Business Process Automation With AI
We automate document-heavy, repetitive work, from medical records to daily data loads and support requests, and deliver the results into the ERP, CRM, and data systems you already run.
Read and checked against your rules
- InvoiceERPPosted
- Purchase orderERPPosted
- Support emailCRMChecking
- Scanned contractSent to a person
EXCEPTION QUEUE
A person reviews
Approves or corrects flagged items
What Is Business Process Automation?
Business process automation uses software to complete repeatable steps in a workflow, such as data entry, routing, matching, and approvals, so people don't have to. Traditional automation follows fixed rules and works well on structured inputs: the same form, the same fields, every time.
AI extends that to unstructured inputs like invoices, emails, contracts, and scanned PDFs. A language model or OCR service reads the document, pulls out the fields that matter, and checks them against your rules and records. The result lands in the system of record, and anything uncertain goes to a person.
Rules-based automation vs. agentic automation
Rules-based automation, including robotic process automation (RPA), works when inputs never change. AI reads inputs that vary, like invoices and emails. Agentic automation goes further, with agents that look up records and propose the next step. People still handle exceptions.
WHEN TO AUTOMATE
Signs a Process Is Ready to Automate
A process is ready to automate when it's repeatable, high-volume, and eating hours your team would rather spend elsewhere. Watch for these signs.
High volume, same steps
Your team handles hundreds or thousands of the same documents or requests every week, and each one follows roughly the same path.
Re-keying between systems
People copy data from emails, PDFs, or exports into the ERP or CRM by hand, and the mistakes show up later in reports.
Month-end backlogs
Work piles up at month end or in peak season, and the only fix anyone has tried is overtime or temporary staff.
Errors caught late
Mistakes surface weeks later in reconciliation, billing, or a customer complaint, when they cost far more to fix than at intake.
Rules that live in one person's head
The process works only because one experienced person knows every exception, and none of those rules are written down anywhere.
DOCUMENTS
Intelligent Document Processing
Intelligent document processing reads documents the way a person would, pulls out the data that matters, checks it, and sends it where it belongs. It turns PDFs, scans, and emails into structured records your systems can use.
We've applied intelligent document processing to medical records, hospital billing files, and daily data exports. For a healthcare technology company, we built a pipeline on Amazon Bedrock and AWS Lambda that reads Epic medical records, from a few pages to hundreds, extracts medications, dosages, administration methods, and physician notes, and outputs standardized JSON for downstream systems. The pattern is the same across document types. Extraction pulls the fields, validation checks them against your rules and records, and routing sends clean records onward while flagging the rest for a person to review.
INPUTS
PDFs
Scans
Emails
EXTRACT, VALIDATE, ROUTE
Extraction
Pulls the fields
Validation
Rules and records
Routing
Sends records onward
WHERE RECORDS LAND
System of record
Clean records
Exception queue
Flagged for a person
Automated invoice processing and accounts payable automation
Automated invoice processing works best when it reuses tools you already have. In the approach we recommend, an existing BI or integration job picks up each invoice, for example from Microsoft Dynamics, finds the matching contract, and sends both to a language model. The model answers yes, no, or not sure: is the price, rate, or not-to-exceed amount right? Confident matches clear, and everything else lands in an exception queue for AP. That's accounts payable automation without a new platform, and AP still approves anything the model flags.
Other documents
The same pattern works for other document types we've handled: hospital billing records cross-checked against clinical documentation, real estate listing photos turned into complete MLS listings, and daily client data exports reformatted and loaded into a CRM. Each starts with the fields that matter and the rules they must pass.
WHAT WE AUTOMATE
What We Automate
Our intelligent automation solutions take on the repetitive, document-heavy steps between systems, and keep people in charge of the decisions.
Daily data loads
For a regional bank, we replaced the manual download, reformatting, and upload of about 30,000 client records a day with an AWS pipeline that runs daily without intervention.
Medical record extraction
For a healthcare technology company, Amazon Bedrock extracts medications, dosages, and physician notes from Epic records and outputs standardized data, cutting the time staff spent on manual review.
