On this page
Put Coworker to work on your stack.
Connect Salesforce, Slack, Jira and run your first agent in minutes.
Enterprise AI
How to Evaluate Enterprise AI on Your Own Data
How to evaluate enterprise AI on your own data: what to measure, how to build the question set, a step-by-step plan, the security questions to ask first, and how to compare the main options.
Key takeaways: To evaluate an enterprise AI platform, run your own real questions on your own connected systems, side by side with the tool you already use, and score the answers blind. Measure answer quality, cost per task, time to first useful answer, how accurate its actions are, and whether each person only sees what they are allowed to see.
How to evaluate enterprise AI, in one line: run your own questions on your own data and score the answers blind against what you use today. Demos, leaderboards and vendor benchmarks answer a different question from the one you have, which is whether this works on your company's systems, at a cost you can defend.
This guide covers what to measure, how to build the question set, a step-by-step plan, the security questions to ask first, and how to compare the options most teams weigh: Claude Enterprise, an in-house build, and Glean.
Why don't vendor demos and public benchmarks settle it?
A demo runs on data the vendor prepared. Your data has duplicate customer names, old docs that contradict newer decisions, half-filled CRM fields, and the decision that only exists in a Slack thread. Finding the right context in that mess is where products differ most.
Public model benchmarks score models on public tasks. Many enterprise AI products run the same frontier models, so the model is often not the variable that matters. Context is: which records get found, how much gets loaded into each prompt, and which permissions apply.
Vendor benchmarks, Coworker's included, are a starting point. Coworker published one: 100 tasks across seven categories, Claude using the Coworker MCP against Claude using its own native connectors, scored by blind pairwise preference against human-written reference answers. OM2's answers were preferred 84.5% of the time, and token cost was up to 89.1% lower on retrieval-heavy task categories. It ran on a standard harness and test sources, not your company, which is the reason to rerun it on your data before anyone relies on it. The full method is on the benchmarks page.
Statistically significant benchmarks comparing Coworker MCP vs. Claude Native Tooling.
What should an enterprise AI evaluation measure?
| Metric | How to measure it | Common mistake |
|---|---|---|
| Answer quality | Blind pairwise preference against a reference answer written by the person who would normally answer, with order randomized | Reviewers can tell which tool wrote which answer |
| Questions it could not answer | Count them as quality losses | Dropping them from the set |
| Action accuracy | Ask it to update a CRM record after a meeting, open a Jira ticket from a Slack decision, and draft a follow-up email, then count how many needed edits | Judging an action by whether it looks plausible |
| Cost per task | Total input and output tokens, or the vendor's billed units, per question, including retries and tool calls | Comparing seat prices instead of usage |
| Speed | Time from question to finished answer | Timing only the first words on screen |
| Time to first value | Calendar time from signed NDA to the first answer your team would act on | Counting from when the vendor says setup is done |
| Permissions | Two test users with different access, plus planted documents only one of them can open | Testing as an admin who can see everything |
| Your team's effort | Hours spent on setup and upkeep | Ignoring connector maintenance on an in-house build |
Two of these are easy to get wrong. Quality needs a reference answer written before any tool runs, or reviewers drift toward whichever answer sounds most confident. Cost needs the same questions on both sides; Coworker's LLM cost calculator lists per-token prices for Claude, GPT, Gemini and other models.
How do you build the question set?
Start from questions people actually asked last quarter: Slack threads that open with "does anyone know", support escalations, account reviews, sprint retros. Cover every team that will use the tool.
Write the reference answers before running anything. The person who would normally answer writes each one and notes its sources.
Mix the question types:
- Single-source lookups ("what tier is Acme on?")
- Cross-tool questions ("who owns the Acme account, and what did we discuss in our last meeting?")
- Commitments spread across tools ("what open commitments has our CS team made to customers in the last 30 days?")
- Stale traps, where an old doc contradicts a newer Slack decision
- Questions the test user should not be able to answer, for the permissions check
Size the set so each team has enough of its own questions to judge; Coworker's published benchmark used 100 tasks. Then freeze it. Editing questions after seeing results lets the evaluation drift toward a preferred answer.
Coworker
Put Coworker to work on your actual stack
Connect Salesforce, Slack, Jira and run your first agent in minutes.
