A working prototype is the beginning. Systems used in daily operations need access controls, clear limits, testing, monitoring and a plan for failures and changing requirements.
We prepare AI solutions for operational use and, where agreed, support them afterwards.
What we deliver
- Deployment and data flow review
- Authentication and access controls
- An evaluation dataset and repeatable tests
- Prompt-injection and security review
- Logging, monitoring and cost alerts
- Fallbacks and failure handling
- Human review mechanisms
- Documentation and team handover
- Ongoing maintenance and improvement, by separate agreement
Technology & implementation
Evaluation comes first: a set of representative inputs with expected results, covering normal cases, edge cases and cases that should be refused or escalated. It runs before launch and after every change to prompts, models or data, so a regression is caught before users notice it.
Operational checks follow: authentication and permissions, what data leaves your environment and under which provider terms, rate limits, timeouts and fallbacks when a provider is unavailable, and cost alerts. Wherever the system reads external content, prompt-injection risks are reviewed.
After launch, monitoring tracks volume, cost, error rates and how often people correct the output. Models and provider terms change; maintenance includes re-running the evaluation when they do.
Example use
An illustrative scenario, not a description of a completed project.
Before rolling out an internal AI assistant to a larger team, we test it against representative questions, verify access permissions, establish escalation rules and document how its performance will be monitored.