Question 1

What does Progressical actually work on?

Accepted Answer

Progressical works on the harness around your LLM feature: retrieval, prompt assembly, conversation memory, tool use, output validation, retries, fallbacks, and evals. The model can stay the same; we focus on the layer that decides what the model sees and what your product does with the result.

Question 2

When should we bring you in?

Accepted Answer

Bring us in when an AI feature already exists, users are touching it, and quality is hard to explain from dashboards alone. Common triggers are flattened usage, support complaints, hallucinated or malformed outputs, quality drift after a model swap, or a team that wants an eval set before making more changes.

Question 3

What do we get from an audit?

Accepted Answer

You get a written failure-mode taxonomy, examples from real traces, a prioritized fix list, and rough implementation estimates. The goal is to show where the harness is losing signal and which fixes are most likely to improve the metric you care about.

Question 4

What happens in a rebuild?

Accepted Answer

A rebuild starts from the audit findings. We turn representative cases into an eval set, change the highest-leverage parts of the harness, and deliver the work as a PR or equivalent artifact your team owns. We also leave an A/B plan so you can validate the change in production.

Question 5

Do you replace our model or application?

Accepted Answer

Usually no. The point is to improve the layer between the model and the application before assuming the model is the bottleneck. Sometimes a model swap is part of the answer, but it should be measured against an eval set rather than treated as the first move.

Question 6

What access do you need?

Accepted Answer

For a useful audit, we need enough visibility to follow real interactions through the harness: traces, logs, prompts, retrieved context, tool calls, outputs, and the relevant code paths. If you do not have traces yet, instrumentation becomes the first part of the engagement.

Question 7

Who is this not for?

Accepted Answer

Progressical is not a fit if the AI feature has not shipped, if there are no real user interactions to inspect, or if you want a SaaS dashboard instead of engineering work in your codebase. We are best suited to teams that already have something in production and need it to become more reliable.

Let's look atyour harness.

Your AI feature works in demos. We fix why it degrades in production.