence, hate symbols, self-harm indicators and other explicit material. Each item gets a client-defined severity tier, and borderline, suggestive and high-severity items are routed to automated enforcement or human review.
Video Frame Review & Scene-Level FlaggingFrame-by-frame classification of explicit, violent and policy-violating content across video clips, livestream captures and OTT catalog, with scene-change detection and temporal context tagging.
Text & Metadata ReviewCaptions, comments, usernames, hashtags and listing descriptions flagged for adult or sensitive signals.
Three-Tier QC and Reviewer CalibrationReviewers calibrate against each other on sample items, and every batch passes three QC tiers before delivery.
Reviewer WellbeingHigh-severity content follows escalation protocols, and reviewers rotate off sensitive queues on a schedule.
Export FormatsDelivered as JSON, CSV, XML or custom schemas with severity tags, category labels and confidence scores, ready to load into enforcement systems and ML training pipelines.
Context & Cultural HandlingReviewers work from your written policy and cultural-context rules to tell violations from legitimate content. That reduces false positives and keeps decisions consistent across jurisdictions.
Guideline CustomizationWe write guidelines around your policy: category definitions, severity tiers and edge-case rules.
Get a Quote
Explicit content annotation capabilities showing classification tiers and the moderation pipeline for trust and safety AI
Explicit Content Annotation Capability Map Diagram showing explicit content classification workflow with severity tiers (borderline, suggestive, and high-severity) alongside content category labels for images, video frames and text, with QC pass badge and a target of up to 99.8% accuracy output. Below, a content-routing panel shows enforcement actions, human review queues, and export formats of JSON, CSV, and custom schema. Overall pipeline accuracy target is up to 99.8%.
Content Types

Explicit Content We Annotate

Our reviewers are trained on each platform's taxonomy and severity scheme, so labels stay consistent from batch to batch.

Uploaded Images & Photos
Per-image labels for nudity, violence, hate symbols and NSFW material, with a severity tier on each item.
Video Clips & Livestreams
Frame-level labels and scene-change flags across streamed video, OTT catalog content and UGC uploads.
Text Comments & Chat Logs
Labels for harassment, hate speech, toxic content and adult-indicator language in comments, chat messages, usernames and hashtags.
In-Game Assets & Avatars
Labels for user-generated game content, avatars and virtual-world assets, covering age-appropriateness and harassment.
Ad Creatives & Brand Media
Brand-adjacency and suggestive-content labels for ad creatives and placements.
Adult & Borderline Content
Tiered labels for borderline, suggestive and high-severity adult content.
Listings & Metadata Signals
Caption, listing description and metadata review for adult indicators, supporting multimodal moderation pipelines. Where listings contain personal data, we can de-identify them first.

Don't see your content type listed? Tell us about it. We set up the policy taxonomy and severity scheme during the pilot.

Process

Our Annotation Workflow

Most projects start with a pilot batch and move to production once the policy guidelines are agreed.

1

Requirements Review

We review your platform policies, content taxonomy, severity framework, dataset volume and review complexity, and agree on a labeling approach before any review begins.

Policy taxonomy Severity tiers Content schema SLA setup
2

Data Intake

Images, video clips and chat or caption text arrive over encrypted transfer. We deduplicate them before they enter the review queue.

Encrypted transfer Access control Batch intake Deduplication
3

Review & Classification

Specialized reviewers classify images, video frames and text using your severity taxonomy. They apply contextual rules and cultural-context checks and calibrate against each other, working in the annotation platform of your choice or our internal tooling.

Image classification Video frame review Text moderation Severity tiering
4

Quality Check & Validation

We run three-tier QC: policy-consistency checks, severity-accuracy audits, batch sampling and reviewer sign-off. Automated checks flag category mismatches for human review before delivery.

Consistency check Severity audit Sampling Reviewer sign-off
5

Client Review & Feedback Loop

You review a sample and we revise against your feedback, so labels stay aligned as your policies change.

