Beye vs Copilot: specialized Generative BI, not generic AI
Copilot is a generic AI assistant. It is not built for data intensive workflows, does not understand your business rules and can feel vague or wrong when you ask real operational questions. Beye is a specialized Generative Business Intelligence layer above the systems you already use. We model your data to how the business actually works and, using a hands on AI playbook, give operations, supply chain and finance teams a conversational workspace with trusted answers in hours instead of weeks. Most teams are live on their first use case in about a week.
Copilot alternative
2-3 words tagline
A simple way to make AI stick in data driven workflows
We start with one high impact business case, connect the systems you already use and get your team hands on in week one. The rest of the proof of value focuses on change management, training and steadily baking your business rules and vocabulary into the model so the workspace feels like it was built for your business.
Features | Beye | Copilot |
|---|---|---|
| We sit with you to learn your current process and the business case. We define success, set up the workspace, and teach the system your terms and rules. On first login you see your goals, your data connected, starter dashboards, and a short list of initial questions and insights to review. Not a blank screen. From there it is self serve. | ||
| 1. Use case discovery + context aware workspace day 1 We sit with you to learn how work is done today and agree on one high impact use case. We define success, set up the workspace with your terms and rules and use our playbook to turn that into a clear decision workflow. On first login your team sees goals, connected data and ready questions and views instead of a blank screen. From there it is self serve. | ||
| We bring data from ERP, CRM, WMS, and other sources into a database ready to use so you can start testing in week two and ask questions across all your systems. | ||
| 2. Connecting siloed systems We bring data from WMS, TMS, ERP and other sources into one model so Beye can answer questions across systems. Your team stops pulling reports from each system by hand and spends time on decisions instead of data wrangling. | ||
| Adoption happens day one after workspace handoff. In the first hour, teams ask three or more questions that matter to them. We run weekly touchpoints to train and support how people use data. | ||
| 3. Change management and training Adoption starts day one after workspace handoff. In the first hour teams ask questions that matter to them while we sit side by side, show good questions to ask and share prompting and best practices. We run regular touchpoints during the proof of value to support how people use data and make sure the new workflow sticks. | ||
| The first month is an iterative partnership. We learn your objectives, how your team asks questions, and which KPI definitions matter. You add context and definitions, the system updates in near real time, and the knowledge base matches how your business operates. | ||
| 4. Refining business rules and KPI The first month is an iterative partnership. We learn your objectives, how your team asks questions and which KPI definitions matter. You add context and business rules, the system updates in near real time and the knowledge base stays aligned with how your operation actually runs. | ||
| Once dialed in, Beye looks at your connected data, your goals and OKRs, and your team’s question patterns. It suggests the next questions and shares what you need to see without you having to prompt it. One use case dialed in becomes the foundation for more. | ||
| 5. scaling from the first use case Once the first use case is dialed in, Beye looks at your connected data, goals and question patterns and suggests the next questions and views. We use the same playbook to roll out additional decision workflows so each new use case goes faster and builds on what the team already trusts. | ||
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