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
Services we provide
From instruction tuning datasets to live model evaluation — every step backed by trained human judgment.
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
Why teams choose DATACLAP for generative AI
The quality of your model is a direct function of the quality of its training signal. Here's how we protect that signal at every step.
Annotators calibrated for LLM tasks
Preference ranking, response scoring, and instruction following evaluation require a different skill set from standard labeling. We screen, train, and calibrate annotators specifically for generative AI tasks before any project begins.
Inter-annotator agreement on every batch
Subjective GenAI judgments — helpfulness, factuality, tone — are only as reliable as the consistency between reviewers. We track inter-annotator agreement on every batch and flag drift before it contaminates your training signal.
Red teaming by domain specialists
Effective adversarial testing requires reviewers who understand your model's domain and failure modes. Our red teamers are matched to your use case — whether that's a customer facing chatbot, a code generation tool, or a medical information system.
Synthetic data that's human verified
We don't just generate synthetic examples — we validate them. Every synthetic prompt, paraphrase, or augmented sample is reviewed by human annotators to confirm it meets your quality bar before entering the training pipeline.
Continuous feedback into retraining
Human corrections, preference signals, and evaluation outputs are structured and delivered in formats your pipeline can ingest directly. Fresh human signal, on your retraining cadence, without manual reformatting overhead.
GDPR compliant, ISO 27001 ready
Model training data and evaluation logs often contain sensitive content. All workflows run under strict access controls, encrypted transfer, and data handling agreements designed for regulated and high sensitivity AI development.
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.