Transform raw, noisy enterprise data into high-quality training sets for LLM fine-tuning and RAG pipelines. Automated deduplication, normalization, PII redaction — 100% on-premise.
AI data cleaning is the systematic process of identifying and correcting errors, inconsistencies, duplicates, and sensitive content in raw datasets before using them to train, fine-tune, or augment AI and LLM models. It is the foundational step in any serious machine learning pipeline.
Raw enterprise data — PDFs, email threads, server logs, SQL archives, CRM exports — is inherently messy. It contains exact duplicates, near-duplicates, format inconsistencies, encoding errors, boilerplate noise, and Personally Identifiable Information (PII) that is illegal to include in training sets under HIPAA, GDPR, and CCPA.
When models train on uncleaned data, the effects compound: duplicate training examples cause overfitting, noisy context windows inflate token costs, PII contamination creates regulatory liability, and inconsistent formatting degrades the structured patterns models need to generalize effectively.
VaultData's AI data cleaning service addresses all of these problems through a fully automated, on-premise pipeline — no external APIs, no cloud processing, no data leaving your infrastructure.
Research consistently shows that data quality has a larger measurable impact on fine-tuned model performance than model architecture choices or hyperparameter tuning. A smaller, well-curated dataset outperforms a larger, noisy one on downstream tasks — every time.
Noisy, contradictory training examples are the primary driver of LLM hallucinations. Cleaning conflicting and low-quality examples before training measurably reduces the rate of false confident outputs.
Training on data containing unredacted PII violates HIPAA, GDPR, and CCPA. Our automated scrubbing pipeline detects and redacts 40+ entity types before any data touches your training infrastructure.
Duplicate and boilerplate data wastes GPU compute during fine-tuning. Aggressive deduplication typically reduces dataset size by 20–45% while improving model quality — cutting training costs proportionally.
All processing occurs inside your own infrastructure. No data is transmitted to external AI APIs, cloud storage, or third-party agents. The pipeline runs on your hardware, under your security controls.
Every cleaned dataset is validated against the target model's context window schema before export. Output is guaranteed to be parseable by your fine-tuning framework with no manual post-processing.
Every transformation is logged. You get a complete audit trail showing exactly which records were removed, modified, or flagged — essential for regulated industries and model governance requirements.
Our pipeline runs entirely within your infrastructure across six automated stages, each configurable to your dataset type and compliance requirements.
Native connectors ingest data from SharePoint, SQL, S3, NFS, Confluence, email archives, and raw file systems. The parser normalizes encoding (UTF-8 enforcement), extracts text from PDFs and DOCX files, and segments documents into addressable chunks.
Exact deduplication via SHA-256 hash comparison eliminates verbatim copies. Near-duplicate detection uses MinHash LSH with configurable Jaccard similarity thresholds (default: 0.85) to catch reformatted, paraphrased, and partially overlapping content that exact matching misses.
Each record is scored on a multi-axis quality metric covering token length distribution, perplexity (low perplexity flags boilerplate), language identification, and structural coherence. Records below configurable thresholds are flagged for review or dropped.
A locally-deployed NLP model (spaCy + custom NER) scans every record for 40+ PII entity types: names, email addresses, phone numbers, SSNs, credit card numbers, IP addresses, medical record identifiers, and more. Detected entities are replaced with semantically consistent synthetic tokens that preserve grammatical structure and dataset utility.
Whitespace normalization, Unicode cleanup, HTML/XML tag stripping, and consistent punctuation handling are applied. For instruction-tuning datasets, records are restructured into the target prompt/completion format (Alpaca, ChatML, ShareGPT) based on your specified schema.
The cleaned dataset is validated against your target model's context window constraints and exported as JSONL or Parquet. A full processing report is generated: record counts in/out, PII entities redacted by type, deduplication rate, and quality score distribution.
Clean decades of case files, contracts, and deposition transcripts for a private legal LLM. PII and client data is redacted before any training, satisfying privilege and bar association requirements.
Preprocess EHR data and clinical notes for a HIPAA-compliant medical coding assistant. Patient identifiers are stripped and replaced with synthetic tokens — PHI never enters the training pipeline.
Build a proprietary financial research LLM from internal analyst reports, earnings call transcripts, and deal memos. Deduplication removes repetitive boilerplate; PII scrubbing removes personal financial data.
Clean Confluence wikis, engineering runbooks, and Slack exports for a private RAG system. Quality filtering removes stale, low-signal content; formatting normalization ensures consistent chunk quality for vector embedding.