End-to-end data structuring for LLM fine-tuning, pre-training, evaluation and RAG pipelines.
We design, build and validate JSONL datasets for fine-tuning. Every dataset is schema-matched to your model architecture, with format, quality filters and annotation tailored to your use case.
Request this serviceWe define the exact JSON structure needed for your training framework — OpenAI, HuggingFace, Axolotl, etc.
Raw inputs are converted, cleaned and formatted into the agreed schema with deduplication and normalisation.
Every row is validated against the schema. A quality score is assigned and flagged rows are reviewed manually.
Final JSONL file + data card + stats report + sample validation set.
We convert large raw datasets into schema-optimised Parquet files ready for ingestion via Hugging Face Datasets, Apache Spark, S3, BigQuery or any other columnar data store.
Request this serviceOutput formats we deliver
We run your raw corpus through a full cleaning pipeline: deduplication, language detection, normalisation, PII scrubbing, toxicity filtering, and quality scoring. You get a clean, audited dataset with a full report.
Request this serviceWe build held-out evaluation and benchmark sets with human-verified ground truth labels, designed to expose model weaknesses and track fine-tuning progress across training runs.
Request this serviceWe work with you to define evaluation dimensions, scoring rubrics and edge case coverage.
Labels are created and verified by human reviewers with inter-annotator agreement scoring.
Eval set in JSONL + Parquet + scoring script. Ready to plug into your eval harness.
We segment, chunk and enrich documents for Retrieval-Augmented Generation pipelines. Chunking strategy, overlap settings and metadata enrichment are all tuned to your vector database and embedding model.
Request this serviceCompatible with Pinecone, Weaviate, Qdrant, pgvector, Chroma and more.
For teams with ongoing data needs, we build repeatable batch processing pipelines — weekly jobs, format converters, annotation tooling and automated QA reporting — that run without intervention.
Let's talkDescribe your data and your goal. We'll tell you exactly what to do with it.