Rilith combines expert human annotators with robust QA workflows and synthetic data pipelines to deliver production-ready datasets for computer vision, NLP, and generative AI.
98.5%
Average annotation accuracy
50M+
Data points processed
3x
Faster delivery cycles
No long-term contracts. Start with a small pilot in under 7 days.
Enterprise data annotation
Human-in-the-loop with multi-layer QA
CV
Bounding boxes, masks, keypoints, polygons
NLP
NER, classification, conversation, intent
Autonomy
3D / LiDAR, tracking, sensor fusion
Synthetic data & augmentation
Stress-test your models before production
Built for product & research teams
We integrate with your workflows, tools, and formats (COCO, Pascal VOC, TFRecord, JSON, custom schemas).
A distributed workforce powering AI data operations at scale.
Geographic coverage
Countries covered
Experience
Data collection & annotation projects
Workforce
Verified gig workers
Language coverage
Languages supported
USED BY TEAMS BUILDING
From one-off pilots to production pipelines, Rilith plugs into your stack to provide the datasets and workflows you need to ship reliable models faster.
Prefer a custom workflow?
Curated, labeled, and versioned datasets for training, validation, and regression testing across computer vision and NLP use cases.
Embed human reviews at key points in your AI product – content safety, search relevance, personalization, and more.
Generate rare, risky, or long-tail scenarios safely using synthetic and simulated data tailored to your domain.
Combine human experts, robust QA, and synthetic data in a single partner. Start focused with one service or run an end-to-end program.
Human-in-the-loop labeling for all major data types, tuned to your guidelines and model behavior.
Independent checks to keep your datasets clean, consistent, and bias-aware over time.
Generate realistic, privacy-safe data to augment or bootstrap your training sets.
We plug into your workflows with clear ownership, transparent metrics, and feedback loops that continuously improve your data.
We align on success metrics, edge cases, formats, and volumes with your team.
We co-create guidelines, run a pilot batch, and calibrate quality thresholds.
Trained annotators work in parallel with multi-stage QA and performance monitoring.
We deliver in your preferred formats and refine based on model performance & feedback.
We work with AI teams in startups and enterprises, across computer vision, NLP, and robotics.
Names anonymized for confidentiality.
Global e-commerce company
“The quality of annotations was consistently strong and the team quickly understood our edge cases. Their work directly contributed to better model performance and faster experimentation.”
Fintech platform
“Their text annotation and QA process helped us significantly improve our intent and sentiment models. The feedback loop with our team was fast and very collaborative.”
Autonomous systems
“The synthetic data scenarios they created for our navigation stack uncovered failure cases we had not seen in production logs. We measured more than 15% improvement on our key benchmarks.”
Share a bit about your use case and timelines. We will get back with a proposed approach and next steps.
Our team has worked with AI, data, and product teams across multiple industries. We will shape a plan that fits your stage and resourcing.
rohit@rilith.com
+91 73888 40089
Locations
San Francisco, CA (USA) · Global remote teams
If you do not see your question here, we are happy to discuss it directly.
Start with a focused pilot project, validate the impact on your models, and then scale confidently.