Typing errors create downstream cleanup
A transposed digit in an invoice total or a policy number is not caught at entry. It surfaces weeks later as a reconciliation problem, a chargeback, or a customer complaint.
Stop paying skilled people to move information between screens.
We capture information from forms, documents, inboxes, and partner systems, validate it, and move it into the right business tools with human review only where judgment is needed.
Intake · 142 documents todayIllustrative
INVOICE
Supplier 99% Invoice no. 98% Total 96% Due date 94% Tax code Review
94% straight-through1 in review
- Fixed scope
- 30 days
- Read-only access
- Written roadmap
- Fig. 01 · Illustrative
- ExtractFields are read with a confidence score each
- ValidateChecks against your own records before posting
- ReviewLow-confidence fields go to a person with context
Skilled staff are being used as a keyboard between a document and a system.
Invoices, applications, orders, and forms arrive as PDFs, photos, and emails. Someone reads each one and types it into software that already knows most of the answer. We extract the fields, check them against your rules, and send only the uncertain cases to a person.
30 daysFixed scope
What structured extraction looks like on a real invoice
Fields above the confidence threshold post automatically. The rest go to a reviewer.
Illustrative example · incoming PDF attachment
INVOICE
SupplierNorthline Supply Co.
Invoice no.INV-20488
Date02 Sep 2026
PO referencePO-7731
Total$4,860.00
Due02 Oct 2026
Received 09:14
Extract + validate
Structured fields · confidence threshold 90%
supplier_idSUP-014299% Postinvoice_numberINV-2048898% Postpo_matchPO-7731 · open97% Posttotal_amount4860.0096% Postdue_date2026-10-0294% Posttax_codeAmbiguous61% Review5 of 6 fields posted automatically1 sent to reviewer
The same work, done two ways.
Every row is a step your team handles today. The right column is what the workflow does after the build, with people kept where judgment is needed.
StepHow it happens todayAfter automation
Reading the document
Open, scan, find the fields
Extracted with a confidence score
Keying values
Typed into the system
Posted directly when confident
Checking
Eyeball against a PO
Matched and tolerance-checked by rule
Duplicates
Noticed occasionally
Blocked before posting
Audit trail
Whoever remembers
Timestamp, result, reviewer
If these situations feel familiar, this is worth assessing.
Backlogs grow whenever volume increases
Throughput is fixed by how many people are typing. A busy season, a new contract, or one person on leave turns a manageable queue into a backlog.
No reliable record of what was processed
Documents are processed from a shared inbox with no log of who handled what. When a customer asks whether something was received, nobody can answer with confidence.
Illustrative scenarioA regional distributor processing supplier invoices
Two people stopped keying invoices and started resolving the ones that actually needed a decision.
“Situation
Around 1,400 supplier invoices a month arrived as PDFs and photos. Two accounts-payable staff keyed each one into the accounting system and matched it to a purchase order by hand. Month end regularly slipped.
What changed
Extraction produced structured fields with confidence scores. Purchase-order matching, duplicate checks, and tolerance rules decided what posted automatically. Anything uncertain landed in one review screen.
Result
Illustrative scenario: A regional distributor processing supplier invoices. An example of a typical engagement, not a named client case study.Most invoices posted without a human touch
Duplicate and out-of-tolerance invoices stopped before posting
Month-end close no longer waited on data entry
A working system, not an automation slide deck.
The engagement is sized around one useful operational outcome. Your team receives the implementation, operating context, and visibility needed to own it.
Input inventory
Documents, emails, forms, fields, variations, and edge cases mapped against their destination systems.
Validation framework
Confidence thresholds, business rules, duplicate controls, and review queues designed around operational risk.
Automated processing flow
A monitored pipeline that extracts, validates, routes, and records each transaction.
Diagnose. Design. Build. Measure.
Each stage has a clear decision and output, so the project remains connected to the business problem.
Catalog every input and its destination
We collect real samples of each document type, note the variations, and map each field to where it must land.
Extract, validate, route
Extraction produces structured fields with confidence scores. Business rules, duplicate checks, and thresholds decide what is posted and what is reviewed.
Track straight-through rate
The share of documents posted without human touch, review turnaround, and error rate are reported weekly.
AI document processing built around the tools you already run.
