Ongoing Support & Optimization

Methodology

1. 1

   Step 1

   Monitor what actually matters

   We set up monitoring that tracks AI output quality for your use case — not just whether the server is up. We alert on the things that affect your team's work.

2. 2

   Step 2

   Fix problems before users notice

   When something looks off in the monitoring data, we investigate and address it before it becomes a visible issue. We log what we did and why.

3. 3

   Step 3

   Tune as patterns change

   We review output quality on a regular cadence and retune prompts, parameters, or switch models when performance has measurably drifted from where it should be.

4. 4

   Step 4

   Quarterly review and planning

   Every quarter we sit down with your team: what's working, what's degraded, what's worth improving, and what new use cases are ready to start. We come prepared with data.

5. 5

   Step 5

   Evaluate new models when it's worth it

   We track what's changing in the AI landscape and bring a recommendation when a newer model or approach would genuinely improve your system — not just because something new came out.

1. 1

   Discovery

   2 weeks

   We interview the teams doing the actual work, map your data, and find where AI can realistically help — and where it can't.

2. 2

   Strategy

   2 weeks

   We rank use cases by impact and build complexity, cut the list to what's worth doing, and produce a concrete plan for the first thing to build.

3. 3

   Prototype

   4-6 weeks

   We build a working prototype against your real data. If the approach won't hold up, we find out here — before committing to a full build.

4. 4

   Production

   8-12 weeks

   We build the full system, connect it to your stack, test against real edge cases, and hand off with your engineers trained to operate it.

5. 5

   Optimize

   Ongoing

   We monitor output quality, tune as patterns change, and run quarterly reviews to decide what to improve next.