Batch submission Feedback loop Revision cycles
6

Delivery & Ongoing Support

We deliver structured datasets in JSON, CSV, XML or custom schemas, with severity tags, category labels and confidence scores, through encrypted transfer. We also apply policy updates and taxonomy changes as your guidelines evolve.

JSON / CSV / XML / Custom Secure delivery Ongoing support
Security & Privacy
ISO 27001-aligned workflows
GDPR-aligned processing
Permission-scoped access
🔧 Platform-agnostic delivery
Case Studies

Recent Projects

Examples of how social media, OTT, gaming and AI safety teams use these datasets.

Social Platform · US

UGC Image Moderation for Social Media Safety

Client Need: A US social media platform required consistent severity-tiered classification of user-uploaded images across nudity detection, violence and hate-symbol categories at scale.
Solution: We annotated 2M+ images with per-image severity tiers and client-defined routing, under three-tier QC, and delivered into the platform's enforcement queues.
Moderation precision improved by 31%
False positive rate reduced by 42%
2M+ images annotated
OTT Platform · EU

Video Frame Review for Age Ratings

Client Need: A European OTT streaming platform needed accurate frame-level classification of graphic violence and adult content across catalog titles for regional age-rating and regulatory compliance.
Solution: We annotated catalog titles frame by frame with scene-change detection and severity tiers, and delivered structured JSON for the platform's rating-enforcement pipeline.
Age-rating classification accuracy improved by 27%
50K+ video clips annotated
Gaming Platform · UK

In-Game Chat & Avatar Content Moderation

Client Need: A UK gaming platform required classification of user-generated in-game content (chat messages, custom avatars and user-uploaded assets) for harassment detection and age-appropriate enforcement.
Solution: We reviewed text and images together against a custom taxonomy covering harassment tiers, sexual content and hate symbols, with fast-turnaround QC and CSV export for the platform's real-time moderation stack.
Harassment detection accuracy improved by 34%
Appeal rate reduced by 28%
Digital Platform · Middle East

Regulatory Dataset for Audit-Ready Moderation

Client Need: A Middle East digital platform required defensible, audit-ready moderation records across content categories aligned to regional regulatory obligations.
Solution: We used multi-reviewer annotation with calibration checks, escalation protocols and cultural-context handling, and exported structured CSV and JSON that fit the platform's compliance reporting.
Audit pass rate improved by 22%
Ad Network · Global

Brand Safety Classification for Ad Placement AI

Client Need: A global ad network needed detailed content classification (safe, risky, unsafe by brand-adjacency tier) across publisher content to power AI-driven brand safety tooling for large advertisers.
Solution: We annotated 500K+ content items with severity labels for brand-safety scoring and contextual content labels, and delivered structured JSON in a custom schema.
Brand adjacency precision improved by 26%
500K+ items annotated
Compatibility

Tools & Platforms

We work inside your existing moderation stack, or recommend tools if you don't have one.

Platforms We Work In
Hive Moderation Amazon Rekognition (review queue) Labelbox V7 SuperAnnotate Label Studio CVAT (video frame review) Custom / In-house Tools
Export Formats
Structured JSON CSV moderation exports Structured XML feeds JSON Lines (per-item) Custom schema on request
Classifiers We Label For
NSFW / nudity classifiers Hate symbol & violence detection Toxicity & hate-speech NLP models Custom severity-scoring models
Secure Transfer
Encrypted SFTP AWS S3 (private bucket) Google Cloud Storage Azure Blob Storage Secure client portals ISO 27001-aligned & GDPR-aligned
Why Precise BPO

Why Trust and Safety Teams Choose Precise BPO

Our 540+ in-house team works with AI, social media, OTT and gaming teams on content classification and severity tiering.

Start a Free Pilot
18+ Years in Business

Data entry since 2008 and data labeling since 2017, from simple content classification to multi-policy severity taxonomies.

