# Considering a Glean alternative? Start with the workflow.

> Glean offers an enterprise AI platform. Genaima is the AI autopilot for decision-dense businesses. Compare the task you need to complete, the evidence each approach provides and who will implement and maintain it.

## Establish what each approach offers

Glean's [platform overview](https://www.glean.com/platform) describes enterprise search, an assistant and agents. Its [agent governance page](https://www.glean.com/ai-agents/agent-governance) describes sharing controls, permission checks and agent boundaries. These public pages were reviewed on 8 September 2026. Glean should therefore be evaluated as more than a search-only tool.

Genaima's AI autopilot starts with an agreed business decision and the work needed to prepare it. We assess the inputs, build the selected workflow and evaluate its output with your team. AI agents, automations and web products can support that workflow. This does not establish feature parity with Glean or a ready-made replacement for its platform.

## Compare the workflow and delivery requirements

**Use the same questions with each team**

| Requirement | Ask when evaluating Glean | Ask when scoping Genaima |
| --- | --- | --- |
| Source access | Which supported connections provide the records this task requires? | Which exports or interfaces are available, and what implementation is needed? |
| Useful output | Can the configured product produce and explain the required result? | What output will be built, and how will it be evaluated? |
| Review and actions | How does this specific execution path handle authority and approval? | Which actions are in scope and which controls will be tested? |
| Data handling | Which account and deployment arrangement meets the requirements? | Is the proposed application and provider data flow feasible? |
| Operation | Who configures, supports and updates the workflow? | Which responsibilities and handover are included in the agreement? |

Ask for a demonstration using representative records, including a missing source and an action the user should not be allowed to take. A feature list does not show how a particular configuration behaves. Confirm what is available in the arrangement being proposed to your organisation.

## Check answers and their evidence

For a knowledge task, choose questions whose answers your team can verify. Include a current policy, an outdated version and a question without a supported answer. Inspect whether the system identifies the relevant source and distinguishes missing information from a confident interpretation.

For an operational task, check identifiers, dates and calculations as well as prose. A correct summary of the wrong account is still an incorrect result. Genaima's [implementation guide](https://genaima.ai/how-it-works) explains how output checks are agreed; apply comparable checks to the other options you evaluate.

## When to evaluate an existing platform first

If the main need is a shared enterprise AI platform and its available connections cover your sources, begin by testing that product's fit. An existing platform may avoid custom implementation work. Evaluate Genaima's AI autopilot against a defined operating decision: confirm the required records, output and review step.

Procurement requirements also matter. Request the evidence your organisation needs for security, processing arrangements and support. A custom build should not be treated as a substitute for a certification or service requirement it has not demonstrated. The [vendor checklist](https://genaima.ai/insights/questions-to-ask-ai-agent-vendors) helps organise those questions.

## Run a bounded evaluation before changing systems

1. **Name the task** — Write the current process, expected output and responsible reviewer.
2. **Choose the sample** — Use comparable records, including incomplete and conflicting examples.
3. **Agree the measures** — Track output quality, access behaviour, preparation effort and review effort.
4. **Review the result** — Identify what configuration, implementation or process change would still be required.

Do not assume that two systems can be connected or that data and history can be moved without work. Export formats, permissions, record mapping and ongoing ownership all need assessment. A pilot should avoid changing operational records until any action path is explicitly authorised and tested.

The [partner page](https://genaima.ai/partner) describes Genaima's scoping and handover process. Hosting constraints, including private or on-site processing, require a feasibility review; they are not blanket capabilities promised by this comparison.

## Common questions

### Is Genaima a direct Glean replacement?

Not as a general claim. Evaluate Genaima's AI autopilot against the task, requirements and evidence from the proposed implementation.

### Can we keep the platform we use today?

That is often the starting point for evaluation. We first identify what is missing and whether existing features or configuration can solve it. Coexistence and any connection still need technical assessment.

### Do agents and approvals differentiate the two by themselves?

No. Glean publicly describes agents and governance capabilities. Compare the behaviour required for your task and execution path rather than assuming those categories are exclusive to either approach.

### Where should we start?

Choose a task your team can judge and describe the required sources, output and controls. That creates a fair basis for comparing a configured platform with custom implementation.

- [Launch your autopilot](https://genaima.ai/contact) — Tell us the workflow you are evaluating and what the current setup cannot yet do.

## Related

- [Chatbot vs agent vs operating layer](https://genaima.ai/compare/chatbot-vs-ai-operating-layer)
- [Security & governance](https://genaima.ai/security)
- [How a workflow runs](https://genaima.ai/how-it-works)
- [Implementation partner](https://genaima.ai/partner)
- [FAQ](https://genaima.ai/faq)

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