Data Entry & Annotation Companies
Data entry and annotation outsourcing gives you access to trained, managed teams who process, clean, and label data at scale, but only works reliably when your process is documented and your quality bar is defined before the first task goes out.
Top data entry & annotation providers
24 providersAI-powered BPO and remote staffing solutions offering virtual assistants, AI agents, and full-time outsourcing services for business growth.
View profile →360 Bookkeeping LLC is a Tampa-based remote bookkeeping firm offering cleanup, reconciliations, payroll, and financial statements for small businesses.
View profile →360 Transcription provides human-delivered medical and medico-legal transcription services for US healthcare practices and legal clients, with HIPAA compliance and next-day turnaround.
India-based data digitization and outsourcing company offering data entry, data conversion, data mining, accounting, and payroll services since 2008.
View profile →Picsera is an outsourced visual content agency offering image editing, video post-production, and 3D design for eCommerce brands and retailers.
View profile →904 Bookkeeping is a Jacksonville-based QuickBooks bookkeeping and payroll practice serving small to mid-sized Florida businesses.
View profile →ACS Contact is a Troy, MI call center and marketing services company offering inbound/outbound calls, lead generation, fulfillment, and extended service plan programs.
View profile →Radical Minds Technologies is an India-based BPO with 15+ years offering CX, healthcare RCM, finance, collections, RPO, and AI chatbot services.
View profile →Saivion India is a New Delhi ITeS company offering eCommerce data entry, photo editing, catalog management, and digital services to global clients since 2013.
View profile →Rannsolve provides AI automation and business process outsourcing solutions targeting enterprise clients with data-intensive or back-office workflows.
View profile →Cognizant is a large-scale IT outsourcing and business process services firm serving enterprise clients across healthcare, financial services, and manufacturing.
Pixel Annotation is an India-based AI data annotation company founded in 2024, offering image, video, text, and audio labeling for ML and AI teams.
View profile →ArhamSoft is an AI software development and IT outsourcing firm offering custom software, dedicated engineering teams, and AI/ML product development services.
View profile →Movate is a global IT services and AI-driven CX company serving enterprise clients in telecom, retail, healthcare, and technology through its Mova iO platform.
View profile →Vsynergize AI provides AI-powered BPO and contact center services, including voice agents, back office, telemarketing, and data management for enterprise clients.
View profile →Rely Services is a trusted BPO company with 25+ years of experience delivering data entry, finance & accounting, and industry-specific back-office solutions to 1,000+ clients worldwide.
Medical billing and revenue cycle management provider serving healthcare practices since 2008, from solo practitioners to large facilities.
Bangladesh-based BPO provider offering back office support, customer care, data entry, IT services, and digital marketing solutions.
View profile →Access Healthcare provides end-to-end revenue cycle management and healthcare BPO services, including RCM automation via its Echo platform, for US healthcare providers and payers.
Magellan Solutions is a Philippines-based BPO provider offering call center, back-office, and virtual assistant services primarily for SMEs.
View profile →HTC Global Services delivers IT outsourcing, digital transformation, cloud, data and AI, and business process services to mid-market and enterprise clients across multiple industries.
View profile →Open Access BPO delivers multilingual CX, content moderation, and back-office outsourcing from the Philippines, US, and Taiwan for digital-first brands.
View profile →Japanese-owned BPO provider based in Cebu, Philippines, delivering call center, back office, IT, and sales outsourcing services to mid-sized and large enterprises globally since 2009.
View profile →India-based BPO and IT services company offering web design, data processing, document scanning, eBook conversion, CAD/CAM, and back-office outsourcing since 2006.
Showing top 24 of 342 providers. Use the filters above to narrow results.
What is data entry & annotation outsourcing?
Data entry and annotation outsourcing means hiring an external team to handle the capture, classification, processing, or labeling of structured and unstructured data on your behalf. This covers everything from ecommerce product catalog entry and document digitization to AI training data labeling, image annotation, text classification, bounding boxes, named entity recognition, RLHF feedback, and more. The vendor supplies the agents, management layer, QA process, and tooling. The buyer supplies process documentation, access to source data, quality standards, and an internal point of contact. Volume, accuracy requirements, domain complexity, and location all determine whether this is a cost-saving or cost-creating decision.
