Services
AI integration services for the systems you already own
Two capable systems and one person re-keying between them. That is what we find at most companies, and a large share of it is not an AI problem at all. It is two systems that do not exchange data. We build the integrations that connect them, and we add AI only where it earns its place: reading documents, classifying, handling exceptions.
Illustrative workflow: connecting order processing, fulfillment and accounting.
The person in the gap
Your TMS knows the load. Your accounting system knows the invoice. Your field-service app knows the job. Your payroll system knows the hours. Each one is fine. None of them talks to the others, so someone spends part of every day copying between them, and the numbers never quite match.
That person is the integration. They are expensive, they make mistakes at 4:45 on a Friday, and when they are out sick the data stops moving. Replacing that with software is the least glamorous and highest-return project most operations companies have.
Who owns what
IllustrativeEach system keeps its job. The integration moves the facts between them.
The integration
Owns the rules: which record is the source for each field, what moves when, and what happens when something does not match.
Field-service app
Owns the job: customer, site, work done, photos, technician hours.
Accounting system
Owns the money: the invoice, the payment, the ledger.
Payroll
Owns the pay: approved hours, rates, the pay run.
The owner's report
Owns nothing. It reads from all three.
What moves
- Job completetoinvoice draft in accounting
- Approved hourstopayroll, once a week
- Invoice paidtojob marked paid in the field-service app
- All threetothe Monday report
One source per fact
The customer's address lives in the field-service app. Accounting reads it. Nobody types it twice, and nobody argues about which copy is right.
When it does not match
A job with hours but no completed status, or an invoice for a job that does not exist, stops in a queue with the difference shown. A person decides. The integration retries what is safe to retry, such as a system that was briefly down, and never retries a write it is not sure about.
Logged, both ways
Every read and write is logged with the source record, so when a number looks wrong you can see where it came from.
Illustrative. The systems named are categories. Which ones are the source for which facts is decided in the assessment, and written into the proposal.
AI integration services: what we connect
Every integration is named with the systems on each end and the P&L line it moves.
TMS ↔ tracking APIs ↔ Teams. Container and shipment status flowing from the tracking provider into the TMS and out to dispatch, with alerts on change. Built for a Houston trucking company on TruckMate, Vizion, and Microsoft Teams. P&L line: demurrage avoided, dispatcher hours.
FSM ↔ QuickBooks ↔ payroll. Jobs, invoices, and timesheets moving from ServiceTitan, Jobber, or Housecall Pro into QuickBooks and Gusto without re-keying. P&L line: days-to-invoice, payroll errors, bookkeeper hours.
ERP ↔ CRM. Quotes, orders, and customer records in sync so sales and operations see the same thing. P&L line: quote turnaround, order errors.
Procore ↔ finance. Job costs, change orders, and pay apps reconciled with the accounting system. P&L line: margin visibility per job, billing lag.
Practice management ↔ phones ↔ email. Intake, scheduling, and reminders wired together for professional services. P&L line: booked appointments, no-shows.
Everything ↔ the owner report. The AI data integration layer that pulls from all of the above into one place so the weekly numbers build themselves. P&L line: decisions made a week earlier.
How an integration project runs
Week one: access and mapping. We get read access to each system, confirm what its API can and cannot do, and write down the field-by-field mapping, including the mismatches: the customer that exists twice, the job that has no PO, the date that means something different on each side.
Week two: build and shadow. The integration runs alongside the manual process. Every record it would have written is logged and compared with what the person did. Differences get fixed or turned into rules.
Then: parallel, production, handoff. A short parallel run with a person reviewing before writes go live. Then production, with alerts on failures. Then the handoff: code in your repository, accounts in your name, a runbook that says what to check when something looks wrong.
Larger integrations with several systems follow the same shape over more weeks. Nothing gets switched off until the numbers match.
AI where it earns its place
Plain integration moves structured data between APIs. It is cheap, fast, and easy to trust. We use it whenever it is enough.