540+ In-House Team

Trained in-house reviewers, no crowdsourced workers.

Security & Privacy

Access control, NDA-bound workflows and automated security monitoring protect your moderation datasets and platform content.

Target of Up to 99.8% Accuracy

Three-tier QC combining severity validation, policy-consistency checks, peer review and expert audit, to keep classification and severity routing consistent across batches.

40–60% Below US/UK Rates

No hidden fees, and a free pilot batch comes first.

Works in Your Tools

We review in your preferred tooling and deliver in the formats and schema you define, with severity tags and confidence scores.

Why teams choose Precise BPO for explicit content annotation
Quality Assurance

Three-Tier QC Pipeline: How We Target Up to 99.8%

Every moderation dataset passes three quality control gates before delivery, covering content classification, severity tiering and policy-consistency checks. See our annotation governance guide for the full framework.

Tier 1 Reviewer + Peer
Tier 2 Severity & Policy Validation
Tier 3 Expert Audit + Delivery
T1

Reviewer Self-Check & Peer Review

The reviewer checks their own work first, then a senior peer cross-checks it. This catches category mismatches, wrong severity tiers, missed contextual flags and guideline deviations before any automated scoring.

Reviewer checks category assignment, severity tier, and contextual flags against client policy guidelines before submitting
Senior reviewer cross-checks: borderline-content consistency, cultural-context handling, and multi-class label correctness across the batch
Batches failing the T1 threshold are returned for correction before advancing to T2
T1 Exit Accuracy Target95%+
Category Consistency Rate97%+
T2

Automated Severity Validation & Policy Check

Automated checks validate severity-tier assignments, check category consistency across image, video and text, catch conflicting or duplicate labels, and flag statistical outliers in the batch for human re-review.

Severity scoring run against reference annotations: tier precision and policy match rate evaluated against project-specific tolerance thresholds
Category validation: duplicate labels, mismatched severity tiers, and missed-context flags returned for correction
Statistical outlier scan: anomalous severity distribution or category imbalance flagged for human review
T2 Exit Accuracy Target98%+
Avg. Policy Match Score (vs. reference set)0.97
T3

Expert QC Audit, Client Loop & Final Delivery

A QA lead reviews every high-severity escalation in full and samples standard queues at random. Disputed severity calls go back through the client feedback loop and are re-verified before final sign-off and delivery.

Random sampling audit: the QA lead reviews 10–20% of items per batch (100% on high-severity or compliance-critical projects)
Client sample review: 50–100 annotated items 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 Accuracy TargetUp to 99.8%

Throughput Capacity

Content Items / Day (Peak)250K+
Items Reviewed / Month5M+
Choosing a Vendor

Your Own Team vs Generic BPO vs Precise BPO

How building your own team, using a generic BPO and working with Precise BPO compare on the points procurement teams ask about.

Criteria Your Own Team Generic BPO Precise BPO
Annotation Accuracy Depends on reviewer experience and workload Often a single review pass Target of up to 99.8%, three-tier severity QC
Setup Time Several weeks (hire, train, tool) Usually a few weeks Pilot starts within 48 hours after the NDA is signed; pilot delivery takes 24–48 hours, where applicable. Production starts once guidelines are agreed
Scalability for Surge Volumes Fixed headcount, slower ramp Can scale, sometimes through subcontracted workers 540+ team, scales for peak content events
Cost vs US/UK Rates Baseline (US/UK salary + infra) Lower than in-house 40–60% below US/UK rates
Information Security Depends on internal IT policy Depends on the contract and the vendor ISO 27001-aligned and GDPR-aligned workflows
Reviewer Wellbeing Protocols Varies by company Usually not specified in the contract Rotation schedules, escalation paths, structured support
Policy Taxonomy Customization Possible but slow Often limited to standard categories Full custom taxonomy & severity-tier configuration
Free Trial / Pilot Not applicable Rarely offered Free pilot batch, no commitment
Engagement

Pricing & Engagement Models

Pricing is based on your content volume, severity mix and timeline. There are no platform fees and no lock-in.