What data entry & annotation outsourcing covers
- Document digitization and data capture from PDFs, forms, scanned files, and handwritten records
- Ecommerce product data entry, titles, descriptions, attributes, pricing, SKU management, catalog maintenance
- Image and video annotation, bounding boxes, polygon segmentation, keypoint labeling, image classification
- Text annotation, named entity recognition, sentiment labeling, intent classification, NLP training datasets
- RLHF and model feedback tasks, ranking AI outputs, preference labeling, response quality scoring
- Medical and clinical data entry, patient records, coding support, form processing (with HIPAA compliance)
- Data cleansing, deduplication, normalization, and database enrichment
- Survey data processing, form extraction, and index data entry
When to outsource data entry & annotation
- Your internal team spends meaningful hours per week on repetitive data entry or labeling that has no strategic value and clear SOPs can be written for it
- You have an AI or ML project that needs labeled training data at a volume your internal team cannot produce without blowing timelines
- Your ecommerce catalog is growing faster than your team can manage product data entry, accuracy is slipping and backlogs are building
- You have a defined quality standard, an acceptable error rate, and someone internally who can own vendor QA and feedback, meaning the outsourcing will be supervised, not abandoned
- You have run the numbers and the cost of outsourcing is materially lower than in-house, even after accounting for management time, QA overhead, and transition effort
When not to outsource data entry & annotation yet
- Your data entry process changes week to week and is not yet documented, outsourcing chaos does not fix it, it amplifies it
- The data involved requires deep domain judgment (clinical diagnosis, legal interpretation, complex financial decisions) where a trained internal expert is the only reliable processor
- Your source data is so messy, inconsistent, or unstructured that even an internal team struggles, clean the data pipeline first, then delegate the entry
- You have no internal owner to review output quality, catch errors, and close the feedback loop with the vendor, unmanaged outsourcing drifts fast
How data entry & annotation outsourcing works
- 1Scope definition, buyer documents the exact tasks, data sources, formats, volume estimates, accuracy requirements, turnaround expectations, and any compliance constraints before engaging a vendor; vendors who skip this step are a red flag
- 2Vendor selection and pilot design, buyer shortlists vendors by process fit, not just price, and runs a 2 to 4 week paid pilot on a real subset of work to test accuracy, turnaround, communication quality, and reporting before committing to full volume
- 3Onboarding and SOP transfer, vendor builds or validates SOPs, trains agents on the buyer's specific process, tools, and quality standards; buyer should see a written training plan and a readiness sign-off before live production begins
- 4Production ramp, vendor handles live volume under agreed SLAs; buyer reviews output samples daily or weekly in the early weeks, tracks error rates, and calibrates quality expectations with the vendor's QA team
- 5Reporting and review cadence, vendor delivers regular reports covering volume processed, accuracy rates, TAT, error breakdown, backlog, and QA scores; buyer uses this to catch drift early and course-correct before errors compound
- 6Ongoing optimization, as the process stabilizes, buyer and vendor identify edge cases, update SOPs, adjust team size for volume changes, and improve tooling integration; a vendor who does not flag process issues proactively is one to watch carefully
Data Entry & Annotation pricing models and typical rates
Data Entry & Annotation: offshore, nearshore, or onshore?
Offshore teams in India, the Philippines, and Vietnam are well-suited for high-volume, documented, repeatable data entry and standard ML annotation tasks where cost efficiency is the priority and timezone can be managed asynchronously. Quality depends heavily on the vendor's management and QA layer, not geography alone. Nearshore LATAM, particularly Mexico and Colombia, suits buyers who need real-time collaboration, bilingual capability, or faster feedback loops on annotation projects with evolving requirements. Onshore US teams make sense for regulated domains like clinical data or legal document processing, RLHF tasks requiring native cultural and linguistic judgment, and any work where data sensitivity or compliance makes offshore access a legal or contractual issue.
Red flags when choosing data entry & annotation providers
Questions to ask data entry & annotation vendors
- Can you walk me through exactly how your QA process works for this type of task, what percentage of output gets reviewed, who reviews it, what does the scorecard look like, and what happens when an agent hits repeat errors?
- Show me a sample report from a current client doing similar work, not a template, an actual or anonymized live report showing volume, accuracy, TAT, error breakdown, and what action you took on the last quality issue
- What is your agent-to-team-lead ratio for this type of work, how long have the agents on this type of task been with you, and what is your attrition rate on this process?
- How do you handle edge cases and exceptions in data entry or annotation tasks, who decides, how fast, and how is the decision documented so agents learn from it?
- If we run a pilot and the accuracy is not meeting our target by week two, what specifically happens, who owns the fix, what is the timeline, and what have you done in that situation before?
- For annotation work specifically, what tools do you use, can you work inside our preferred platform, and how do you handle inter-annotator agreement and consistency across a distributed team?
