Extract, classify and summarize the data in your documents, emails and text, with accuracy you can measure.
Stack we work in
Figures as of Oct 2026
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We build document AI and natural language processing (NLP) systems that read invoices, contracts, forms, IDs and emails, extract the data you need and send it into your ERP, CRM or workflow tools.
The same methods classify and route documents, summarize long text, pull out names and terms, and analyze sentiment and topics across large volumes. Human review handles uncertain cases, and accuracy is measured on your own documents before and after launch.
Most business data still arrives as PDFs, scans and emails that someone has to read.
Staff retype invoice, order and form data into business systems, which is slow and causes errors.
Every supplier, customer and form version looks different, so template-based tools break.
Emails and documents arrive in shared inboxes and wait until someone reads and forwards them.
Contracts, reports and feedback hold useful information that nobody has time to read at scale.
Without accuracy measurement and review, teams cannot rely on what the system extracts.
Outcomes
The document types, fields, rules and target systems, agreed before the build.
A set of your documents with correct values, used to measure accuracy for every field.
Ingestion, OCR, extraction, classification and validation, running in your environment.
A review interface for low-confidence fields, with the original document beside the extracted data.
Extracted data sent to your ERP, CRM, document store or workflow tools, with an audit trail.
Accuracy, review rates and throughput tracking, plus repositories and a runbook handed over to you.
Extract supplier, totals, tax and line items from invoices and match them to purchase orders.
Pull out parties, dates, renewal terms and obligations, and flag clauses that differ from your standard.
Read IDs, proofs of address and application forms, and check them against your rules.
Classify incoming emails and tickets by type, urgency and team, and route them automatically.
Analyze reviews, surveys and support notes for sentiment and recurring topics.
Make large archives of reports and records searchable by meaning, not only by keyword.
Built with
Built with
We build a labeled test set from your real files and report field-level accuracy before go-live, not a vendor's benchmark figure.
Confidence scores, validation rules and review screens route uncertain fields to a person, so automation never silently corrupts a record.
Azure AI Document Intelligence, Textract, Google Document AI, open models or LLM extraction, picked by accuracy and cost on each document class.
Each extracted value links back to its page and source, and integrations with ERP, CRM and document systems keep records consistent.
Work runs under NDA, and processing can run inside your own cloud account or on private models when documents cannot leave your environment.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We collect sample documents, map the fields and decisions needed and agree where the extracted data must go.
You receive Document inventory and field map
We label a representative set of your documents so accuracy can be measured for every field.
You receive Labeled test set
We compare cloud document services, open-source models and LLM extraction on your test set for accuracy, cost and speed.
You receive Approach comparison and recommendation
We build ingestion, OCR, extraction, validation rules and the review screen for low-confidence results.
You receive Working document processing pipeline
We connect the pipeline to your ERP, CRM, email or document systems and launch with a pilot group of documents.
You receive Integrated production pipeline
We track accuracy, review rates and throughput, retrain or retune on corrected data and add new document types.
You receive Accuracy dashboard and improvement plan
Tech stack
AI and machine learning
Databases
Backend and APIs
Languages
Cloud and DevOps
Other tools
Cost depends on the number of document types, how varied the layouts are, the number of fields, the accuracy needed, the review workflow and the systems the data must reach. We usually start with a fixed-scope pilot on one or two document types, then estimate the full rollout. Per-page cloud service and model costs are estimated separately. As a guide, a focused AI pilot usually costs $10,000 to $50,000, and a full custom AI build $40,000 to $150,000 or more (USD).
A pilot on one or two document types often takes a few weeks. A production pipeline with several document types, review screens and integrations typically takes a few weeks to a few months, depending on scope.
It depends on document quality and layout variety, so we measure it on a labeled set of your own documents before launch and report accuracy per field. Confidence scores and validation rules send uncertain results to a person, so errors are caught before they reach your systems.
We choose by testing. Cloud services such as Azure AI Document Intelligence, AWS Textract and Google Document AI work well for standard forms and invoices. Open-source models suit private deployments, and LLMs help with varied layouts and free text. Many pipelines combine them.
Yes. OCR handles scanned and photographed documents, and current services read many forms of handwriting. Accuracy on poor scans or difficult handwriting is lower, so those cases are routed to human review.
Each extracted field gets a confidence score. Fields below a threshold you set appear in a review screen next to the original document, where a person confirms or corrects them. Corrections are saved and used to improve the pipeline.
Documents are processed in your cloud account or approved region, encrypted in transit and at rest, and kept only as long as you set. Access to review screens follows your user permissions, and we use enterprise model services whose business terms do not use your data for training. Data handling is designed around GDPR and the local data protection rules that apply to you.
You own the code and IP we build for you, including pipelines, extraction prompts, trained models and labeled data. Your documents stay in accounts you control.
Yes. We connect to your email, document management, ERP and CRM systems, work in your repositories and can hand the review workflow to your operations team.
We monitor accuracy, review rates and throughput, retune on corrected data, add new document types and handle changes in layouts. This runs on a monthly support plan, or we hand over to your team with documentation and training.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. Share 50 to 100 anonymized sample documents across the types you handle and the fields you need, and we return a feasibility check with expected accuracy.
Book a free consultation. We will review sample documents and suggest a document AI pilot.
