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Mowa

AI GOVERNANCE

Change control for your AI's behavior.

Mowa is the system of record for how your AI behaves: every prompt change proposed, run against datasets, scored, and reviewed before release. No vibe checks. No repo gate.

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Runs
1,248
Cases
8.4K
Avg score
91.4%
Regressions
12

Support triage eval

run_1248

prompt v13 · zendesk-escalation-set · 412 cases

91.4%

Case scores

case_0378case_0394

Scorers

Policy compliance94%
Answer helpfulness89%
JSON validity100%
case_03910.96+0.02
case_03920.91-0.01
case_03930.88+0.04
case_03940.970.00
Baseline 91.4%Candidate 90.8%Δ -0.6%

HOW IT WORKS

One loop from draft to release.

Every step leaves a record. Every record is comparable. That is the whole system.

  1. 01

    Write

    Draft prompts with variables, versions, and team review.

  2. 02

    Run

    Execute versions against datasets and models.

  3. 03

    Score

    Attach LLM judges, code checks, or human review.

  4. 04

    Compare

    Save runs as experiments. Read the score deltas.

  5. 05

    Ship

    Promote changes with regression context attached.

PLATFORM

Eight objects. One quality system.

The same object model you meet inside the workspace, end to end.

01PromptsDraft, version, review, and scope without a repo gate.02DatasetsInputs, expected outputs, and metadata for repeatable tests.03ScorersLLM judges, code validators, and review workflows.04PlaygroundPrompt, dataset, and model combinations side by side.05ExperimentsImmutable snapshots of what changed and why it matters.06RegressionCandidates compared against known-good baselines.07TeamPMs, experts, operators, and engineers in one workspace.08ProvidersBYOK model access, scoped per workspace.

PLAYGROUND

Four candidates. One screen.

Run prompt versions, models, and parameters side by side. Each instance carries its own config; the scores line up for reading.

  • Per-instance prompt version and model
  • Shared dataset context across instances
  • Scores rendered where the outputs sit
v13 · gpt-4.10.94

priority: P1

summary: "Refund blocked by KYC hold"

next_action: escalate_to_payments

v13 · claude-40.89

priority: P2

summary: "Refund delayed, KYC pending"

next_action: request_documents

REGRESSION

The delta decides.

Every candidate is read against a known-good baseline. A drop is a flag, not a feeling.

  • Baseline pinned per dataset and scorer
  • Per-case deltas, not just averages
  • Release notes written by the numbers
ScorerBaseCandΔ
Policy compliance0.940.95+0.01
Answer helpfulness0.890.84-0.05
JSON validity1.001.000.00
Tone adherence0.910.92+0.01

1 scorer regressed. Hold release.

SCORERS

Three kinds of judgment.

Quality is not one number. Chain model judges, deterministic checks, and human review into a single signal.

  • LLM judges for rubric-based grading
  • Code checks for structure and policy
  • Human review queues for the edge cases

LLM judge

0.89

Rubric: support tone v4

Code check

pass

JSON schema + policy regex

Human review

3 open

12 cases queued to experts

Mowa for

  • Shape AI behavior without opening a repository. Propose a prompt change, read the scores, and sign off on the release with the delta in front of you.

    Learn more

  • Wire providers, import prompts straight from your repos, and let the eval loop gate merges instead of gut feel. Our agent reads your code, not just your prompt files, so prompts hiding in template literals and configs get found.

    Learn more

  • Label edge cases, review model output against real policy, and turn judgment into a scorer the whole team can run.

    Learn more

  • Every prompt change is proposed, reviewed, and written to an audit trail. When the regulator asks why the model said that, the answer is a link.

    Learn more

  • Monitor production-facing prompt changes and catch quality drops as score deltas, not support tickets.

    Learn more

  • Curate datasets, compare models side by side, and keep a durable record of which combination won and why.

    Learn more

DEVELOPERS

Built for machines too.

Everything the workspace does is reachable by code and by agents. Same objects, same records, no UI required.

Read the docs
REST APIWorkspace keys, full CRUD over every object/developers/apiMCPPaste mowa.dev/mcp into your agent/developers/mcpllms.txtThe whole platform, machine readable/llms.txt

SIGNALS

The workspace keeps score.

1,248

Eval runs recorded

Each run stored as an immutable experiment with its dataset and scorer versions.

12

Regressions caught

Score drops flagged against known-good baselines before release.

91.4%

Average quality score

Tracked across prompts, models, and datasets in one ledger.

USE CASES

Where mistakes are expensive.

Prove a prompt behaves for a specific team, dataset, policy, and release.

Insurance quality review

Policy scorer

Claim-summary and agent-assist prompts asserted against regulated edge cases before rollout.

Support triage

Avg 91.4%

Escalation prompts compared across real ticket datasets. Missed priorities surface as score drops.

Fintech policy flows

12 flagged

Refund, KYC, and compliance responses validated against strict expected outputs.

Healthcare-adjacent intake

Review ready

Intake and routing prompts reviewed for safer wording and structured output.

ACCESS

Early access is open.

Mowa 2.0 is rolling out to teams in order of request. Start free. Scale when you are ready.

Request accessSee pricing
Does Mowa replace GitHub?

No. Prompts live in Mowa first so the whole team can work on them. GitHub sync remains available for engineering workflows.

Which models can we use?

Bring your own keys. Each workspace configures the providers it trusts and every run records which model produced which output.

Who is Mowa for?

Teams where PMs, engineers, domain experts, and operators all influence AI product behavior and need one shared quality record.

What does a quality signal look like?

A scored, versioned comparison: this prompt, this dataset, this scorer, this delta against baseline. Attached to every release.

SHIP WITH A NUMBER

Give every change a quality signal.

Request access to the Mowa 2.0 workspace and move from idea to scored release without losing context.

Request accessOpen workspace