We connect to what is in place through APIs, exports, and databases. Nothing here requires a platform change, and tools not listed are usually reachable too.
QQuickBooksAccounting
XXeroAccounting
NNetSuiteERP
OOutlookShared inbox
GGmailShared inbox
SPSharePointDocuments
DGoogle DriveDocuments
FWeb formsIntake
+Your other toolsAssessed in the first call
Good fit when. Not yet when.
Good fit when
Hundreds of similar documents a month
Fields map to known destinations in a system
Rules for what is acceptable can be written down
Errors are costly to find later
Not yet when
Fewer than a few dozen documents a month
Each document needs interpretation, not extraction
The destination system has no way to accept data
The 30-day assessment says whichAnswered before you book.
Three questions we hear most often about this service. The rest is answered in the assessment.
How accurate is extraction on messy scans and photos?+
Accuracy depends on the document type. The design assumes imperfection: fields below a threshold are reviewed by a person, and the threshold is tuned on your real samples.
Where does the data go?+
Directly into the system you already use through its API or import path. We do not introduce a new place for data to live.
What about documents in unusual formats?+
They are routed to review by default. As patterns repeat, rules are extended so the straight-through rate rises over time.
Less handling. Fewer errors. Faster answers.
Most documents post to the target system with no human touch.
Low-confidence fields are reviewed in one screen, not rekeyed from scratch.
Duplicates and out-of-policy values are stopped before they enter your books.
Every document has a processing record with a timestamp and a reviewer.
30day assessment
Thirty days inside the process and the tools around it. You finish knowing what to automate, with what, in which order, and how long it will take.
Start a 30-day assessment Scope: Fixed · Access: Read-only · Output: Written findings you keepA 30‑day assessment before anything gets built.
Where your process stands
How the work really flows today: volumes, handoffs, waits, error rates, and the automations that already exist.
What you can do about it
Which steps to automate, which need an AI step, which should stay human, and which to leave alone.
Which tools will help
n8n, Zapier, Make, agents, or custom code: what fits your stack, team, and budget, and what would be hype.
What to improve in the process
Rules, ownership, and data fixes that should come before or alongside any automation.
How to work with legacy systems
If older software is in the way: how to connect to it, wrap it, or upgrade it so automation is possible.
How long it takes, and the roadmap
A sequenced build plan with durations, costs to expect, and the measures that prove it worked.
What happens in each of the four weeks.
Kickoff and scope
We walk you through our in-house analysis system, which scans your code, repositories, and databases, and agree exactly what the assessment will cover and what you will receive.
- Scope, systems, and people agreed
- What you will get: architecture, workflows, recommendations, timeline, risks
- A go or no-go decision before any access is given
NDA, access, and the scan
Once you are ready to proceed, we sign an NDA and you grant read-only access to the necessary repositories and databases. Then our system runs.
- NDA signed, read-only access granted
- Data lineage traced across code and databases
- Workflows and dependencies identified
Verify and weigh the options
Senior engineers verify what the system found in working sessions with your team, and capture what is not written down anywhere.
- Findings confirmed or corrected with your people
- Options, tools, and process fixes compared
- Legacy upgrade paths tested against your constraints
Roadmap and readout
You receive the written findings and we walk the decision makers through them.
- Overall architecture and workflow documentation
- Recommendations and risks, in priority order
- Timeline and roadmap for the work
Built and delivered by Trobus Technologies.
A Maryland-based engineering company. AICO Services is how we package our AI automation, modernization, and assessment work. Our engineers have delivered for organizations including these.
Organizations represented in the broader Trobus Technologies delivery history. Engagement scope and team role vary; reference details are shared where authorized.
Delivery recordRepresentative production engineering record. Details available where authorized.
4systems in production4 yrscontinuous operation990+automated tests1,361commitsCompany qualifications
Verified credentials maintained by Trobus Technologies, LLC
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See exactly what the 30 days deliver.
We email you the 2-page specification and a 13-page sample report written for a fictional company: findings, options compared, recommended tools, target architecture, risks, and a 40-week roadmap.
- Assessment specification
- 13-page sample report
- Architecture, risks, roadmap
What could AI document processing change for your team?
The 30-day assessment tells you whether it is a good candidate, which tools fit, and how long the build will take. We will tell you when not to automate it, and the assessment says so in writing.