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.
Natural language to SQL for your ERP
An assistant that turns a question in plain language into a database query, the approach usually called text-to-SQL, lets managers get figures without waiting on a report. The model writes only the query: every query is validated against the real schema and runs read-only, so the numbers in the answer come from database rows rather than from the model. We have built this inside a distributor ERP, set out in our ERP AI assistant case study. If you are weighing up an ERP chatbot over your own data, our write-up on why our ERP chatbot gave wrong answers covers what actually goes wrong and what fixed it.
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.
ERP AI Assistant: Plain-English Questions to Safe SQL
- A data dictionary the model can trust: Every column with its type, meaning, join keys and real values, pulled from the live database. Always-empty and misleading columns removed, business rules rewritten by domain, and forty golden question-to-SQL examples executed against the real database on every build.
- Live schema checking: At startup the application reads the live schema and drops any column or table the database does not have. Examples whose SQL no longer validates switch off automatically, and rules for new columns switch on once the data mart adds them.
- A validation layer in front of the database: Every query is parsed with Microsoft's T-SQL parser and bound against an allow-list of tables, columns, aliases and functions. Only read-only SELECT statements run. Validation errors and runtime errors each trigger one schema-aware repair.
- Server-side conversation memory: Recent turns are kept per user on the server, so follow-up questions work even from clients that send no history of their own.
- Fixing the data, not just the assistant: Inventory moved to the live item source. Refreshes now load into staging tables and swap in one transaction with a row-count guard, so a failed refresh cannot leave tables empty. Fields visible on ERP screens were added to the data mart: tracking numbers, shipment cost, carrier, rep names, return reasons and received quantities.
Also relevant: Order-to-cash ERP for a US orthotic and prosthetic manufacturing lab , 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.
How do you stop an AI assistant from inventing numbers?
The model only writes the query. Every query is parsed and checked against the real schema, only read-only SQL runs, and the numbers in the answer come straight from the database rows.
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.