High-quality human-labeled data for training, fine-tuning, evaluation, and validation. From computer vision and NLP to LLM evaluation, speech, documents, and 3D sensor data, Maurvi AI provides structured human-in-the-loop data operations built around your model, taxonomy, and quality requirements.
Built for AI Teams That Need Reliable Data Operations
Pilot First
Validate the workflow before scaling.
Human Reviewed
Structured human review supports dataset quality.
Flexible Delivery
Capacity can scale according to project requirements.
Transparent Process
Clear requirements, workflows, QA and deliverables.
Maurvi AI — Where we compete
Built for the projects we can actually win.
Early-stage teams win by choosing the right fights. Maurvi AI concentrates on high-margin, fast-entry AI services instead of spreading thin across every lane.
Annotation built around the data your model actually needs.
We can start with a representative pilot, turn domain requirements into clear labeling guidelines, and deliver reviewed batches with documented decisions. The right modality, schema, volume, and export format are agreed before scale-up.
Define labels, edge cases, examples, and acceptance criteria.
03
Pilot
Run a representative sample before production scale.
04
Production
Execute annotation through trained delivery teams.
05
QA & Adjudication
Review quality, resolve disagreements, and document decisions.
06
Delivery
Return validated datasets in the agreed schema and format.
AI Data Operations
More than annotation.
AI data work is rarely just labeling. We support the operational workflow around datasets — from intake and guideline development to annotation, evaluation, quality review, and delivery.
01
Data Intake
Dataset review, structure analysis, and project scoping.
02
Taxonomy Design
Labels, schemas, definitions, examples, and edge cases.
03
Annotation
Human labeling across image, video, text, audio, documents, and 3D data.
04
AI Evaluation
LLM response evaluation, preference ranking, quality assessment, and human feedback.
05
Quality Assurance
Calibration, sampling, review, disagreement handling, and adjudication.
06
Dataset Delivery
Validated datasets delivered in the agreed format and schema.
Workflow
Every project starts with understanding the data.
Dataset
Understand the source data, structure, modality, and intended model use.
↓Scope
Define volume, classes, edge cases, timeline, and acceptance criteria.
↓Taxonomy
Translate requirements into consistent annotation definitions.
↓Pilot
Test the workflow against a representative sample.
↓Calibration
Align annotators and reviewers before production scale.
↓Production
Process approved batches through trained delivery teams.
↓QA
Review, measure, adjudicate, and document quality.
↓Delivery
Return validated data in the agreed schema.
Enterprise readiness
Designed for controlled delivery.
Security
Project-specific access controls, confidentiality requirements, and controlled data handling can be incorporated into the engagement.
Process
Defined guidelines, review stages, escalation paths, and documented decisions create a repeatable delivery process.
Scalability
Start with a pilot and expand the delivery team as project volume and requirements become clear.
Visibility
Project progress, quality observations, rework, and delivery status can be tracked through agreed reporting workflows.
Quality framework
Quality is measurable.
Quality can be evaluated using project-specific metrics and acceptance criteria defined during scoping. Quality thresholds are defined during project scoping and may vary by task.
Annotation Accuracy
Agreement with defined labeling guidelines and acceptance criteria.
Inter-Annotator Agreement
Consistency between annotators working on the same task.
QA Score
Reviewer assessment against project-defined standards.
Rework Rate
Volume requiring correction after review.
Rejection Rate
Data rejected against agreed quality criteria.
Turnaround Time
Time from production assignment to validated delivery.
Supported data
Multimodal data operations, built for real workflows.
Visual
ImageVideoComputer Vision
Language
TextNLPLLM
Audio
SpeechTranscriptionSpeaker Data
Documents
OCRFormsInvoicesTables
3D
LiDARPoint CloudsSensor Fusion
Evaluation
LLM EvaluationPreference DataAI Model Evaluation
Delivery options
Delivery that fits your pipeline.
Dataset Delivery
Validated annotation datasets delivered according to the agreed schema.
Batch Delivery
Production data delivered in defined batches for continuous review.
Custom Schema
Output structured according to client-defined requirements where supported.
Documentation
Annotation guidelines, decisions, QA observations, and delivery documentation can accompany the dataset where required.
