Build reliable, productive generative models with human expertise.
We deliver scalable annotation, instruction-tuning datasets, evaluation & H-in-the-Loop (HITL) services that make LLM and multimodal models safer, more accurate, and production ready.
What we do
We provide end-to-end human-centric services to train, fine-tune, evaluate and monitor generative AI.
Our process-driven services bridge this gap by:
Instruction dataset creation (pair prompts & ideal responses for instruction-tuning / SFT)
Human-in-the-Loop workflows for model improvement, online learning, and human review of model outputs
Evaluation & red-teaming: scoring, edge-case identification, adversarial tests, hallucination checks
Prompt engineering & dataset augmentation: curated pseudo-labels, synthetic data verification
Continuous quality pipelines and audit trails for compliance
Types of content moderation we offer
Here are some types of image annotations we provide.
Instruction tuning & supervised fine-tuning (SFT)
We design and annotate instruction-response pairs tailored to your model objective:
- Prompt engineering & prompt variants to cover tone, format, and domain constraints
- Multi-turn conversations and context window construction
- Role-based responses (assistant/persona conditioning)
- Output format enforcement (JSON, tables, code blocks)
Evaluation, QA & Red teaming
Measure and harden model behavior:
- Automated and human evaluation metrics (accuracy, helpfulness, factuality, bias)
- Adversarial prompt generation & stress tests
- Hallucination detection workflows & factual grounding pipelines
- Annotator-led root cause analysis and mitigation plans
Annotation & data labeling
High-accuracy annotation across modalities:
- Text: intents, entities, spans, relation labels, toxicity/safety tags, correctness checks
- Code: docstring generation, code synthesis verification, unit test generation
- Multimodal: OCR + alignment, image captioning, bounding boxes, visual question answering pairs
- Audio: transcription with timestamps, speaker diarization, semantic tagging
Human-in-the-Loop (HITL)
Embed humans where models fail or where high-stakes decisions matter:
- Real-time human review for critical outputs
- Active learning loops — human labels guide sampling for next training batches
- Onboarding and calibration of reviewers to keep decision consistency
Synthetic data & augmentation
Generate controlled synthetic examples and validate them:
- Bootstrapping prompt templates + human vetting
- Back-translation, paraphrase pools, and negative example mining
- Synthetic-to-real parity testing and drift monitoring
Our Advantages
Understand how our data collection approach improves model quality, compliance, and time-to-market.
Optimized for quality
We have a two-layer QC process that ensures the quality of the output. This is enabled by a short feedback loop process.
End to end solutions
From data collection and cleaning to data annotation, we offer End to end solutions for your training data needs.
Cost efficient
Our pricing is transparent and economical. We are more cost-effective than contract workers and large annotation platforms.
Completely managed
Our services are fully managed with dedicated account managers to ensure smooth operations.
Scalable workforce
Start with a single person and grow with us. We scale our team based on your demands.
Data security
Data security is paramount. We are GDPR compliant and ISO 27001 certified.
Use cases
What makes us different from others? We give holistic solutions
with strategy, design & technology.
Build Gen AI you can trust in production
Talk to us about building ML-ready processes that turn relevance into results.