For teams shipping AI

Turn AI feedback into fixes you can measure.

Musal brings prompts, context, and evaluations into one shared workflow for product and engineering teams. Improve weak outputs, test changes, and choose models for the quality you need at a cost that makes sense.

Book a Musal demo See an improvement

30 minutes. One issue, a proposed fix, and the evidence.

After the first release

The hard part
starts after launch.

The product changes. The reference document behind the answer still describes last quarter.

Feedback arrives. The failing output gets pasted into a chat, separated from the version that produced it.

Usage grows. A better-looking answer is not enough. You need to know what improved and whether a different model is worth the cost.

Meet Improve

A bad answer is
a place to start.

Follow the feedback to a proposed change. Test it on examples that matter. Decide what deserves to become the next version.

01 / Investigate

Start with what went wrong.

Bring the output and the feedback together. Identify what needs to change and what a good answer should do.

Feedback stays connected to the output.
Musal / Improve Illustrative example
Support reply

Investigate the feedback

Investigate
The missing instruction

The response explains the problem but leaves the next step open.

What better looks like

Gives the customer a concrete next step

Investigate the issue before proposing a fix.

02 / Prove

See whether the fix helped.

Test a proposed change on relevant examples. Inspect the targeted result and check for regressions before you move on.

The evidence includes what did not improve.
Musal / Improve Illustrative example
Support reply

Review the measured change

Prove
Proposed prompt change

Previous instruction: Acknowledge the issue and offer assistance.

Proposed instruction: Acknowledge the issue, then give a concrete next step using the available support policy.

Targeted check Improved
Other checked behaviors No regression

Measured on the selected examples. Review the results before saving.

A measured change, ready for your review.

03 / Promote

Make the next version a decision.

Review the evidence, save the change as a version, and choose where to use it. The working draft is not live.

Your team stays in control of the change.
Musal / Improve Illustrative example
Support reply

Save a better version

Promote
The change helped.

The targeted check improved. Other checked behaviors held.

Current version Support reply · v3 Available to your app
Working prompt Concrete next step Not live
Next action in Musal Save as version
An improvement project does not change production.

Prompts + context

Improve the knowledge behind the answer.

A well-written prompt can still work from yesterday's information. Give your AI a shared home for the product knowledge and reference documents it uses.

  • Keep the source clear. Manage documents and their versions alongside your prompts.
  • Test their effect. Assess a context change through the outputs of linked prompts.
  • Review what changes. Inspect suggested edits, save a draft, and publish when it is ready.
One shared homePrompts and knowledge,
kept together.
Knowledge that evolvesNew information.
A version you review.

Model choice, with evidence

Your task.
Your examples.
A choice you can defend.

Start with the quality your task needs. Compare models on the same examples in Test Workbench and Evals, with recorded cost and latency alongside the outputs. See when extra spend earns a better result.

Make the tradeoff visible before you repeat it at scale.

Test Workbench & EvalsIllustrative example
Same input. Same task.

Does the answer help the customer act?

Blind comparison
Output A

“We're sorry about the issue. Our team is here to help.”

Acknowledges the issue.
Next step missing.
Output B

“Reply with your order number so support can check the delivery.”

Gives a concrete next step.
Quality on your examplesRecorded costLatency

Work with supported models from OpenAIAnthropicGoogleCompatible endpoints

Built for your team

Different roles.
A shared way to improve.

For product teams

Make every AI review a decision.

Connect customer feedback to the prompts and context behind it. Test the change, then bring engineering evidence for the quality you need and the model spend you can justify.

Explore for product teams

Illustrative example

MusalImprove

Across your AI features

A shared way to improve.

Support reply Investigate
Onboarding assistant Prove
Help center answer Promote
Feedback, examples, and the reason for the change.

For engineering teams

Know what changed. Choose what ships.

Keep prompts and context versioned. Test the behavior, compare model cost and latency, and control which version your app calls. Make the tradeoffs clear before usage grows.

Explore for engineering

Illustrative example

MusalVersions

Support reply

The next version is a choice.

v3 Selected by your app Current prompt version
A concrete next step Working draft · Not live
Review the evidence before saving a version.

For revenue operations

Keep every play grounded in the same knowledge.

Give buyer profiles, value propositions, and product knowledge a shared home. Turn repeated corrections into examples your builders can test, so the next review builds on the last.

Explore for RevOps

Illustrative example

MusalContext

Shared buyer context

Keep the source in sync.

Buyer profile v2
Value proposition v3

Linked prompts

Account research Outreach draft

For gtm engineers

Keep the craft. Make the evidence reusable.

Compare prompt and model changes on the same examples. Check the output quality and what each run costs before choosing a model for the next batch. Keep buyer context close.

Explore for GTM engineers

Illustrative example

MusalCompare

Same buyer. Same task.

Compare the actual output.

A

“We help companies work more efficiently.”

B

“Help your support team turn repeat questions into clear next steps.”

Review quality, recorded cost, and latency.

For founders

Put what you know into the next AI version.

Turn your product expertise into shared instructions, reference documents, and examples to test. Choose the quality your customers need with a clearer view of running cost as the product grows.

Explore for founders

Illustrative example

MusalProduct knowledge

What your team knows

Give your expertise a home.

Product knowledge

“If a policy is unclear, ask before making a promise.”

Shared context Testable change
A source your team can version and review.

From a single feature onward

Fits the product
you already run.

Start with a prompt, its reference documents, and a few useful examples. Connect through the API. Keep improving without losing track of the version behind an output.

Your productOne AI featureCustomer-facing output
API · selected version
MusalPrompt + contextVersioned and reviewable
Feedback informs the next improvement

Versions you can revisitInspect earlier changes and restore a previous version.

Tests you can reuseKeep relevant examples close to the prompt they test.

Quality and cost in viewReview recorded executions, feedback, cost, and latency as usage grows.

Before we talk

A few useful
answers.

How does Musal fit our existing app?

Connect your app through the API and start with one AI feature. Keep its prompts, reference documents, and versions in Musal, with control over which version your app uses.

Who decides what changes?

Your team does. Improve proposes changes and shows the test results. You review the evidence, save a new version, and decide where to use it.

How do we know a change helped?

Test it against relevant examples and the criteria your team cares about. Review the targeted improvement alongside other checked behaviors. A better result on a test is evidence for that test, not a guarantee for every future output.

Can we compare different models?

Yes. Use Test Workbench and Evals to compare outputs from supported providers, including OpenAI, Anthropic, and Google. Start with the quality your task needs, then weigh recorded cost and latency. The comparison helps you decide when extra spend is justified by a better result.

What will we see in a demo?

A 30-minute walkthrough from a reported AI issue to a proposed fix and its results. We will also cover context, model comparisons, integration, and pricing for your team. You do not need to bring proprietary prompts.

A 30-minute Musal demo

See a better version take shape.

Explore one AI feature: the fix, the test evidence, and the model tradeoffs behind a version worth running.

Book a Musal demo