Fix the pattern. Improve the agent.

Turn recurring conversation issues into targeted instruction and flow improvements. The current suggested-fix pilot keeps every change behind validation and reviewer approval.

Talk to sales

Review suggested fix

Inspect the change, its evidence and validation before applying.

Pilot

Handle requests for a person

Service appointments · Example proposal

Not tested

The caller asked to speak with a person. The proposed instruction gives the assistant a clear next step before continuing qualification.

− Removed+ Added

Help callers arrange service appointments.

Continue qualification until you have collected every required field.

If the caller asks for a person, acknowledge the request and use the configured handoff. If no handoff is available, explain the next step before continuing.

Validation

No replay results in this example. Validation and reviewer approval are required before applying a proposed fix.

AI suggested fixes Manual approval required

Applies to all assistants in this subaccount. Suggested fixes require manual approval during this pilot.

Allow AI suggested fixes

Review proposed Agent Flow or prompt changes, reasoning, and replay results before approval.

Automatically apply validated fixes

Disabled during this pilot. A reviewer must approve every fix after validation.

Text replay does not execute live tools or webhooks. Voice behavior and blocked actions remain unvalidated.

Interactive product example with illustrative data. Changes stay on this page.

Powering businesses, agencies and franchise operators.

Household-brand logos represent franchise-level deployments.

From an observed issue to a reviewed improvement.

An agent repeatedly leaves after-hours requests unclear. Use those findings to propose a focused change and examine its behavior before applying it.

Propose the change
Group related findings and generate a targeted instruction or supported flow-field edit. Keep the proposed change connected to the issue it addresses.
Review the validation
Replay candidate responses against the rule and inspect regression cases from earlier fixes. See the trials behind the result.
Approve the improvement
A reviewer checks the edit and validation evidence before it is applied. Every suggested fix requires manual approval in the current pilot.

Build improvement into the operating process.

Make focused changes

Address a recurring weakness in the relevant instructions or flow fields instead of rewriting the whole agent.

Keep earlier lessons in view

Use retained regression cases to assess whether a new change preserves the behavior your team already improved.

Give reviewers the context

Bring the source finding, proposed edit and validation results together for a more informed decision.

Review the change and its testing boundary.

Use validation to inform a decision, with additional checks for the actions and channels outside text replay.

Confirm pilot access

Suggested fixes are a pilot capability. Our team can confirm the settings and review process available to your account; automatic application is disabled.

Keep external actions separate

Replay does not execute live tools or webhooks. Voice behavior and external results need their own testing before relying on the change.

Improve configuration

Learning here means evaluated instruction and supported flow changes. It does not retrain a foundation model or guarantee future accuracy.

Product capabilities and architecture

Assistable Evals & learning connects observation findings with proposed changes to agent instructions and supported flow fields. Candidates undergo synthetic replay and regression checks. A reviewer approves each fix before application in the current pilot.

From a quality finding to a tested proposal.

Grouped issues and focused proposals: Bring related findings together and generate a targeted improvement. Account settings control scheduled suggestion generation.
Candidate replay: Evaluate regenerated candidate responses against the observation rule. The review includes the trials behind the result, rather than the drafting model’s own rating.
Regression checks: Use retained cases from successful fixes to assess later changes. Keep earlier lessons available as the agent’s instructions evolve.
Prompt and supported flow edits: Suggest instruction changes and prompt-routing conditions on existing flow elements. Broader graph design, integrations and tool configuration remain in the builder.

Evidence attached to the change.

Keep the observed issue, candidate behavior and source configuration attached to the change.

Separate proposal from validation: The system drafts a candidate and evaluates its behavior. Confidence comes from conclusive replay trials.
Replay without live side effects: Synthetic text replay excludes live bookings, webhooks and external tool effects. Tool-dependent cases require manual review and testing.
Apply against the right version: Source checks catch newer edits or conflicting drafts before applying a fix.

Operating controls and references

Review every change before it is applied.

Suggested fixes are currently in a pilot with manual approval. Confirm availability and the review process for your account.

Inspect the proposed improvement: Review the edit, its source finding and validation results. See how the candidate differs before changing the agent.
Keep a reviewer in control: Every fix requires approval after validation in the current pilot. Automatic application is disabled, including for call-derived and shared-flow changes.
Understand the replay boundary: Replay evaluates text behavior without executing live tools or webhooks. Voice behavior, blocked actions and external outcomes need separate testing.

Observations

Know which conversations need attention.

Flow Builder

Build the questions, branches and actions your process needs.

Call tracing

Understand the call. Resolve the issue.

Evals & learning
questions.

What does self-learning mean in Assistable?

It means using observed issues to propose and evaluate configuration improvements, including instructions and supported flow fields. It does not retrain or fine-tune the underlying foundation model.

Does Assistable apply suggested fixes automatically?

No. In the current pilot, a reviewer must approve every fix after validation. Automatic application is disabled. Our team can confirm feature access and the review process available to your account.

Does replay execute real bookings or calls?

No. Replay is synthetic text interaction without live external tool effects. Cases that depend on those actions require additional testing and review.

What does the confidence score represent?

It reflects resolved trials among conclusive replay trials under the validation policy. It is evidence about those tests, not a guarantee of future accuracy. Feature access and application settings are confirmed for the account.

Make your next improvement a considered one.

Show us the recurring behavior you want to improve. We’ll walk through the available proposal, validation and approval process.