Performance review
Look at representative outputs, team feedback, workflow failures, response times, and usage costs where that information is available.
04 · Improve
AI Optimization
Make your AI solutions
more useful over time.
Our AI optimization service reviews how your existing tools and automated workflows perform, then focuses improvements on the issues that matter to your team.
The service
Keep improving after the first version.
AI optimization is an ongoing review of an AI solution’s outputs, reliability, cost, and usefulness. Business processes change, source information changes, and teams discover new needs. Funk Tech School helps identify weak spots and test practical improvements, so changes are guided by real examples and feedback.
Who is this for?
This service is useful if your team already uses an AI tool or automated workflow but sees inconsistent results, slow responses, repeated corrections, or unclear value. It can also help you decide whether an existing solution is ready for wider use.
What’s included
A clear scope.
Practical deliverables.
The final scope is agreed around your workflow. Here’s what this service can cover.
Targeted improvements
Refine prompts, input handling, information sources, or workflow rules based on the issues found in the review.
Before-and-after evaluation
Compare changes against agreed examples and measures. Check for regressions as well as improvements before wider use.
Next-step recommendations
Document what changed, what still needs attention, and which improvements should be considered next.
In practice
Example: improving an AI summary tool
An existing tool may create useful summaries but miss important details in longer documents. A review can look at the input format, instructions, source handling, and team corrections. Proposed changes are then tested against the same sample set to see whether they address the problem without introducing new errors.
Illustrative workflow · The approach depends on your tools and requirements.How it works
From the first conversation
to a clear next step.
- 01
Agree on what to measure
Define a useful result and review the information available. Establish a starting point for quality, speed, reliability, or cost.
- 02
Investigate and refine
Identify recurring issues and make focused changes. Test realistic cases instead of relying on a few successful examples.
- 03
Review the results
Compare the outcomes, document the limits, and decide which changes to keep and what to improve next.
Can you review an AI tool built by someone else?
We can assess whether a review is practical based on access to the tool, its configuration, documentation, and representative examples.
Does optimization mean changing the AI model?
Not always. Better inputs, clearer instructions, improved source information, or a simpler workflow may address the problem.
How do you measure improvement?
We agree on measures relevant to the task, such as fewer corrections, more complete outputs, lower response time, or fewer workflow failures. The same examples help compare versions.
Can you guarantee a specific saving or accuracy level?
Results depend on the task, data, and system. We establish a starting point and test changes rather than promise a result before reviewing the solution.
Keep exploring
More ways we can help.
Let’s talk
Where does your current AI solution fall short?
Tell us what works, what needs correcting, and what your team wants to improve.
Get in touch