AI Platform
The hard part is turning what the business decided into technology that works, on systems that are already running, with the evidence to prove it. GeneWorks runs that loop: intake through memory.
Architecture
Claude, GPT, and other foundation models provide reasoning. MCP and APIs provide connectivity. But enterprise delivery needs more.
Intent to memory
Software factory takes a request to a work order. SDLC automation builds, tests, and signs off. Organizational Memory files what was learned so the next request starts here.
Business intent arrives as plain language, from Teams, Slack, Jira, or the workspace. GeneWorks holds goals, systems, controls, dependencies, and prior decisions in one context instead of a document that will be reinterpreted at every handoff.
Software factory. Organizational Memory supplies what this estate already knows.
The request becomes testable functional requirements. Obligations are written once and travel with the work.
Software factory.
A versioned technical design. Blast radius is calculated from the actual record before a line is built. The instance map and the design live together.
Software factory. Command Center and Application Explorer are the named surfaces.
The design becomes task orders for build and test, written from the same obligations. A named human completes Design Authority Review before any build starts. Status in the product: Draft, Awaiting Design Authority Review.
Software factory. This is the first human gate. It cannot be switched off.
Approved design becomes real configuration, in dependency order, through MCP. Changes return as a scoped update set on the customer's promotion path. No plugin is installed. The admin role is not required.
SDLC automation. 152 specialist agents execute against encoded skills. 500+ ServiceNow skills ship in the library.
ATF, then browser-driven functional tests against the live UI, with screenshots, video, and logs captured as the run happens. You can trace a production outcome back to the requirement, decision, code, tests, approvals, and evidence.
SDLC automation. Own test runner. Selenium and Playwright for the user journey.
A named human examines the evidence package. Nothing is handed over for promotion without that signature. The customer still owns promotion and change management.
SDLC automation. This is the second human gate. It cannot be switched off.
The outcome is written back into Organizational Memory, including the human judgments at both gates. Gate 08 feeds gate 01. The next request starts from here.
Organizational Memory.
Why not the tools you already know
The AI development wave produced two useful things. Neither was built to operate inside an enterprise estate that is already live.
They made the individual engineer faster. Completion, refactoring, whole files from a comment. Real gains, immediately felt. Still one person, one repository, one change, and nothing in it answers whether the change is safe to ship.
They let a business leader commission software directly. The intent comes from the person who holds it. They build something new, where nothing is already running, and nothing downstream depends on getting it right.
Business intent enters as a conversation and leaves as working configuration and code, across ServiceNow, Salesforce, .NET, Java and beyond. Orchestrated, governed, remembered. The evidence is not a feature. It is the product.
Modelled outcomes
These figures are modelled until a named engagement measures them. They are not reference-customer results.
Backlog cleared without adding contractors. Senior engineers move from toil to change-order work.
Applications, integrations, and workflow builds delivered with test coverage and design documentation included.
Workshop outputs become structured requirements, build-ready actions, test coverage, and a clean release package.
Who it is for
The mechanical work that consumes most of the hours, handled inside the loop. Senior people move to the work customers pay a premium for.
Workshop outputs become structured requirements, build-ready actions, test coverage, and a release package. Architects design rather than write stories about design.
Scaffolding, data model, business logic, integration patterns, and interface work generated, tested, and documented in the same workspace.
Next
A working session on one high-friction workflow. We will show how intent becomes a work order, then evidence, then memory.