Each layer hands the machine more.
Each layer raises the bar for proving it.
Scope comes in layers. At the core, AI writes code. Wider, it shapes requirements. Wider still, it touches compliance and the audit trail a regulator actually reads. Every step outward hands the machine more of the decision, so the proof has to grow with it.
Product Engineering
Product Delivery
Product Development
Development
modernization
Proof required: the output compiles, passes tests, and behaves like the system it replaced.
Proof required: every requirement traces back to something in the existing system, not to a model’s guess.
Proof required: an evidence trail you can hand a regulator without assembling it afterwards.
Holds across every layer inside it: correctness demonstrated against the specification at every step, not sampled at the end.
You can enter at any layer and expand outward. Scope and verification move together, so widening autonomy never outruns the ability to prove the result is correct.
Agents are something the platform produces, alongside requirements, tests, documentation and modernized applications. The platform itself stays deterministic: knowledge is verified and frozen before anything is generated.
Uniform governance across all four is the failure, not the fix. Clamp down on trivial work and teams route around you; leave consequential work loose and you own the outcome.
Every value traces back to the rule that produced it.
On the left, a benefits terminal that has been in production since the 1980s. On the right, the interface EltegraAI generated from it — not redrawn, reconstructed. This is the Modernize intent at the Development layer; the same trace exists at every layer above it.
Legacy · CICS session BEN015
BEN015 EMPLOYEE BENEFIT INQUIRY CICS / PROD
--------------------------------------------------
EMP ID . . . . : 104782
NAME . . . . . : SMITH, MARGARET A.
EARN NUMBER . : ACCT0204 STATUS: VERIFIED
PROV BN . . . : PRV-482 MATCH: EXACT
BENEFIT CYCLE : 04/2024
SPECIAL CODE : --- OVERRIDE: NONE
--------------------------------------------------
BENEFIT ACCRUAL BY AREA
AREA 1 (MEDICAL) MED-A 12.00 486.00
AREA 2 (DENTAL) DEN-B 4.50 162.75
AREA 3 (VISION) VIS-A 2.00 58.40
--------------------------------------------------
ACCRUED TOTAL . . 707.15
ELIGIBILITY . . . PASSED
CPS BYPASS . . . . NO
PAYABLE BENEFIT . 707.15
--------------------------------------------------
PF1=HELP PF3=EXIT PF7=PREV PF8=NEXT
Modern · generated by EltegraAI
Employee 104782 · Plan PRV-482 · April 2024
Employee 104782 · Plan PRV-482 · April 2024
The missing layer between AI code generation and enterprise reality.
What should be built? Why?
Capture the business and technical knowledge trapped in software, documentation and people, and structure it into a trusted, patent-pending Enterprise Knowledge Graph — the foundation for everything that follows.
Build it. Test it. Validate it.
Turn that knowledge into production-ready requirements, tests, documentation, modernized applications and AI agents — with full traceability.
Keep it correct and current.
Continuously keep software and knowledge synchronized as the enterprise changes, creating a living source of truth for both people and AI.
Autonomous Product Engineering ≠ autonomous code generation.
Three ways in. One governed pipeline.
Every engagement enters through one of three intents. All three run the same pipeline and the same verification.
Modernize
“We need to move a twenty-year-old .NET Framework application to .NET 10.”
Intent arrives as a legacy estate. The platform reconstructs its knowledge, designs the target architecture, then rebuilds and validates against the original.
Change
“We need to support California’s new tax rules.”
Intent arrives as a change request. The platform identifies every affected application and business rule, traces dependencies and assesses enterprise-wide impact before any code moves.
Build
“We need a new onboarding flow that respects every rule already in the system.”
Intent arrives as an idea. The platform locates the relevant knowledge, data, systems and policies, then builds against verified ground truth rather than against assumptions about what the business does.
Every artifact traces back. The knowledge graph updates continuously.
Modernize what you have. Change it when the rules do. Build what you don’t.
If the AI rewrites it, who signs off that it still behaves?
Neither of those first two is a technology failure. Both are failures to prove control. Generate first and check afterwards, and the checker inherits the same blind spot as the generator.
Ask the system what it does.
Right now the answer to "why does this behave like that" lives in two engineers, and one of them retires next year. Once the knowledge graph exists — the patent-pending Enterprise Knowledge Graph at the centre of the platform — anyone on the team asks in plain language — and every answer cites the rule and the file it came from.
Autonomy is only as safe as what the system knows.
Knowledge, not retrieval — and not only from code
Most platforms retrieve context and hope it’s representative. Better ones read the source. But code tells you what a rule does, never why it exists, and the why is what breaks when you rebuild. EltegraAI reconstructs a dependency-linked graph from source, documentation, tickets, meeting transcripts and automated SME interviews with the people who still hold the knowledge — capturing why a rule exists, not just what it does.