Best for: Standard image batches
Per Image

Pay per reviewed and classified image. Suited to defined upload batches, one-off moderation audits or pilot programs needing predictable per-unit cost.

Best for: Video and livestream review
Per Frame

Priced per video frame. Suited to livestream review, age-rating compliance datasets and frame-level moderation where frame count is the natural unit of work.

Best for: Complex multi-policy review
Per Hour

Hourly model for high-complexity review, such as multi-label severity tagging, contextual policy adjudication and layered text, image and metadata review. It suits work where per-image pricing doesn't reflect the actual review effort.

Best for: Ongoing pipelines
Monthly Retainer

A dedicated annotation team at fixed monthly capacity. Suits social platforms and AI safety teams with continuous labeling needs, such as live upload queues or active learning pipelines.

Volume discounts available from 50K+ items/month. White-label pricing for trust and safety partners.
All models include secure transfer and a free pilot batch before commitment. Need online data entry alongside annotation? Ask about combined engagement pricing.
Get a Quote
Global Delivery

Work That Fits Your Time Zone

Our Pune team covers multiple time zones, so annotation keeps moving while your team is offline, and we set working hours to match yours.

North America
USA · Canada
Overlap with US hours
United Kingdom
Overlap with UK hours
Australia & NZ
Australia · New Zealand
Overlap with AU/NZ hours
Europe
Germany · France · Netherlands · Nordics
Overlap with European hours
Asia-Pacific
Singapore · Japan · India · SEA
Local working hours
Middle East & Africa
UAE · Saudi Arabia · South Africa
Overlap with Gulf and African hours
Latin America
Brazil · Mexico · Argentina · Colombia
Overlap with LATAM hours
Other Regions
Wherever your team is based
Hours set to match yours
Testimonials

What Our Clients Say

Feedback from trust and safety, content policy and AI safety teams we work with.

"As our community grew, so did the amount of chat and avatar content we had to review. Precise BPO's reviewers apply our rules consistently, turnaround has been dependable, and they handle the sensitive cases with care."

M
Megan R.
Trust & Safety Manager · Online Gaming Platform, Canada

"We needed reviewers who could tell routine policy violations from genuinely serious safety issues. Precise BPO fitted into our existing workflow, and their review support on sensitive user-generated content has been reliable."

D
Daniel K.
CTO · Brand Safety Technology Company, Australia
FAQ

Frequently Asked Questions

Answers on scope, accuracy, borderline content, security and pricing.

We label nudity, violence, hate symbols and other policy-violating material in images, video and text. Those labels train AI models to recognize unsafe content, rate its severity and route it for review or removal. Teams use them for trust and safety and brand safety. Our guide to data labeling covers the broader background.

We annotate user-uploaded images, livestream and on-demand video frames, in-game chat logs and ad creative against your policy categories and severity tiers. The material includes nudity, graphic violence, hate speech and other sensitive content.

Borderline cases are where most labeling disagreements happen, so we write them into the guidelines up front. Ambiguous items go to a senior reviewer, and decisions are logged so the same call is made the same way in later batches.

We split large datasets into batches that each follow one fixed set of policy rules. When a policy changes or new violation patterns appear, we update the guidelines and apply the change from the next batch forward, so severity definitions and categories stay consistent within every batch.

Mostly social media platforms, OTT services, gaming companies, generative AI vendors and brand safety agencies.

Reviewers work from written policy guidelines, severity-mapping rules and fixed category definitions, and they calibrate against edge cases and ambiguous scenes. Three-tier review makes sure the same violation type gets the same severity tag in every batch. Our annotation governance framework describes how we apply these standards.

We deliver JSON, CSV, XML or a custom schema, whichever your moderation platform or training pipeline loads directly. Structured outputs let your team validate severity mappings and use the data for training, policy audits or live moderation queues.