Billing review support
For a healthcare solutions client, a RAG-based system summarizes long clinical records and cross-references them with billing lines, so medical reviewers spend less time per patient.
Support routing and triage
Agents read support issues, check the order, logging, and back-end systems involved, and propose a fix for a person to approve. For multi-step work like this, see AI Agent Development.
Order and scheduling checks
For a custom windows and home services company, automated checks on sourcing, pricing, and scheduling after each order is entered cut wrong orders and mispriced quotes.
Listing content from photos
For a real estate startup, automation scans listing photos, identifies the home's features, writes the description, and fills in the MLS fields agents tend to skip.
OUR APPROACH
How We Build Automation That Holds Up
Automation projects fail after the demo because the demo handles the happy path and production doesn't. As JBS Dev puts it: "What we hand over is a working system inside the client's environment, with integrations, guardrails, observability, error handling, and human oversight built in."
Built for the demo
- Guesses when it isn't sure
- Bad records in the books
- A new portal to log into
- Data stuck in another vendor's platform
Built to hold up
Exceptions go to a person
When the system isn't sure, it says so. Our AI automation services route low-confidence results to an exception queue where a person decides, instead of guessing and pushing a bad record into the books.
People approve what matters
Automation proposes and people approve. Anything where a mistake would be costly, like releasing a payment or a vehicle title, waits for human sign-off or a hard rule enforced in code. Every approval is logged in an audit trail.
Good, not perfect
AI won't be right every time, so we design for that from day one. Getting most of the volume through cleanly is valuable; chasing the last few percent can cost more than the first eighty.
Built into your systems
We plug automation into the ERP, CRM, BI jobs, and integration tools you already run, so results land where your team works. No new portal to log into, and no data stuck in another vendor's platform.
Messy data and legacy systems
You don't need perfect data to start. Language models handle misspellings, inconsistent formats, and missing fields in ways older automation and machine learning couldn't. You don't need a rebuild first, either: automation can wrap the systems you have and use the business rules already in them. Where a system really does block progress, we'll say so and point to legacy application modernization.
HOW IT WORKS
How an Automation Project Runs
STEP 1
Pick the process
Start with one process, often through an AI readiness assessment. We ask: if this works exactly as planned, will it pay for itself? If not, we pick a different one.
STEP 2
Prove it on real documents
A proof of concept runs against a sample of your real documents and systems, so you see accuracy and exceptions before committing to a full build.
STEP 3
Build and connect
We connect extraction, rules, and routing to your ERP, CRM, or data platform, and set up the exception queue and approval workflows your team will use.
STEP 4
Run alongside the old process
At first the automation runs in parallel with the manual process, so results can be compared and trust built before the old way is retired.
STEP 5
Expand
Once one process runs cleanly, the next one is faster to build. We reuse the pattern and expand from there.
What your team gets at handoff
You own everything we build, and it runs in your environment. Services we can provide at handoff include knowledge transfer sessions and documentation covering how the automation works, where exceptions go, and how to adjust its rules. Many automations run on tools your BI and data teams already maintain, and ongoing support is also available.
RESULTS
Business Process Automation Results
Published JBS Dev projects. Explore the work, approach, and results.
30,000
Records a day, with no manual work
Financial Services
Automating 30,000 Daily Records for a Regional Bank
Challenge: Staff moved about 30,000 records a day from FIS DataConnection to ActiveCampaign by hand, after two to three years of failed automation attempts.
Result: An AWS Lambda and Python pipeline now runs the transfer daily with no manual work, delivered in just under four months.
Read the regional bank case study1,000s
Of medical records, now structured data
Healthcare
Medical Record Extraction for a Healthcare Technology Company
Challenge: Epic medical records arrived as PDFs in inconsistent formats, and manual review was slow.
Result: Amazon Bedrock models and AWS Lambda now extract the data into a standard format, with less manual work and more accurate, timely reports.
Read the healthcare technology case study<10 min
Per listing, down from up to a full day
Real Estate
AI Listing Creation for a Real Estate Client
Challenge: Realtors spent hours entering MLS listings by hand on outdated platforms.
Result: An Amazon Bedrock platform and a Chrome extension cut listing creation from up to a full day to under 10 minutes.
