CY-GONServices
Four engagements.
Listed in the order we are usually needed. Most work starts with the second or the third, because most systems that reach us have already been built once.
AI systems engineering
Agents and AI systems built around a workflow that already exists in your business. Before a single model call is written we fix the task boundary, the data the system is allowed to reach, what it does when it fails, and how its output is measured. You get a system you can audit and change, not a demo you have to trust.
- Task boundary
- A written definition of what the system may do, what it may not, and what happens at the edge of that line.
- Data access
- An explicit list of the sources it can read and write, with those permissions enforced in code rather than in a prompt.
- Evaluation
- A test set and a scoring method, so a change can be shown to help before it reaches users.
- Failure behavior
- Defined fallbacks, timeouts, and cost caps. The system degrades in a way you chose instead of stopping.
Production rescue and hardening
For products that are live and breaking, and for products that are built but stuck before launch. We audit the running system, find what fails under load, and fix it in priority order: access control, data handling, rate limits and cost caps, error paths, database and query behavior, deployment and rollback. The engagement ends with a written findings report and the fixes applied.
- Access control
- Who can reach which endpoint, which record, and which model, checked against what the code actually enforces.
- Load behavior
- Where the system stops answering as traffic rises, and what it does to the database on the way there.
- Cost exposure
- What a loop, a retry storm, or a hostile user can spend before anything stops them.
- Recovery
- Whether a bad deploy can be rolled back, and whether anyone would know it needed to be.
Strategy and architecture advisory
A short, paid engagement that happens before anyone writes code. We examine the workflow you want to change, decide whether AI belongs in it at all, and specify what to build: components, data flow, cost model, risks, and a build sequence. If AI is the wrong tool for the problem, we say so and tell you what to use instead.
- Feasibility
- Whether the workflow has the data, the volume, and the tolerance for error that an AI system needs.
- Cost model
- What the system costs to run per request and per month, at the volume you expect and at ten times it.
- Build sequence
- What to build first, what can wait, and which decision has to be made before the next one.
Software engineering
Web applications, internal systems, APIs, and mobile apps, held to the same standard as the AI work: tested, documented, and handed over with the infrastructure they run on. Every AI system needs this layer around it, so we build it rather than leaving it to whoever comes next.
- Interfaces
- The screens and endpoints people actually touch, built to work on the devices and connections they have.
- Infrastructure
- Hosting, environments, backups, and deployment, set up so a second engineer can take it over.