The engineering system
behind your next chapter.

A useful application needs a clear purpose, a coherent data model, a usable interface and a controlled route to production. Generating code is one part of that work. Forge brings planning, implementation, verification and the next improvement into a single product workspace.

A founder may need to turn a service idea into the first usable customer portal. An established company may already have a valuable application whose integrations, interface or deployment are difficult to change. Both need engineering work that follows their business intent.

A new customer request, an aging dependency or a changed business rule creates another engineering decision. When context is scattered across chats and handovers, each change begins with rediscovery. The code survives, but the reason behind it often does not.

The Architect clarifies the goal, designs the approach and defines acceptance. The Developer implements the scoped task, tests the changes and returns evidence. The Architect reviews the result, including user experience, rather than simply repeating the Developer’s completion message.

Forge separates persistent product state from the replaceable agents performing the work. The specification explains what should exist; the roadmap describes the initiatives; evidence records what was actually checked. These are related records, not a summary that one model keeps in its private chat.

A task moves through implementation, validation and independent review. A passed check is tied to a particular version and environment. If an important check fails, the work returns for a bounded correction; if a business decision is missing, the Architect asks the owner.

New products begin with users, jobs, rules and exclusions. Existing products begin with an authorized import and a baseline: routes, records, integrations and important behavior. Original source, editable workspace, private preview and production are distinct environments.

A rule can be simple: one office must not edit another office’s reservations. Its consequences are not. It affects database queries, API authorization, bulk actions, administrative tools and tests. Describing it once at Product level gives every later Project the same point of reference.

A bulk reservation update may look correct while changing records belonging to a different office. Rehearsal connects the approved rule to a controlled scenario, executes it against the candidate release and inspects the resulting records and permissions.

In Product → AI Native Processes, choose the workflow you want to change. The guided questions identify the trigger, source data, permitted actions, human decisions and exceptions. The result is a specification change you can inspect before development begins.

An embedded AI process calls a provider to interpret text, classify a document or propose an action. The application remains responsible for authentication, deterministic business rules, records and approvals. The client selects and pays its runtime AI provider directly.

Consider a service enquiry with attached documents and an incomplete deadline. The AI module extracts the request and highlights missing information. It does not guess a binding price or invent available capacity. Approved application functions perform the calculation and return a draft.

Keep the application on its original server, move it to a new supported environment or add Yepsy hosting. Each path needs a compatible runtime, a release plan and explicit permission. Private previews provide a review surface before the production cutover.

Optional monitoring observes the agreed endpoints, API contracts and integration dependencies. A useful finding identifies the affected service, what changed, when it was observed and what evidence supports it. It can work with authorized access even when the application is hosted elsewhere.

An Atlas profile brings together observed metadata, desktop and mobile captures, technical checks and prioritized recommendations. Its score is a technical summary of the measured surface, not a customer review or proof of the company’s reputation. Capture date and coverage belong next to the result.

Choose the Architect and Developer independently from supported account connections. Model capabilities, provider permissions and available resources determine the valid choices. Each execution records the selected connection and model, so you can see what performed the work.

Knowledge contains the specification and approved rules. Projects hold initiatives and their visible task plans. AI Native Processes guides a particular kind of improvement. Integrations describes connected business operations, while Evidence and Releases connect decisions to exact versions.

Forge is the engineering workspace. Atlas is the public discovery and website-analysis entry. Yepsy Coder is an execution option inside the engineering process, not a separate obligation for every client. Account, organization and service management belong to the common Yepsy foundation.

You control intentional business-rule changes, consequential actions and deployment. Your application uses its own credentials and its own operating policies. The delivered AI module continues to run independently of your development subscription, using the hosting and AI services you configure.

A first product, a safer modernization or an AI capability can start from the same question: what should the software help a person accomplish? Describe that job, choose the boundaries and define what a good result looks like.