Read the real estate case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
COST AND TIMELINE
What Drives Automation Cost and Timeline
Cost and timeline depend mostly on document variety, volume, and how many systems the results must reach. A single document type flowing into one system is a much smaller project than many formats across several systems.
- 01Document volume
- 02Document variety
- 03Systems the results must reach
- 04Exception handling
- 05Approval steps
- 06Model usage
How the timeline is set
A focused automation can reach production in months, not years. For a regional bank, a daily process handling about 30,000 records went from kickoff to fully automated in just under four months. Your timeline depends on document variety, integrations, and review steps, and the assessment or proof of concept sets it before the build starts.
How cost is set
The main cost drivers are document volume and variety, the number of systems the results must reach, the exception and approval steps you need, and model usage. Running models only when a document arrives, rather than constantly, keeps token costs down. The proof of concept shows real volumes and exception rates, which is what the estimate is built on.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you know a process is costing you but not how to automate it. We review the workflow, the documents, and the systems involved, apply the ROI test, and recommend where to start. You get a roadmap you can act on with us or on your own.
- Workflow and document review
- Systems review
- ROI test
- Where to start
- Automation roadmap
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to automate a defined process.
For a defined process, senior engineers build the automation end to end: extraction, validation, routing, exception queues, and integration with your ERP or CRM. It runs in your environment, and you own everything we build, with knowledge transfer and documentation for your team.
- Extraction and validation
- Routing
- Exception queues
- ERP and CRM integration
- Knowledge transfer and documentation
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support for the systems we build or modernize. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Architectural guidance
- Hands-on development
- QA
- Ongoing support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose an Automation Partner
Ask any automation partner these questions before you sign; here's how we answer them.
- Will it work with our existing ERP and data?
- Yes. We connect to the systems you already run, and you don't need to clean up your data or rebuild your legacy systems first.
- What happens when the system isn't sure?
- It sends the item to a person. Exceptions land in a review queue, and costly actions wait for human approval.
- Who owns what you build?
- You do. It runs in your environment, and you own everything we build.
- Who does the work?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
Business Process Automation FAQs
What is business process automation?
Business process automation uses software to complete repeatable workflow steps such as data entry, routing, matching, and approvals. Modern automation adds AI, so it can read unstructured inputs like invoices, emails, and PDFs, not just structured forms. People stay in charge of exceptions and approvals, while the routine volume moves through without them.
How is AI automation different from RPA?
RPA follows fixed scripts, while AI automation can interpret inputs that vary. RPA clicks through screens the same way every time and breaks when a layout or format changes. AI automation reads documents and messages, extracts meaning, and checks it against your rules. We often combine both: fixed rules where inputs are stable, and AI with human review where they aren't.
Can you automate invoice processing into our existing ERP?
Yes, in most cases, and without replacing your ERP. Automated invoice processing can run on BI or integration jobs you already have: pull the invoice, find the contract, ask a language model whether price and terms match, and route anything uncertain to AP. Confident matches clear, and exceptions wait for a person. We confirm ERP specifics, such as Microsoft Dynamics, during the assessment.
What happens when the system isn't sure?
It hands the item to a person. Low-confidence extractions and anything that fails a rule go to an exception queue instead of into your systems. Actions where a mistake would be costly wait for human approval, and recurring mistakes are fed back into the rules or prompts so they don't repeat.
Our data is messy. Can we still automate?
Yes. Language models handle misspellings, inconsistent formats, and incomplete fields far better than older automation or machine learning did. You don't need a data cleanup project or a legacy rebuild before you start. We begin with the documents and systems as they are, and fix only what actually blocks the automation.
Does our data leave our environment?
Not outside your control. Automations run in your environment, and where a hosted model is used, enterprise agreements bar the provider from training on or retaining your data. We apply the same controls you use for email: company licenses only, no personal accounts, and access limited to what each process needs.
How long does an automation project take?
A focused automation can reach production in a few months. For a regional bank, a daily process handling about 30,000 records was automated in just under four months. Timing depends on document variety, integrations, and review steps, and the assessment or proof of concept sets it before the build starts.