Pricing depends on content volume, severity-tier complexity, the mix of image, video and text, and review depth. We price per image, per frame, per hour or as a monthly retainer, typically 40–60% below US/UK rates. See our data labeling pricing guide or request a tailored quote. If you are comparing vendors, our comparison of annotation vendors shows how providers differ.

Our workflows are aligned with ISO 27001, GDPR and HIPAA. Reviewers sign NDAs before any project access, roles are permission-scoped, and we run regular security audits across project environments.

Explore by Industry

AI Annotation by Industry

Beyond content moderation, we label data for automotive, agriculture, medical, fashion, sports and retail AI pipelines.

Automotive Annotation
Lane detection, object tracking and sensor fusion labeling for driverless and ADAS programs.
Agriculture Annotation
Labeling for crop health, pest detection and yield estimation, for precision agriculture computer vision models.
Medical Annotation
HIPAA-aligned labeling of radiology, pathology and other diagnostic imaging, by trained annotators.
Fashion Annotation
Apparel attribute tagging, visual search and style classification for fashion e-commerce and styling AI.
Sports Annotation
Player, ball and pose tracking for sports analytics, broadcast AI and fan-engagement platforms.
Retail Annotation
SKU recognition, planogram compliance and attribute tagging for retail AI pipelines.
From the Blog

Guides & Resources for AI Data Teams

Practical guides on data labeling, annotation pricing, vendor selection and data entry, for ML teams and trust and safety leads comparing annotation partners.

Latest guides Browse all annotation & AI guides
Industry Workflow
Annotation Governance: Stop Label Drift in ML Models
Label drift reduces ML accuracy over time. Learn how our 6-layer governance framework helps detect, measure and prevent it.
7 min read
Complete Guide
Bounding Box Annotation: 5 Mistakes That Hurt AI Accuracy
The five most common bounding box errors, how to measure them with IoU, and how enterprise teams catch them.
11 min read
Pricing Guide
Data Labeling Pricing 2026: USA vs India
What each annotation type costs in the USA and India, and where hidden costs show up.
8 min read
Rankings
Top 10 Data Annotation Companies 2026: Pricing Comparison
Ten annotation vendors compared on pricing and strengths, with a side-by-side table.
10 min read
Vendor Selection
Top 10 Data Entry Companies 2026: Pricing and Comparison
Ten data entry vendors compared on pricing and services, plus what to look for when choosing one.
7 min read
Fundamentals
What Is Data Labeling? Enterprise Guide
Learn what data labeling is, its types, AI use cases, quality standards and how enterprises scale annotation for ML.
9 min read
Data Entry Guide
How to Evaluate Online Data Entry Outsourcing Vendors (2026)
A framework for evaluating online data entry outsourcing vendors: what to ask about accuracy, security, pricing, and scalability before you sign.
9 min read
Get Started

Ready to Start a Pilot?

Your pilot starts within 48 hours after the NDA is signed, and pilot delivery takes 24–48 hours, where applicable. No commitment.

Start a Free Pilot Call Us Now
Global Reach: USUnited States GBUnited Kingdom CACanada AUAustralia Europe Middle East APAC LATAM
Get in Touch

Tell Us About Your Project

Tell us what you need reviewed and we'll reply within one business day with next steps. Use the form to ask for a free pilot batch or a project quote. Other data labeling services can run under the same engagement if you need them. You can also read more about Precise BPO.

Phone & WhatsApp
+91 7972620994
Office
B3, Sant Namdev Marg, Varundavan Colony, Walhekarwadi, Gurudwara Colony, Nigdi, Pimpri-Chinchwad, Pune, Maharashtra 411033

Request a Free Pilot

Get a response within one business day, no commitment required.

Please enter your first name.
Please enter your last name.
Please enter a valid email address.

NDA on every engagement. No commitment required.

Thank you. We've received your request.

Our team will review your requirements and respond within one business day.