My take on data entry & annotation outsourcing
Data entry and annotation outsourcing is one of the clearest cases where outsourcing genuinely works, if the process is defined. The mistake I see most often is buyers treating this as a commodity purchase and choosing on price per hour or price per label without checking the management layer or QA process behind it. A $3 per hour offshore team with weak QA and no team lead will produce rework that costs you more than a $10 per hour team with strong process discipline. For AI annotation especially, bad labels compound into bad models. I would always run a pilot on real data, not sample data, before committing volume. This is also one of the few outsourcing categories where dedicated FTEs often outperform shared pools over time because process knowledge accumulates.
Frequently asked questions
- What is the difference between data entry outsourcing and data annotation outsourcing?
- Data entry is the capture, transfer, or processing of structured information, typing from forms, building product catalogs, digitizing records, updating databases. Data annotation is labeling raw data to make it usable for AI and machine learning, drawing bounding boxes around objects in images, tagging text with entities, ranking AI responses, or classifying audio. Both involve human processing of data at scale, but annotation requires more specific guidelines, consistency standards, and often domain knowledge. Some vendors do both; others specialize in one.
- How accurate should outsourced data entry or annotation be?
- For standard data entry, 99% to 99.5% accuracy is a reasonable starting expectation; some vendors claim 99.9% but that depends on task complexity and how errors are counted. For AI annotation, inter-annotator agreement and consistency matter as much as raw accuracy, an accuracy figure without a measurement methodology behind it is not useful. Define your acceptable error rate before you brief a vendor, not after you receive the first batch.
- How much does data entry outsourcing cost in 2025 to 2026?
- Offshore data entry in India and the Philippines typically runs $3 to $15 per hour (indicative), with India-specific rates as low as $2.50 to $4.50 per hour for simple tasks. Nearshore and onshore US providers run $20 to $40 per hour. Ecommerce product data entry offshore typically falls in the $8 to $15 per hour range. Always compare cost per accurately completed record, not just hourly rate, a slower, more accurate team can be cheaper overall.
- How much does AI data annotation outsourcing cost?
- Offshore annotation in lower-cost regions runs $5 to $15 per hour for standard ML tasks; some African-market providers charge under $7 per hour, though ethical and quality considerations apply. LATAM nearshore annotation, particularly Mexico, runs $25 to $50 per hour for skilled annotators. US-based entry-level annotation roles run $15 to $20 per hour; domain specialists in medical, legal, or code annotation command $20 to $30 per hour. Per-label pricing varies widely by complexity, simple classification tasks cost fractions of a cent per label while complex image segmentation can run considerably higher.
- Should I use a large platform like Scale AI or a smaller BPO for annotation?
- Large platforms like Scale AI make sense for enterprise AI programs that need massive volume, platform tooling, and model-in-the-loop pre-annotation at scale. Smaller specialist BPOs can be a better fit for mid-market buyers with defined but lower-volume annotation needs, tighter budgets, or domain-specific requirements where a dedicated team with subject matter knowledge outperforms a high-throughput crowd. The platform route trades cost for speed and tooling sophistication; the BPO route trades sophistication for process control and relationship depth.
- What types of data entry can actually be outsourced?
- Most documented, repeatable data entry tasks can be outsourced: ecommerce product catalog entry, form and survey processing, document digitization, database management, invoice and order processing, medical record entry, insurance form processing, real estate listing data, and research data compilation. The key word is documented, if your team cannot write a clear SOP for the task, it is not ready to outsource.
- How do I protect sensitive data when outsourcing data entry or annotation?
- Ask the vendor specific operational questions, not just whether they are compliant. Who accesses the data, from which devices, in which locations? Can agents copy or export data? What happens when an agent leaves, how fast is access revoked? Is there CCTV or screen monitoring in the facility? For healthcare data, confirm HIPAA BAA. For payment data, confirm PCI-DSS scope. For European data subjects, confirm GDPR data processing agreements. A vendor who answers these questions in detail is better than one who says they are SOC 2 certified without explaining what that means in practice.
- How long does it take to onboard an outsourced data entry or annotation team?
- For simple, well-documented data entry, a competent vendor can be productive within one to two weeks. For annotation projects with complex guidelines or domain-specific requirements, expect two to four weeks of onboarding before quality stabilizes, sometimes longer for medical or technical domains. Vendors who promise full productivity from day one on complex annotation work are overpromising. A structured onboarding plan with a defined readiness sign-off is a basic expectation, not a premium feature.