AI Integration Services
We add AI to the business systems a company already runs, without rewriting them. Work in production includes extracting structured orders from scanned paperwork, matching inconsistent free-text records, OCR matched to ERP orders and on-premises document processing, with an AI assistant for plain-language ERP questions now in development.
+ RULES
The scope, and what has to get right.
- Document extraction from scanned paperwork. Handwritten and checkbox data from scanned forms extracted into structured records that create real orders, removing manual re-keying for paper that still arrives by fax or email.
- OCR matched to the right record. Packing slips, carrier labels and emailed attachments read automatically and attached to the correct order, with an exception queue for anything that cannot be matched with confidence.
- Entity resolution from free-text data. Inconsistent, free-text records, like practice or company names typed differently every time, matched automatically to the correct canonical record.
- Plain-language questions about ERP data. An AI assistant that lets staff ask questions about inventory, orders, customers and sales in everyday language, answered from the system's own data. This is in active development inside a live multi-marketplace ERP.
- Choosing a cloud API or an on-premises model. A cloud AI API where volume and data sensitivity allow it, or a local model on your own hardware where cost or data-residency requirements rule out sending documents to a third party.
- Integration into an existing system, not a bolt-on tool. AI capability wired into the application's own workflow, triggered from the same screens staff already use, with results written back into the system of record.
Inside the system, not bolted on.
Start from a real operational task
Each integration targets a specific manual step, such as re-keying a paper work order or matching a document to an order, so the result can be checked against how the work was done before.
Several integrations in production systems
In one manufacturing ERP, the Claude API turns scanned work orders into structured orders and a separate OpenAI integration resolves practice names. In a multi-marketplace ERP, OCR matches packing slips and labels to orders.
No rewrite required
AI is added to the existing application's workflow. The surrounding system does not need to be rebuilt to accommodate it.
People stay in control of exceptions
Extracted data lands in normal screens where staff can review it, and anything the process cannot match confidently goes to an exception queue instead of being guessed.
Cost and privacy considered upfront
The choice between a cloud API and a local model depends on volume, latency and data sensitivity. For one legal services client, documents are processed on an on-site AI server orchestrated by n8n.
Order-to-cash ERP for a US orthotic and prosthetic manufacturing lab
- Browser-based measurement viewer: A Three.js application inside the existing ERP displays left and right foot scans side by side, with preset and custom named measurement lines placed by clicking two points on the mesh. Measurements save in the background without reloading the page, adding a modern client-side tool to an existing application without a platform rewrite.
- A digital work order that mirrors the paper form: Every clinical field is driven by an admin-configurable option table rather than hardcoded, so the form can change without a code change. A two-way messaging thread between doctor and lab is attached to each order and triggers email on status change.
- Two independently scheduled stages: Glue and lab production run as separate scheduling grids with their own shop-floor screens, plus landed-cost purchasing that rolls duty, tariff and freight into a computed cost per unit at the time of receiving.
- Self-service for doctor practices: A permission-gated portal lets each practice submit orders through the same screen the lab uses internally, track status, view invoices, and see a patient-level re-order reminder list, with OTP authentication across multiple practices per login.
- Two separate AI integrations in production: Scanned paper work orders are extracted directly into structured digital orders through an AI document pipeline, and a separate integration resolves inconsistently written practice names on incoming orders to the correct physician record.
Also relevant: An on-premises AI document processing pipeline , Multi-marketplace ERP, EDI and logistics automation for a US consumer electronics brand , A web layer and B2B portal over a commercial ERP
Common questions.
Do we need to rebuild our system to add AI to it?
No. Our AI integrations were added to existing production systems and triggered from screens staff already use, without changing the surrounding application.
What kinds of problems has AI integration actually solved?
Turning scanned handwritten work orders into structured digital orders, matching inconsistently written practice names to the correct record, and processing varied-layout legal documents into structured data. OCR also matches packing slips and labels to ERP orders.
Can staff ask questions about our ERP data in plain English?
That is the goal of an AI assistant we are currently building into a live ERP, so users can ask about inventory, orders, customers and sales without building a report. It is in active development, not a packaged product.
Cloud API or a local model, which should we use?
It depends on volume and data sensitivity. A cloud API is simpler at moderate volume. A local, on-premises model removes per-request cost and keeps documents on your own hardware, which matters for legal, healthcare or government data.
How accurate is AI extraction?
Accuracy depends on the documents and the data, so we do not promise a fixed rate. Integrations are designed so staff can review results and exceptions go to a queue rather than being silently accepted.
Is AppsXone an AI company?
No. We are a custom software engineering company. AI is one capability we apply inside ERP systems, business applications and integrations when it solves a real operational problem.
Is this the same as AI document automation?
AI document automation is one application of this, focused on document processing pipelines. AI integration covers the broader range of adding AI capability to an existing system.
Where this connects.
What manual data entry are you doing that a model could do instead?
Tell us what the data looks like and where it comes from, and we will tell you honestly whether AI is the right tool for it.