Ask any vendor where their knowledge comes from. If the answer is only code and tickets, the intent that was never written down is not in their model.
Verified first, not reviewed after
Most platforms generate, then check. EltegraAI verifies the specification and locks it as the baseline before generation, so correctness never depends on the generator grading its own work. You correct the specification while it’s still a specification, rather than after it has become code.
Ask any vendor when verification happens. Before or after is the whole difference.
Persistence, not project by project
One knowledge graph that compounds across every workflow, rather than re-analyzing the estate project by project. What the first modernization learns about your system is still there for the next change request, and for the application you have not started yet.
Ask any vendor what survives the engagement. If the analysis is scoped to one project, you pay for it again next time.
Committees do not buy velocity. They buy evidence — and an audit trail you can hand a regulator shortens a sales cycle faster than a demo does.
Numbers you can check against your own system.
18-year-old payroll system, ~2.5M LOC. Calendar time, not person-months.
5× faster · ~15 calendar months saved on 2.5M LOC · full docs, traceability and compliance included.
Two limitations worth stating up front. Our analysis requires access to source code: if all you have is compiled binaries, we cannot build the knowledge graph. And the SME interviews depend on people who still hold the knowledge — the sooner you start, the more of it there is to capture. If you are not sure what you have, the first conversation will tell you.
Programs that were stalled before we arrived.
“Our original timeline to migrate 3M lines was 18 months with an internal team and a coding agent. With EltegraAI feeding those agents context, we compressed it to just over three months.”
Payment processing · 3,000+ employees
SQL stored procedures → C# / .NET Core
“We had been afraid to touch our core loan servicing platform for a decade. EltegraAI surfaced every business rule and dependency in the first two weeks.”
Regional bank · 900+ employees
COBOL → Java Spring Boot
“EltegraAI gave us a complete specification before we wrote a single line of new code. The program finally has a contract to build against.”
P&C insurer · 1,500 employees
PowerBuilder → .NET Core
“So far, everyone has been impressed with the product.”
Work Truck Solutions
“Our review of the generated code has gone very well. Our team was satisfied with the quality and accuracy of the results.”
Nulogy
Any intent source. Most legacy stacks.
Not all legacy is COBOL, and not all intent arrives as code.
Read and reconstructed
Generated into
C# / .NET and Angular appear on both sides on purpose: a ten-year-old Angular front end is legacy too.
Stack not listed? Ask. We have added languages on customer request before.
Built for systems that carry regulatory weight.
Automotive
Embedded control, diagnostics and dealer systems where safety and homologation logic must survive the rebuild intact.
View industryBanking and payments
Core banking and payment engines where SOX, PCI DSS, AML and Basel III logic lives inside undocumented COBOL.
View industryHealthcare
Claims processing, benefit administration and eligibility rules that predate the compliance frameworks they now answer to.
View industryTelecom
Billing, provisioning and OSS/BSS platforms carrying decades of tariff logic and customer-specific exceptions.
View industryInsurance
Policy administration, underwriting and claims, where renewal and eligibility rules have accumulated across decades of regulatory findings and nobody has the full list.
View industryFive ways to do this. One that verifies first.
Coding agents reason from syntax. When the business logic was never written down, they guess, and produce code that compiles but does not behave like the system it replaced. Those tools translate your code. EltegraAI specifies your system, then supports Claude, Cursor and Copilot through MCP. You keep the tools your engineers already use; we make them safe to point at a system that cannot afford to break.
Built to survive your security review.
Cloud is the default, processed in EltegraAI's Azure-hosted environment. Full on-premise runs entirely inside your own infrastructure, and air-gapped deployment is supported for environments with no outbound path at all.
We do not use your code, your inputs or anything the platform generates to train, fine-tune or improve models.
The platform is built to analyze systems that process protected health information, cardholder data and personal data. Production data records stay in your environment.
Traceability from source system to generated artifact is produced as the platform runs, not assembled afterwards for an audit.
ISO/IEC 27001 certification is in progress. Full status available on request.
Security documentation, including our DPA and current subprocessor list, is available on request.
See it run on a system like yours.
Thirty minutes with our engineering team. You describe the system you are afraid to touch — the language, the age, what broke last time someone tried. We show you what the platform reconstructs from systems like it, and what a first phase on yours would look like.
The first conversation needs nothing from your repository. If it goes further, we sign an NDA before anything technical is shared.
A view of which parts of your system carry the most undocumented logic, an outline of the phases, and an honest read on whether we are the right fit.
A scoped assessment under NDA returns documentation, dependency maps, a complexity heatmap and a modernization timeline with estimates.