STRUCTURED DATA EXTRACTION
Structured data extraction from documents
We extract defined fields from documents into a validated schema. For an end-to-end intake and review workflow, we connect this with document processing.
- A schema you agree up front, so output maps cleanly to your systems
- LLM and VLM extraction that reads variable layouts, not just fixed templates
- Confidence gating that routes uncertain fields to review, not to a guess
Judged on what lands in your systems, not on the model behind it.
We lead with the deliverables, because that is what extraction is judged on.
Structured data extraction is one of our applied AI solutions and powers Intelligent Document Processing end to end. Feed the output into an approved downstream workflow to complete the task.
Source connectors
Ingest from email, upload, document stores, and scan streams, whatever your inputs look like. Meet the data where it lives.
Schema definition
We agree the exact fields, types, and formats you need, so output maps cleanly to your systems of record. Agreed up front, not reverse-engineered.
LLM and VLM extraction
Language and vision-language models read variable, unlabelled layouts, so a new vendor form does not break the pipeline. Variable layouts, not fixed templates.
Validation rules
Cross-field checks, reference-data lookups, and format enforcement catch bad extractions before they propagate. Errors caught before they spread.
Confidence gating
Every field carries a score; high-confidence values post automatically, low-confidence values route to review. A score on every field.
A review path and output
Reviewers correct only flagged fields; structured records land in your database, API, or workflow, ready to act on. Corrections become training signal.
Most enterprise data is not missing. It is trapped in documents no one designed for machines.
We measure precision on the fields that are hardest to extract.
Vendors quoting a headline accuracy number are almost always describing clean documents or a balanced test set.
Your reality is the long tail: the faded scan, the handwritten annotation, the form field someone used for the wrong purpose. On structured inputs, extraction precision is genuinely high; on that long tail it is lower, and no honest system pretends otherwise. So we design for it. We measure extraction precision on the messy long tail, not the easy majority, and we set the confidence threshold so uncertain fields are reviewed rather than trusted.
The result is a system that is dependable end to end: automation carries the clean cases, humans handle the genuinely hard ones, and the metric we report reflects the documents you actually process. We agree the target and baseline before we build, then measure against it in production.
Five steps, from a schema to precision that keeps rising.
Define
Agree the schema and the metric that matters: extraction precision on your real document mix.
Build
Extraction and validation built against a labelled sample from your long tail, not a clean subset.
Gate
Confidence thresholds set to your risk appetite, so uncertain fields are reviewed, not trusted.
Integrate
Structured output flows into your systems of record, ready to act on.
Improve
Reviewer corrections lift precision on the documents you see most.
Established OCR, LLM and vision extraction, on your platform.
A representative stack by layer. We build cloud-native on the platform you already run.
We build cloud-native on your platform. See our Scaled GenAI and AI Platforms practice. We report extraction precision on the long tail, not a vanity number. Data handling aligns to Responsible AI and Governance.
From thousands of re-keyed forms to automatic records.
Challenge: A [global enterprise client] re-keyed data from thousands of inconsistent [DOCUMENT TYPE] each week.
Approach: LLM and VLM extraction with validation and confidence gating, measured on the long tail.
Result: Most fields extracted automatically; effort concentrated on genuine exceptions. (Softobiz to verify.)
Part of a portfolio built to an accuracy bar.
Intelligent Document Processing
The full document workflow this extraction core powers, ingest through straight-through posting.
Enterprise Knowledge Assistant
RAG over your content with citations, once your data is structured and searchable.
Demand Forecasting
Clean, structured inputs feeding models measured on WAPE across SKU, store, and region.
Fraud Detection
Precision and recall tuned to expected loss, another workflow engineered to its metric.
GenAI Email Categorization and Automation
Classify and draft on inbound queues, the same confidence-gated pattern for email.
Applied AI Solutions
The parent portfolio this solution belongs to, each engineered to an accuracy bar.
What teams ask us first.
High extraction precision on structured inputs, lower on the messy long tail, which is why confidence gating routes uncertain fields to review. We set the target and baseline up front and measure in production.
No. LLM and VLM extraction reads variable layouts, so a new format does not require a new template. Validation rules still enforce your schema.
Reviewer corrections on flagged fields become training and tuning signal, so precision rises on the document types you process most.
Tell us where re-keying is slowing you down, we will show the precision extraction can reach.
On your real documents, measured on the long tail, with uncertain fields routed to review rather than trusted.