Your AI Model Is Only
As Good As Your Training Data
Markifid delivers high-accuracy image, text, video, and LLM training data for AI and ML companies that cannot afford to train on bad data.
Poor Training Data Is Why Most AI Models Fail in Production
AI projects rarely fail because of model architecture. They fail because training data is inconsistently labeled, insufficiently diverse, or wrong.
At Markifid, labeling accuracy is non-negotiable. Every project starts with taxonomy discussion, annotator training, and agreed benchmarks. India-based team serving US, UK, UAE, and India.
Which Type of Data Annotation Do You Need?
Different AI use cases require different annotation types.
Image Annotation
- Used for
- Computer vision, object detection, autonomous vehicles, medical imaging, retail AI, facial recognition
- Techniques
- Bounding box · Polygon · Semantic segmentation · Keypoint · Instance segmentation
Text Annotation
- Used for
- NLP models, chatbots, sentiment analysis, named entity recognition, intent classification, search relevance
- Techniques
- Entity labeling · Sentiment tagging · Intent classification · Coreference resolution · Dependency parsing
Video Annotation
- Used for
- Action recognition, object tracking, autonomous systems, security surveillance, sports analytics
- Techniques
- Frame-by-frame bounding boxes · Temporal segmentation · Pose estimation · Activity labeling
LLM Training Data
- Used for
- Large language model development, instruction fine-tuning, RLHF alignment, chatbot training, generative AI evaluation
- Techniques
- Instruction pairs · Preference ranking · Red-teaming · Response evaluation · Domain-specific fine-tuning data
Our Data Annotation Services
Four modalities. One quality standard. Delivered at the scale your model requires.
Image Annotation Services
Bounding box, polygon, semantic segmentation, keypoint, and instance segmentation annotation for computer vision models across healthcare, automotive, retail, agriculture, and robotics applications.
Accuracy target: 98%+ | Formats: COCO, Pascal VOC, YOLO, custom
Text Annotation Services
Named entity recognition, sentiment labeling, intent classification, question-answer pair generation, and coreference resolution — for NLP pipelines, search engines, and conversational AI systems.
Languages: English, Hindi, and major Indian languages | Domain-specific annotators available
Video Annotation Services
Frame-level bounding box annotation, temporal segmentation, action labeling, and object tracking — for autonomous vehicles, surveillance systems, sports AI, and video understanding models.
Output: Frame-accurate, timestamp-aligned annotations | Scalable to millions of frames
LLM Training Data Services
Instruction tuning datasets, RLHF preference data, model response ranking, red-teaming, and domain-specific fine-tuning data — built for teams training, aligning, or evaluating large language models.
Speciality: Generative AI · Chatbot alignment · Domain fine-tuning
Why AI Companies Choose India for Data Annotation
Cost Efficiency Without Quality Compromise
Companies that outsource annotation to India typically reduce labeling costs by 40 to 70 percent compared to in-house teams in the US or UK — without sacrificing accuracy when the right quality processes are in place.
Large, Skilled, English-Proficient Talent Pool
India's annotation workforce combines English proficiency with strong STEM backgrounds — critical for text annotation, NLP tasks, and LLM training data that require genuine language understanding, not just pattern-matching.
Scale on Demand
Indian annotation partners can rapidly scale team size for high-volume projects — from 10,000 labels to 10 million — without the recruiting timelines, overhead, or management burden of building an in-house team.
Time Zone Advantage for Global Teams
India Standard Time (IST) overlaps with both European business hours and early US East Coast hours — enabling daily handoffs, faster QA turnaround, and effective collaboration with global AI teams.
How Markifid Ensures Annotation Accuracy
Annotator Training and Certification
Every annotator is trained on your specific taxonomy, edge cases, and quality standards before touching your dataset. No annotator begins production work without passing an accuracy threshold test on a calibration set.
Dual-Annotator Review
High-stakes annotation tasks use a dual-review workflow — two independent annotators label the same sample, and disagreements trigger a senior reviewer adjudication before the label is finalised.
