Core software cannot integrate with newer tools
The application has no API, exports are manual, and the database is off limits to anything modern. Every new tool needs a person to bridge the gap.
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AI-assisted modernization
AI-Assisted Legacy Modernization
Use AI to understand the old system before you change it.
We use AI to read legacy code, extract business rules, draft documentation, and generate tests, with senior engineers verifying every finding. Then we integrate, wrap, or replace only what blocks you.
Start with a 30-day assessment Bridge · Read + validated writeCONNECT FIRST
Modern tools Portal CRM Reporting
API bridgeRead modelsValidated writesEvents
Legacy application Core records Pricing rulesBatch jobs
First slice in production0 big-bang cutovers
The operating problem
The old system still runs the business. It just cannot talk to anything new.
The core application is stable, familiar, and full of rules nobody has written down. AI is now good at reading large codebases, explaining what they do, and drafting tests. We use it to recover the knowledge quickly, have senior engineers verify every finding, and then change only the parts that block you.
30 daysFixed-scope assessment
How it works in practice
Where AI speeds up modernization, and where it does not decide
Every AI-produced finding is verified by a senior engineer and confirmed with the people who run the system.
01Code comprehensionLarge legacy codebases summarized module by module
02Rule extractionCalculations, validations, and edge cases stated in plain English
03DocumentationArchitecture, data model, and workflows drafted from the code itself
04Test generationCharacterization tests that pin down current behavior
05Migration draftsFirst-pass translations reviewed line by line
AI is used for
Reading and explaining legacy code
Drafting documentation and rule inventories
Generating characterization tests
Proposing first-pass migrations
AI is never used for
Deciding what the business rule should be
Unreviewed changes to production
Skipping confirmation with your operators
Replacing engineering judgment
AI-assisted modernization by the numbersIllustrative figures from representative engagements
15 yrsof undocumented rulesTypical age of a core application we are asked about
Weeksto document, not quartersAI reads the codebase; engineers verify the findings
0big-bang cutoversReplacement happens in slices your team can absorb
1st slicein production before more spendThe path is proven before it is funded
Signals it is time
If these situations feel familiar, this is worth assessing.
Important behavior is undocumented
Pricing rules, validations, and exceptions exist only in code and in the memory of the person who has run the system for fifteen years.
A full rewrite would put daily operations at risk
A full replacement means re-implementing everything at once, retraining every user, and finding out on go-live what was missed. The safe path is incremental.
Is this the right first step?
Good fit when. Not yet when.
Good fit when
A core system that works but cannot connect
Business rules nobody can fully explain
A previous rewrite that stalled or was abandoned
Appetite for incremental change with proof at each step
Not yet when
The system is being retired next quarter regardless
No one on the business side can validate the rules
The only goal is a new look, not new capability
The 30-day assessment says which Illustrative scenarioA manufacturer running a 2008-era order system
The old order system kept running. The new tools just stopped needing a person to talk to it.
“Situation
Quotes, orders, and pricing rules lived in a desktop application with no API. Every new tool needed someone to rekey data both ways. A full rewrite had been quoted twice and abandoned twice, mostly because nobody could say what the system actually did.
What changed
AI analysis produced a first rule inventory and documentation in days. Engineers and the operators corrected it, generated characterization tests locked in the behavior, and a bridge layer exposed validated reads and writes. Manual quoting screens were replaced first.
Result
An illustrative example of a typical engagement, not a named client case study.Pricing rules documented and tested for the first time
Portal orders flowed into the legacy system without rekeying
Replacement sequenced over quarters, not one weekend
What we build
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.
Application knowledge map
Workflows, rules, data, and dependencies reconstructed with AI analysis and confirmed with the people who run the system.
Integration and modernization plan
A pragmatic decision for what to connect, retain, wrap, replace, or retire, and in what sequence.
First modernization slice
One useful capability delivered end to end, with generated tests protecting the behavior you rely on.
Delivery process
Diagnose. Design. Build. Measure.
Each stage has a clear decision and output, so the project remains connected to the business problem.
Recover the knowledge with AI
AI reads the code and data to draft documentation and a rule inventory. Engineers and your operators confirm or correct every item.
Keep, wrap, replace, or retire
Each capability gets an explicit decision and a sequence. Nothing is rewritten because it is old; things change because they block value.
One slice, protected by tests
The first capability is exposed or replaced in production, with generated characterization tests proving the behavior did not change.
Side by side
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
Understanding the code
Months of reading by scarce experts
AI drafts, engineers verify
Business rules
In code and memory
Documented and tested
Getting data in and out
Manual export or rekeying
Validated reads and writes through a bridge
Replacement
All at once, someday
One capability at a time
Risk
Concentrated on cutover
Spread across reversible slices
Works with your stack
AI-assisted modernization 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.
AIAI code analysisUnderstanding
TCharacterization testsSafety net
DBSQL Server and OracleLegacy data
VBAccess, VB6, FoxProDesktop apps
APIREST bridgeBridge layer
EvEvent feedBridge layer
WModern web appsReplacement
CCloud hostingReplacement
+Your other toolsAssessed in the first call
Questions about ai-assisted modernization
Answered before you book.
Three questions we hear most often about this service. The rest is answered in the assessment.
Can AI really understand our old code?+
It is good at reading large codebases and explaining them, and it is sometimes wrong. That is why every finding is verified by a senior engineer and confirmed with the people who run the system.
Will you rewrite the whole system?+
Almost never as a first step. We connect first, replace only what blocks value, and keep what works. Rewrites happen in slices with evidence at each step.
How is this different from your enterprise modernization assessments?+
Those cover large Oracle Forms, IBM i, and .NET estates. This service is for the single business-critical application a growing company depends on.
What good looks like
Less handling. Fewer errors. Faster answers.
Business rules are written down and testable, not remembered.
Modern tools read and write to the legacy system through a stable interface.
Replacement happens in slices your team can absorb.
AI speeds up the understanding. Engineers own the conclusions.
How this engagement starts30day 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 Fixed scope · read-only access · 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.
Week by week
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
Who is behind AICO Services
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
Credentials maintained by Trobus Technologies, LLC
MBE
Minority Business Enterprise
MDOT
Maryland DOT certified
SBA WOSB
Woman-Owned Small Business
WOSB
Women-Owned Small Business
E-Verify
Participating employer
2 free PDFs · Specification + sample report
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
A promise before the proposal
We will tell you when not to automate it. The assessment says so in writing.
30-day assessment
Start a 30-day assessment What could ai-assisted modernization 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.