What does a practical evaluation plan look like?
- Name the baseline: the AI tool you already pay for, or no AI at all. Comparing two new tools without one tells you which is better, not whether either is worth buying.
- Write the decision rule before you start, for example "preferred in most blind comparisons, at no higher cost per task than the baseline."
- Run the security review in parallel. Ask for each vendor's trust center up front (Coworker's is public). Let the review block the decision, not the start.
- Connect real sources with real permissions. A sandbox copy hides the problems you are trying to find.
- Hold the model constant where you can, so differences come from context and tooling.
- Run the full set through each option and log tokens and time per question.
- Score blind, then unblind and read the losses. They tell you what would frustrate users.
- Run the permission test with your two test users.
- Decide against the rule from step 2.
What security questions should you ask first?
Before any vendor connects production data, get answers to five questions:
- Data isolation: is our data isolated from other customers?
- Training: will our data be used to train or improve the vendor's models?
- SOC 2 report: can we see the most recent SOC 2 Type II report?
- DPA: is a Data Processing Agreement available before we connect production data?
- Access controls: does the AI respect our existing permission model, so each person only sees what they can already open?
A vendor that cannot answer all five on the first call is not ready to connect to your production data.
How should you compare Claude Enterprise, an in-house build and Glean?
These are the three alternatives buyers most often weigh against Coworker, and each has real strengths.
Claude Enterprise has strong reasoning, SAML SSO, SCIM, role-based access, audit logs, no training on customer data, and retention configurable down to 30 days, per Anthropic's deployment guide. If your people mostly need a strong assistant, it may be enough. Run your question set through Claude's own connectors; that is the same baseline Coworker's benchmark used. It is also not strictly either/or, since Coworker works inside Claude through the Coworker MCP.
An in-house build gives full control over data, models and the security boundary. The cost is engineering time: connectors, permission sync, retrieval tuning, and upkeep as source APIs change. Put your prototype through the same questions and permission test, and count the hours. The context layer guide covers when building makes sense and when buying does.
Glean is well regarded for permission-aware enterprise search and lists SOC 2 Type II, ISO 27001, ISO 42001 and HIPAA on its security page. Coworker's trust center lists SOC 2 Type II and GDPR; it does not list ISO 27001, ISO 42001 or HIPAA. Compare the two closely on cross-tool questions. Coworker vs Glean and Coworker vs Claude cover the differences.
How does the free Context Impact Report work?
The Context Impact Report is one way to run this evaluation on your data without building the harness yourself. It starts with a 20-minute call to agree which systems to cover. Then your side is two steps:
- Sign an NDA and tell Coworker which systems to cover.
- Name one or two testers and have them install the Coworker app.
Coworker does the rest: connects your sources, builds OM2 on your company's data, runs the full question set against your current setup and through Coworker, and scores every answer blind. Coworker covers the token costs, and you keep the numbers whichever way they land.
As with any vendor-run test, ask to see the question set and scoring method, and re-score a sample of answers yourself.
Get a free benchmark. Prefer to see the product first? Book a demo.
Frequently asked questions
How many questions should an enterprise AI evaluation include?
Enough that each team using the tool has its own questions to judge. Coworker's published benchmark used 100 tasks across seven categories. Freeze the set before you see any results.
Should both tools use the same model during an evaluation?
Where possible, yes. Many enterprise AI products run the same frontier models, so holding the model constant shows you what the context and tooling add.
What is blind pairwise scoring?
A reviewer sees two answers to the same question, without knowing which tool wrote each, and picks the better one against a reference answer. Randomize the order and count unanswered questions as losses.
How do you test permissions during an evaluation?
Use two test users with different access and plant documents, channels and records only one of them can open. The other user should get nothing from them, including titles or hints.
How do you measure enterprise AI success after rollout?
Track answer quality on a fixed question set, the share of automated actions (CRM updates, tickets, emails) that needed edits, cost per task, and the share of eligible users still active after 30 days.
What does the Context Impact Report cost?
Nothing. Coworker covers the token costs. Your side is an NDA, a list of systems, and one or two testers who install the Coworker app.
Related reading
Ready to get started?
Put Coworker to work inside your actual stack
Connect Salesforce, Slack, Jira, whatever you use, and run your first agent in minutes.