// AI integration
AI integration for your existing systems, with a person making the final call
Can you use AI without ripping out the system you already have? Usually, yes. AI integration for existing systems can start with one small, testable check in a workflow you already run: the AI reads a document or submission against your team’s checklist, quotes what it found, and a person decides. You talk directly to the U.S.-based engineers who would build, host and maintain it.
// How a check works
AI quality checks: your checklist, an AI check, a person decides
The pattern we’d start with is simple enough that anyone can see what the AI did and why. Examples on this page are hypothetical.
Step 1
Your instructions become a checklist
We’d rewrite the instructions your reviewers already follow as specific, testable items: “every line over the receipt threshold has a receipt,” not “the report looks complete.”
Step 2
The AI checks each item
Each item gets Pass, Needs review or Fail, with the quoted text it relied on, so a reviewer can verify the call without re-reading the whole document.
Step 3
A person makes the final call
The result would land with whoever owns that decision today, in the system they already use. They accept it, override it or send it back.
Step 4
The result is logged
What the AI said and what the person decided. Overrides show where the checklist needs rewording, and the log records who approved what.
// Placement
Where a check could fit in an existing workflow
A check earns its keep at a hand-off, where a mistake gets more expensive once it moves on.
Before a submission is accepted
The person submitting sees what’s missing while they can still fix it, not days later in an email from the office.
Before a record moves to the next step
A request or job is checked before the next approver sees it, so reviewers spend their time on flagged items, not on every field.
Before something goes to a customer
A quote, letter or written reply is checked against your style or compliance guide before it’s sent, while a correction is still cheap.
// Examples
Examples (hypothetical): AI document review in everyday workflows
Made-up illustrations of AI document review automation, not client data. In each one, a person stays in charge of the outcome.
Example (hypothetical)
An expense report missing a receipt
Every line over the receipt threshold needs a receipt. The check matches lines to attachments and flags the hotel night with nothing attached, quoting the line. Finance approves the rest and sends one question back.
Example (hypothetical)
A written response checked against a guide
Before a customer reply goes out, the check compares it with your style or compliance guide: required disclosure present, correct product names, no promise the guide rules out. Flagged sentences go to a supervisor.
Example (hypothetical)
A purchase request with no quote attached
Ordinary code sees the request is over your quote limit. The AI does the reading: the only attachment is a product brochure, not a vendor quote, so the item fails and the request goes back before an approver sees it.
// Connecting safely
Connecting AI safely to existing and legacy systems
AI integration for existing systems shouldn’t mean rebuilding them, or handing an AI service the keys to your live database. Whether it’s a newer web app, an older SQL Server database or a Classic ASP application, we’d connect in the most limited way that works:
- Read-only access or a copy of the data, so nothing writes to your live system without review
- Existing permissions respected: the check sees only the records its user is already allowed to see
- Business rules stay in normal code. Limits, required fields and routing are ordinary, testable logic; the AI only does the reading
- A trial run against a copy of past records before anyone relies on it
No API? Older database?
A read-only feed, a small API in front of an old database, or the exports a system already produces can all give a check what it needs. An engineer looks first and recommends the safest route.
How we connect older systems →// Limits
Honest limits of AI checks
AI interprets text rather than following fixed rules, and it can be wrong while sounding certain. NIST’s Generative AI Profile calls this “confabulation”: “confidently stated but erroneous or false content” (NIST AI 600-1). So we’d design around these limits from the start:
- AI flags; it doesn’t certify. A Pass means “nothing found,” not “approved.”
- It isn’t legal or expert review. It can spot a missing disclosure; it can’t judge whether the wording is legally adequate.
- Accuracy has to be measured on your real documents, not a demo on someone else’s files.
- Unreadable scans and handwriting would go to Needs review instead of getting a guess.
Human-in-the-loop review: what the reviewer sees
We’d show each flagged item, the checklist line it relates to and the quoted evidence, with options to accept, override or send back, plus a note field that captures the reason for every override.
How to add AI to existing software →// Fit
When you don’t need custom AI work
If a product you already pay for has a built-in feature that does the job, use it. It will usually cost less than anything we’d build, and the vendor maintains it.
If the rule is mechanical (a blank field, a total over a limit, an expired date), you don’t need AI at all. A validation rule is cheaper and never guesses. We’d tell you that on the first call.
Custom AI integration makes sense when the check depends on reading, follows your own checklist and has to sit inside a system your team already works in.