We can support organizations that need additional AI-data delivery capacity, specialized annotation teams, QA support, or human-in-the-loop evaluation operations.
Quality is part of the workflow, not a final checkpoint.
We make the review path visible from the first calibration sample through final delivery. Metrics are tracked with the client's agreed definitions rather than presented as unsupported headline claims.
01
Annotator
Trained delivery team applies the agreed taxonomy.
02
Self Review
The annotator checks completeness and edge cases.
03
QA Review
A second reviewer samples and checks the work.
04
Adjudication
Team leads resolve disagreements and update guidance.
05
Client Feedback
Client review informs the next calibration cycle.
06
Final Dataset
Validated output is delivered with documented decisions.
Bring us the dataset, the problem, or simply the requirement. We'll help define the right annotation or evaluation workflow and start with a practical pilot.
A representative pilot lets both teams validate the taxonomy, annotation guidelines, quality expectations, production assumptions, and delivery format before committing to larger volumes.
01
Validate the taxonomy
02
Measure the workflow
03
Scale with evidence
Who we work with
Built for teams that need reliable AI data operations.
AI Product Teams
Teams building machine-learning and generative-AI products.
AI Research Teams
Teams creating datasets, benchmarks, and evaluation workflows.
Autonomous Systems
Computer vision, ADAS, robotics, LiDAR, and sensor-fusion teams.
Enterprise Technology
Organizations integrating AI into large operational workflows.
IT & BPO Partners
Technology and service providers requiring a focused AI-data delivery partner.
Healthcare AI
Teams working with appropriately governed medical and clinical AI datasets.
Enterprise FAQ
Questions buyers ask before a pilot.
What types of data can Maurvi AI annotate?
We support image, video, text, audio, documents, 3D/LiDAR, and AI-generated content for evaluation workflows.
Can we start with a small pilot?
Yes. Our engagement model is designed to validate a representative sample before production scale-up.
Can you follow our existing annotation guidelines?
Yes. We can work from client-provided taxonomies, schemas, examples, and acceptance criteria.
Can you create annotation guidelines?
Yes. We can help translate project requirements into structured labeling guidelines and edge-case definitions.
How do you handle quality?
Projects can include annotator review, dedicated QA, adjudication, calibration, sampling, and documented quality reporting.
What output formats do you support?
Depending on the project, delivery can use formats such as JSON, JSONL, CSV, XML, COCO, YOLO, Pascal VOC, TXT, or a client-defined schema.
Can you support large volumes?
Yes. Projects can begin with a pilot and scale into dedicated or managed delivery teams once the workflow is validated.
Do you work with confidential data?
We can operate under client-defined confidentiality, access-control, and data-handling requirements. Specific security and compliance requirements should be agreed during project scoping.
Company Profile
About Maurvi AI
Who We Are
Maurvi AI Private Limited is an AI data services company focused on helping AI teams transform raw data into structured, annotated and evaluation-ready datasets.
Our Approach
Pilot first
Clear requirements
Structured annotation workflows
Human review
Quality assurance
Transparent communication
Scalable delivery
Our Vision
To build a focused AI company that combines human intelligence, data operations and technology to help organizations develop better AI systems.
Workflow
How We Work
01 — Understand
We understand the AI use case, dataset and expected outcome.
02 — Define
We establish taxonomy, annotation guidelines, quality requirements and delivery format.
03 — Pilot
We process a representative sample before production scale.
04 — Validate
We perform structured quality review and resolve annotation disagreements.
05 — Deliver
We provide the agreed dataset and supporting documentation.
06 — Scale
Once the workflow is validated, production capacity can be expanded according to project requirements.
Why Maurvi AI?
Start Small
Begin with a representative pilot rather than committing to a large production engagement immediately.
Structured Workflows
Clear taxonomy, guidelines and delivery specifications.
Quality Focus
QA and review are built into the workflow.
Flexible Scale
Delivery capacity can be expanded as project requirements grow.
Direct Communication
Founder-led engagement provides direct communication during the early stages of a project.
Contact
Tell us what you're building.
Share a few details about your data, annotation requirements, expected volume, and timeline. We'll review the scope and help define a practical pilot.
Have an AI data, annotation, evaluation or human-in-the-loop requirement? Tell us what you're building and we'll discuss the right workflow for your project.