Data Processing Services — Clean Data In, Better Decisions Out

We work with structured and semi-structured data in any common format — CSV, Excel (all versions), XML, JSON, database exports (SQL, Access), PDF-extracted text data, and data extracted from web sources. We can also work with data that has been pre-processed by OCR tools from image or scanned-document sources. For unusual or proprietary formats, we ask you to send a sample file and we will advise on feasibility and any pre-processing steps required. We do not process raw audio, video, or non-textual binary files without a prior extraction stage.

Every record that fails validation or cannot be transformed to meet the target specification is logged in an exception report — not silently approximated, not processed with a default value, and not held indefinitely without notification. The exception report is delivered with every processing batch and contains: the record identifier, the specific field or rule that caused the exception, the original value, and (where we have enough context) a suggested resolution for your review. You confirm the resolution approach and we apply it. Over time, recurring exception patterns are used to update the processing rules and reduce future exception rates.

Turnaround depends on volume and processing complexity. As a practical guide: 10,000 records with standard validation and formatting — same-day processing, typically under four hours. 100,000 records — one to two business days. 1,000,000 records — project-scoped, typically five to ten business days depending on team size. For ongoing scheduled batches, we commit to a specific SLA window — for example, all data received by 22:00 GMT will be processed and delivered by 06:00 the following morning. We will agree your specific window in the scoping call.

We use a combination — the right tool for each task. Where rule-based automation (Python scripts, data transformation tools, macro-enabled Excel processing) can reliably handle a task with full accuracy, we use it to improve throughput and consistency. Where human judgement is required — interpreting ambiguous values, applying contextual merge rules, reviewing exception items — we use trained agents. Critically: all output, regardless of how it was produced, goes through a human review stage before delivery. We never deliver unreviewed automated output.

Yes. For engagements involving personal data from EU or UK data subjects, we execute a Data Processing Agreement (DPA) as standard and operate under a GDPR-compliant processing framework — including data minimisation, access controls, processing logs, and data subject request handling where relevant. For US engagements involving California residents' data under CCPA, we apply equivalent access and processing controls. For healthcare data subject to HIPAA, we implement a HIPAA-compliant operating configuration. The appropriate agreement should be discussed and executed before any personal data is transferred to us for processing.

We apply a configurable confidence-scoring model to all deduplication work. Records are only automatically merged when they meet a high-confidence threshold — typically 95%+ match across your defined primary and secondary key fields. Records that fall into a medium-confidence band (70–94% match) are flagged in a review list for your team to confirm before merging. Records below the lower threshold are treated as distinct. The confidence thresholds and key field weightings are agreed with you before processing begins, and the deduplication report shows the full decision logic for every merge action performed.