Sometimes the fix is a better form
If reviews keep failing on the same missing field, a required field, a clearer approval step or a small internal tool may fix it without any AI.
Internal tools & workflow automation →// Running cost
What drives the running cost of an AI check
Cost depends on your volumes and your checklist, so we don’t put numbers on a web page. Providers publish their usage prices; see Anthropic’s API pricing and Amazon Bedrock pricing, for example. What moves the total:
- Volume: how many documents or submissions are checked each month
- Length: providers bill by tokens (chunks of text), pricing what you send and what comes back separately, so long documents and checklists cost more
- Model choice: each model has its own per-token price; we’d start with the least expensive one that passes your accuracy test
- Timing: both providers above publish lower batch prices than on-demand requests (for select models, in Bedrock’s case), for checks that can wait
- Review effort: time your people spend on flagged items. A check that flags too much just moves the work around
- Upkeep: re-testing when your checklist, your documents or the model change
Price the manual review first
Enter how often the review happens, how long it takes and how often mistakes need fixing, and see what the manual step costs you a year.
Automation ROI calculator →// Engagement
How a first AI integration project would run
One workflow, one checklist, measured before anyone depends on it, and the same crew hosting and maintaining it afterward.
Step 1
Pick one workflow
Your first call is with an engineer. We’d choose one hand-off where reviews pile up or mistakes slip through, and write the checklist with the people who review it today.
Step 2
Test on past examples
On a copy of past records with known answers, we’d compare the check with your team’s decisions and tighten the wording.
Step 3
Run it alongside your team
Before it affects anything, the check could run quietly on live work while people decide as usual, showing how often it would have agreed.
Step 4
Go live, log, maintain
A person still makes the final call and overrides are logged. When your process, documents or the AI model change, we’d re-test before your team sees the change.
// Selected work
Selected work: data, reporting and a platform we run
These aren’t AI examples. They show the groundwork a check depends on: production data in SQL Server supporting Tableau reports, and a platform we designed and still run. See all services.
Manufacturing · Data collection
Production, traceability and downtime data from the plant floor
A custom application where Chelten House operators record production performance, product traceability and equipment downtime (planned vs unplanned, with causes and durations), stored in SQL Server to support their Tableau reporting.
Our own platform · Restaurants
A platform we designed, built and still run
Just A Menu keeps QR menus, TV menu boards, print, websites and an API in sync from one dashboard, plus the JAM Player hardware.
// Credentials
The basics, before you ask.
Since 2018
Building and supporting software
Orlando, FL
100% U.S.-based team, clients nationwide
SAP Ariba
Registered supplier
NDAs welcome
Happy to sign before you share details
Top Web Design Companies
DesignRush
// FAQ
AI integration FAQ
What happens when the AI is wrong?
A person makes the final call on anything that matters, overrides are logged, and the checklist is refined over time. That’s why we’d start with checks that flag items for review rather than decisions that run unattended.
Who writes the checklist?
Your team would, with our help. Good checks start from the instructions your team already follows, written as specific, testable items.
Can AI connect to our legacy database without replacing it?
Usually, through read-only access or a copy of the data, so nothing writes to your live system without review. If the system has no API, see API integration services for other safe routes in.
How would we measure accuracy before rollout?
Run the check against real past examples where you already know the right answer, on a copy of the data, compare the results and adjust the checklist before anyone relies on it.
Is our data used to train AI models?
That depends on the AI provider and the account settings used. We’d review the provider’s data terms with you before anything connects to your data. Retention matters too: Amazon Bedrock’s data retention documentation, for example, shows that what’s kept can depend on both the account setting and the model.
How do we start?
Your first call is with an engineer. Bring the instructions your reviewers follow today and a few example documents with sensitive details removed. We’re happy to sign an NDA first.
// Keep exploring
More on adding AI and automation to what you run
Free tool
Automation ROI calculator
Free automation ROI calculator: price your manual processes, re-keying and spreadsheet work with your own numbers, then see payback and break-even.
Use the free tool →Guide
Add AI to existing software
How to add AI to existing software without rebuilding it: start with one check in one workflow step, keep a person in the loop and test it on past examples.
Read more →Service
API & database integration
Custom API integration services for the systems you already run: REST APIs, SQL Server databases and Tableau reporting, built by U.S.-based engineers.
Read more →// Bring us in
Tell us about the software your business runs on.
A new build, a system that’s hard to use, a stuck project or a prototype that needs to go live. An engineer, not a salesperson, will get back to you.
202-88-MAGIC · or email info@effortlesspresence.com