AI earns its place when the data is not structured: an invoice as a PDF, a rate confirmation in an email, a customer request in a web form, a port page that changes shape. There, a model extracts the fields, classifies the document, or decides which of two conflicting values to trust, and hands the result to the plain integration. Generative AI integration services, in our practice, means exactly that: AI at the messy edge, deterministic plumbing everywhere else, and a person on the exceptions.
We tell you which one you need before you pay for either. Where the job needs judgment end to end, it becomes an agent. AI agents →
When Zapier, Make, or n8n is enough, and when it isn't
For a two-step workflow between two well-supported apps, a no-code tool is the right answer, and we will say so. It stops being the right answer when the flow has ten steps and breaks weekly, when a system has no connector, when the data is sensitive, when you need an audit trail, or when nobody knows who fixes it at 6 a.m. Our comparison of Zapier, Make, n8n, and custom →
You own it
Source code in your repository. Cloud, database, and vendor accounts in your name. The runbook and credentials inventory handed over at go-live. No platform of ours in the middle, so there is nothing to migrate off. If we disappeared tomorrow, your integration would keep running and any competent engineer could pick it up.
Security controls your insurer asks about
Least-privilege access for every integration: it can read and write what its job needs and nothing else. Logging of every read and write. MFA on every account. Encryption in transit and at rest. Secrets in a vault, never in code. These are the controls that show up on cyber-insurance renewal questionnaires, and we build them in from the start rather than retrofitting them.
How we decide what to connect first
A two-day AI Readiness Assessment at your operation. We inventory your systems, find every place a person stands between two of them, and score each candidate on P&L impact and risk. You leave with a ranked roadmap you keep and a fixed-scope proposal for the first build, with acceptance tests in writing. If it misses them, the build fee is waived.
The assessment starts at $10,000 and is credited once against your first build. Book your assessment · See pricing
What it costs
- AI Readiness Assessment: from $10,000, credited once against the first build.
- Integration workstreams: fixed fee, from $10,000 each, sized by systems touched, data quality, and the number of connections. Quoted before work starts.
- Managed AI: from $1,200 per month. Monitoring, fixes when a vendor changes an API, engineering hours included.
Prices verified September 2026. Full pricing →
Why Vorsa Logic
Business goals first. Every integration has a P&L line before it has a design.
Built into what you already run. Your systems stay the systems of record. We are a systems integration company, not a platform vendor.
Yours to keep, ours to run. You own the code and accounts. We carry the pager.
Founder-built by Jonathan Klein and Chip Ray, based in Houston. Insured: cyber liability, technology E&O, and general liability.
Proof
A Houston trucking company had dispatchers checking port and steamship-line websites for 600 to 700 containers a month. We connected their TMS, their tracking provider, and Microsoft Teams so container status and last-free-days flow automatically and a person sees only the exceptions. They own the integration and keep us on a managed plan. More on trucking and logistics →
What happens after go-live
Vendors change APIs. Portals change pages. Our managed plan monitors the integration, fixes it when something upstream changes, and includes engineering hours each month for the next connection. How managed AI works →
Frequently asked questions
No. We connect what you have. If a system is the real problem we will say so, but the problem is almost always what sits between systems, not the systems themselves.
The first integration is quoted with its own schedule, scoped so it can go live in weeks, not quarters. A shadow period, where the integration runs alongside the manual process, comes before anyone stops re-keying.
We work from exports, reports, and scheduled files, and we tell you up front what that limits. In a few cases we read the vendor's web interface directly, with the understanding that it will need maintenance when the vendor changes it.
You do. Source code, data, cloud and vendor accounts, and documentation transfer to you at go-live.
The assessment starts at $10,000 and is credited once against the first build. Integration workstreams are fixed-fee, from $10,000, quoted before work starts. Managed plans run $1,200 to $2,200 a month.
Which two systems should have been talking years ago?
Book a two-day assessment. You get a ranked list of every gap worth closing, scored by P&L impact and risk, and a fixed-scope proposal to close the top one.