- Passed46 of 48
- Failed2 · pricing exception
- Regression1 · competitor entry
Model A
Model B
qualitycost
Do not promote yet. Three open items.
Zime Workbench
Zime Workbench Judgment Evals Regression Cost against quality Verdict
Engineer judgment the way you engineer software. Encode, test, ship, and keep it fresh.
Zime Workbench gives engineers the APIs, SDKs, MCP connectors, and lifecycle tooling for company-specific judgment. Package it, test it, ship it to every harness, and improve it from execution evidence.
# gtm-engineering
Riley9:41 AM
Run the evals on the new discovery skill, compare it on two models, and tell me if I can promote it.
Zime WorkbenchAPP9:41 AM
The evals are done. Here is the verdict.
1,200+
GTM behaviors
trained with 50K+ human-in-the-loop hours.
5 mins
to go live
with your GTM judgment, ready to drive adoption.
80%
of reps running behaviors
in 90 days, resulting in 20% uplift in deals won.
The Problem
Why does sales AI ship without the tests you demand of every other system?
You test code before it ships. You version it, review it, and roll it back. The prompts and skills that advise your sellers get none of that.
A connector kit and a pile of prompts.
The tooling connects systems. It says nothing about what the AI should know. Generic prompts fill the space.
Skills ship untested.
No evals. No regression suite. A change to a skill, a model, or the context reaches reps unchecked.
Model changes made on cost alone.
A cheaper model goes in. No one compares quality first.
Knowledge left to rot.
The strategy changes. The skills do not. No one owns the refresh.
The Gap
An integration toolkit connects systems. It gives judgment no lifecycle.
Judgment needs what code already has: a package, a test, a release, and a maintenance schedule.
Code
Commit→Test→→Release
Prompts
Prompt editno gateCustomer conversation
The Solution
Zime Workbench: the full lifecycle for judgment
The engineering environment for encoding, testing, shipping, and maintaining company-specific judgment. Every skill moves through one lifecycle: encode, package, evaluate, promote, propagate, and refresh. What your team already built stays in place.
Build
Encode company-specific judgment, and package it for the systems you already run.
- APIs, SDKs, and MCP connectors.
- Strategy-to-behavior maps, Behavior Graph and Skill Graph packages, and reusable skills.
- Delivery into your CRM, collaboration tools, agents, and CI/CD pipelines. No new tools.
- Configure once: roles, permissions, customer context, models, and harnesses.
- Strategy, expert input, and call evidence become executable skills, not generic prompts.
Build
Connect · APIs, SDKs, MCP
Package · Behavior Graph and Skill Graph
Configure · roles, permissions, models
Encode · strategy and expert input
Prove
Test every skill and every model change before either reaches a rep.
- Judgment Evals and behavior tests, with an evaluation routine for each knowledge facet.
- Tests run in your environment and cover complex-deal conditions and exception cases.
- Regression testing across skill, model, and context changes.
- Rot detection for stale or degraded knowledge.
- Cost against quality, compared before promotion and never after.
Before promotion
Judgment Evals · 46 of 48 passed
Regression · 1 caught
Cost and quality · compared
Promotion blocked until all pass
Ship
Promote a tested release through the hub to every harness, and roll it back when you need to.
- Environment promotion and managed releases.
- Hub-and-spoke deployment to every seller harness, with automatic propagation of approved updates.
- Version management, execution traces, outcome telemetry, and rollback.
- An indexed, versioned repository with source provenance and approval controls.
- The Enterprise Sales Harness governs what happens after promotion.
The repository
Current · v3.2
Approved · v3.2
Deployed · every harness
Rollback · one click
Keep fresh
Automate the refresh, and run the right model for each GTM task.
- Scheduled and event-triggered workflows keep CRM, evidence, data, and collaboration in sync.
- Execution and outcome evidence flows back into the packages.
- Monthly or quarterly knowledge maintenance.
- Models routed by task, quality threshold, latency, sensitivity, and unit cost.
- Cost tracked by user, team, agent, environment, initiative, and customer deployment.
The refresh loop
Evidence · returns from execution
Maintenance · monthly or quarterly
Routing · by task, quality, cost
Cost · by user, team, agent
Business Case
Skip the build. Keep the control.
The value has two parts: engineering time saved against an in-house build, and lower inference cost through routing.
01
What success looks like
- Engineers ship governed skills without rebuilding the stack
- Every release evaluated before promotion
- Cost and quality compared before any model change
- What the team already built stays in place
02
What it takes from you
- A named engineering owner
- Access to CRM, call, and data sources
- Evaluation in your own environment
- Scheduled knowledge maintenance
- Forward-deployed support at the start
Who is involved
Your team
- AI or GTM Engineer
- DevOps
- Platform Owner
Zime provides
- Forward-Deployed Engineer
Before and After
From prompts as notes to judgment as an engineered artifact
Prompts as notes
Write→Paste→no tests
- SourceGeneric prompts, written once
- TestsNone
- Model changesDecided on cost
- ReleaseAn edit goes live
- VersionsWhatever sits in the file
- FreshnessRot, until someone notices
- What you builtRewritten for each new tool
Engineered judgment
Encode→Test→Ship
- SourceCustomer-specific packages, from strategy and expert input
- TestsJudgment Evals and regression suites, in your environment
- Model changesCost and quality compared before promotion
- ReleasePromotion, approval, and rollback
- VersionsAn indexed, versioned repository
- FreshnessRot detection and scheduled maintenance
- What you builtStays in place
Timeline
The path of one skill
- 1
Encode
Capture the judgment behind one skill.
- 2
Package
Configure roles, permissions, and model.
- 3
Evaluate
Evals and regression tests, in your environment.
Gate - 4
Promote
An approver releases the tested version.
- 5
Propagate
The hub sends it to every harness.
- 6
Refresh
Evidence returns. The skill stays current.
FAQ
Frequently Asked Questions
What is Zime Workbench?
Zime Workbench is the engineering environment for encoding, testing, shipping, and maintaining company-specific sales judgment. It supplies APIs, SDKs, MCP connectors, and lifecycle tooling. Every skill moves through one lifecycle: encode, package, evaluate, promote, propagate, and refresh.
How does Zime Workbench differ from an integration toolkit?
An integration toolkit connects systems. Zime Workbench gives engineers the full lifecycle for judgment: encode it, evaluate it, ship it, and keep it fresh. It supplies the packages, tests, release controls, and maintenance workflows that a connector kit leaves out.
What do Judgment Evals test?
Judgment Evals test whether encoded, company-specific judgment guides execution correctly in real deal situations. They run in the customer's own environment and cover complex-deal conditions and exception cases. Regression tests run across skill, model, and context changes.
Does it replace what a company's engineers already built?
No. What the team already built stays in place. Zime Workbench integrates with customer-built agents, internal agent stacks, CRM and collaboration tools, data sources, and CI/CD pipelines.
How does it handle model choice and cost?
Zime Workbench selects and routes models by task, quality threshold, latency, sensitivity, and unit cost. It compares cost and quality before any model change reaches promotion. It tracks cost by user, team, agent, environment, initiative, and customer deployment.
How does Zime Workbench relate to the Enterprise Sales Harness?
Zime Workbench holds what engineers do before a release: encode, package, and evaluate. The Enterprise Sales Harness governs what happens after promotion. Its hub propagates the release to every harness and observes its use and cost.