Inter-Annotator Agreement (IAA) Scoring
We measure and report inter-annotator agreement scores across every batch. IAA scores below agreed thresholds trigger immediate rework before delivery — not after you have already used the data in training.
Automated Consistency Checks
Automated scripts flag statistical outliers, labeling inconsistencies, and format errors across large batches — catching systematic errors that human review alone would miss at scale.
Transparent Accuracy Reporting
Every delivery includes a QA report: accuracy scores by annotation type, IAA metrics, rejection rates, and any edge cases flagged during review. You always know the quality of the data you are putting into your model.
Why AI Companies Choose Markifid for Data Annotation
Accuracy Benchmarks Agreed Upfront
We do not promise "high quality" in general terms. We agree specific accuracy targets — typically 98%+ — before the project starts and report against them with every batch delivery.
Domain-Specialist Annotators Available
General annotators are fine for commodity labeling tasks. For medical imaging, legal text, financial documents, or domain-specific NLP — we source annotators with relevant domain backgrounds, not just annotation experience.
Flexible Engagement Models
Project-based annotation for one-time dataset builds. Retainer models for teams with continuous labeling pipelines. Dedicated team models for enterprises that need annotation as an ongoing operational function.
Full Data Security and NDA Coverage
Every project operates under a signed NDA. Data is handled in access-controlled environments with no third-party sharing, clear retention policies, and full deletion protocols on project completion.
Industries We Annotate Data For
How a Data Annotation Project Works at Markifid
Scoping and Taxonomy Definition
We start with a detailed scoping call to understand your use case, model architecture, annotation requirements, and quality benchmarks. We then co-develop the annotation taxonomy and labeling guidelines with your team before any work begins.
Annotator Selection and Training
Based on your data type and domain, we select the right annotator profile — general or specialist. Every selected annotator is trained on your specific guidelines and must pass a calibration test before entering production.
Pilot Batch and Benchmark Validation
Before full-scale production, we deliver a pilot batch of 500 to 1,000 samples for your team to review. This validates that our labeling meets your expectations and allows us to refine the guidelines before scaling.
Production Annotation with Ongoing QA
Full production runs with our multi-layer QA process active throughout — dual review, IAA scoring, and automated consistency checks running in parallel with annotation, not after.
Delivery, Reporting, and Iteration
Final delivery in your required format (COCO, Pascal VOC, YOLO, JSON, CSV, or custom) with a full QA report. We remain available for post-delivery queries, iteration rounds, and ongoing annotation as your model evolves.
Questions About Data Annotation
Other Services
Discover how our integrated ecosystem of services can drive growth for your brand across every layer.
IT Services
Web development, mobile apps, AI/ML, cloud solutions, UI/UX, ERP/CRM, and cybersecurity — engineering that scales with your ambitions.
Marketing
SEO, PPC, social media, content, email, and influencer marketing — campaigns designed to perform, not just appear.
Brand Consulting
Brand strategy, identity design, messaging, personal branding, and rebranding — we build brands people remember.
Funding
Startup funding, seed rounds, venture capital consulting, brand funding — we help you walk into the room ready.
Business Expansion
Market entry strategy, international expansion, franchise consulting — we map the path to your next market.
Strategy Planning
Business consulting, go-to-market strategy, digital transformation — a clear playbook before every move.
Project Launchpad
MVP development, startup consulting, product launch strategy — from zero to live, fast.
Brand Due Diligence
Brand audit, valuation, and competitor analysis — know exactly where you stand before you invest or acquire.
Political Portfolio Management
Campaign management, candidate branding, voter outreach, election analytics, political PR — win with digital.
Need High-Quality Training Data for Your AI Model?
Tell us your use case, data type, volume, and timeline. We scope the project, confirm accuracy benchmarks, and send a quote — typically within 1 business day.
- NDA before any data is shared
- Multi-layer QA on every batch
- Serving AI companies globally
Headquartered in Jaipur · India, US, UK & UAE