The spreadsheet someone rebuilds by hand every morning.
Every morning, someone on the team opens the overnight documents, invoices in email attachments, forms, supplier PDFs, and keys each line into the ERP by hand. A few hundred documents, a few hours, every day. When volume spikes, the backlog grows and the error rate climbs with it.
Now the same documents arrive and land themselves. The pipeline reads each one, extracts the fields your ERP needs, validates them against expected formats, and writes the record. What reaches a person is the handful of low-confidence extractions flagged for review.
The extraction is not a macro taped over a broken process. It reads unstructured documents, checks its own work, and knows when it is unsure. The manual queue is the part that disappears.
Every business has data in places it can't easily reach. Invoices in email attachments that need to be keyed into the ERP. Product data on supplier websites that needs to be in your catalogue. Report data in PDFs that needs to be in your analytics database. Application data in forms that needs to be in your CRM.
Manual extraction is the solution that scales linearly with volume. When the volume doubles, the headcount doubles. When the volume spikes, the backlog grows and accuracy drops. Automated extraction changes the relationship between data volume and processing cost.
According to Gartner, 80% of financial document processing will be handled by AI by 2027, up from approximately 30% in 2024. Businesses that move early lock in the cost and accuracy advantages before competitors catch up.
RaftLabs has built AI OCR pipelines, document extraction, web scraping, and enterprise data integrations for clients including Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin, across healthcare, fintech, logistics, and industrial operations. One production OCR pipeline processes over 20,000 transactions a day with manual errors eliminated; a multi-source pipeline delivers structured output to ERP at 97% field-level accuracy on digital PDFs. The team that scopes your extraction problem is the team that ships it, with no offshore handoff after the contract is signed.
Automation pays off when the extraction is repeatable and high-volume.
Everything on the left should already be true for your operation. Even one thing on the right, and a direct integration or a one-off manual pass is the smarter first step.
A fit01A repeatable, high-volume extraction task: someone keys data from PDFs, invoices, emails, or a legacy system into your ERP, database, or warehouse every day.
02The source is consistent enough to template, or varied enough that AI extraction beats rules, and you have a target schema for structured output to land in.
03You need accuracy validation and exception handling, not blind trust, and budget for a build from $15,000.
Not a fitA one-time, low-volume extraction you could clear by hand faster than automating it.
The data already flows through a clean API or structured feed you can integrate directly, with no extraction step needed.
Every record needs human judgment, not straight-through processing with an exception queue for the flagged cases.
What we build
What we extract and where we deliver it
01Document OCR and extraction
AI reading of any document, invoices, contracts, application forms, purchase orders, regulatory filings, with extraction of the specific fields your downstream systems need. LLM-based extraction resolves ambiguities pure OCR cannot, digital PDFs reach 97-99% field accuracy, and scans are pre-processed to handle variable quality. Built on Azure Document Intelligence, Google Document AI, Textract, and OpenCV.
Automated collection of pricing data, product catalogues, competitor intelligence, and regulatory disclosures from websites, on your schedule and at any volume. Anti-bot measures are managed with rotating proxies and fingerprint randomisation, change detection alerts when a page layout shifts, and output lands deduplicated in your warehouse in your target schema. Built with Playwright, Scrapy, and delivery into PostgreSQL, BigQuery, or Snowflake.
03Email and attachment extraction
Extraction triggered by email arrival: supplier invoices, order confirmations, shipping notifications, remittance advices. The pipeline monitors designated inboxes, routes each message to the correct extraction workflow by sender, subject, or content, and lands structured data in your ERP, database, or CRM within about 60 seconds, with failures routed to an exception queue.
04Legacy system screen scraping
For systems built before APIs existed, 20-year-old ERPs, government portals, partner portals with no data feed, we build browser automation with Playwright that logs in, navigates, extracts, and delivers to your modern platform on a schedule. Selector failure detection alerts when a UI changes rather than silently producing empty output, and extracted data passes the same validation and deduplication as every other pipeline.
05Database and API data extraction
Extraction from relational databases, REST and GraphQL APIs, SaaS platforms, EDI feeds, and SFTP transfers, spanning Salesforce, NetSuite, and SAP. Incremental extraction pulls only new or changed records instead of reprocessing everything each run, pagination and rate limits are handled per source, and data is typed and schema-validated before landing in your warehouse.
06Validation and exception handling
Every extraction pipeline includes multi-layer validation: format checks, range validation, cross-field consistency, and business logic gates. Confidence scores route records straight through, to spot-check, or to an exception queue where reviewers correct only the flagged fields, and corrections feed model retraining so accuracy improves on the exact variants that failed.
Every pipeline ships with accuracy and throughput monitoring, so you see straight-through processing rates, exception volumes, and time savings from the first week in production. Most systems hit 85-95% straight-through processing rates within 30 days, and monitoring flags a drop in accuracy as an early signal that a source format has changed, before it becomes a backlog.
Compliance requirements are scoped in week 1, not retrofitted before launch. We have shipped HIPAA-compliant data pipelines for US healthcare clients and GDPR-compliant extraction systems for European markets, and build to SOC 2 access-control and logging patterns where an enterprise deployment requires it.
What data are you extracting manually today?
Tell us the source and the destination. We'll design the automation and give you a fixed cost.
How it works
From scope to shipped
Every extraction project follows the same four phases. Scope is locked and price is fixed before development starts.
- Week 1
01Audit and scope
We map every data source, the target system, and the volume. You leave week 1 with a written scope document, a data model, and a fixed-price quote. No development starts without your sign-off.
- Weeks 2-3
02Architecture and extraction design
Extraction templates, validation rules, and exception-handling logic are designed before any code is written. Decisions made here cost ten times less than the same decisions made in week 8.
- Weeks 4-10
03Build, integrate, and QA
Working pipeline at a staging environment by the end of sprint one. Bi-weekly demos. QA and accuracy validation run in parallel with every sprint, not as a phase at the end.
- Weeks 10+
04Launch and post-launch support
Production deployment with monitoring and accuracy dashboards activated on launch day. 8 weeks of post-launch support included in every project to handle format changes and edge cases as they surface.
Where you land in that range depends on scope, not negotiation:
- Focused extraction system, $15,000-$35,000
- One document type, one output target, scoped, built, and delivered to your ERP, database, or data platform.
- Multi-source pipeline, $40,000-$100,000
- Multiple sources with complex transformation logic and multiple output destinations. Web scraping projects vary by site complexity and anti-bot measures.
What it costs
Automated extraction, fixed price, scoped before we start.
A written scope, a data model, and a fixed-price quote in week 1. No development starts without your sign-off.
$15,000-$100,000Fixed cost by project. Focused extraction systems start from $15,000. We scope every project before pricing it.
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a priced change request, never a surprise on the final invoice.
Fixed price
We scope the work, calculate the cost, and lock it in writing before any development starts. A scope change is a priced change request, agreed before work begins, never absorbed into the project.
Post-launch support
Every project includes 8 weeks of post-launch support to handle format changes and edge cases as they surface, plus monitoring that alerts you before an accuracy drop becomes a backlog.
