AI & Automation
Text & Language Intelligence
3–8 weeksDocument → dataDemo
Fields extracted
24
per document
Accuracy
99.1%
per field
Manual work
−80%
less data entry
All the text your team processes by hand or ignores because of volume — invoices, forms, contracts, emails, tickets, reviews, and social conversation — turned into structured data and actionable signals.
We extract fields, classify, detect entities, and measure sentiment and topics, over both internal documents and your brand's external conversation. Unlike conversational assistants, these pipelines process in batch and produce data, not chat answers. Technically: extraction with field schemas and validation rules, NLP for classification and entity extraction, domain-fine-tuned sentiment models, topic analysis (BERTopic / LDA), and structured output (JSON, CSV, database, API) with human review for low-confidence cases.
Use cases
- —You manually copy data from invoices, forms, or emails into a database or system
- —Your team repeatedly classifies tickets, requests, or emails by hand
- —You need to extract specific fields from contracts, reports, or case files
- —You want to measure sentiment, topics, or reputation across reviews, social, and customer feedback
- —You have PDFs or unstructured documents slowing down internal processes
Deliverables
- —Extraction pipeline with field schema and validation rules per document type
- —NLP pipeline for classification, entity extraction, and text analysis
- —Fine-tuned sentiment model and topic analysis (BERTopic / LDA)
- —Structured output in the required format (JSON, CSV, database, API)
- —Human review workflow for low-confidence cases and per-field quality metrics
- —Coverage, limitations, and maintenance guide documentation