Bring users, AI usage, conversations and limits into one workspace. Understand how your developers use Claude, manage subscriptions and investigate the working practices behind the numbers.
Built by CybergizerUnderstand how developers use Claude, where limits affect their work and which practices deserve a closer look.
Inspect captured activity, tasks discussed, prompts and available tool events. Review MCP use and task links in the proposed practices view.
Review subscription observations, reported quotas and reset times in the operator view. Keep provider limits distinct from internal user caps.
Open a developer’s usage and conversations. Review instructions and tools in context. Explore proposed prompt, MCP and task analysis when you need a deeper view.
The customer workspace shows users, usage and conversations. The operator view adds subscription observations and quota resets. The proposed analysis layer helps turn these records into questions and next actions.
Usage by engineer and model, request timing and status, conversations, available tool events and configured limits.
Bring the task, code-review evidence and engineer’s explanation. Decide whether the example reveals a useful improvement.
Explore engineer filters, task timelines, release status and quality evidence. Task, Git and deployment integrations are proposed, not released capabilities.
Explore the performance concept ↗Start with team usage, then open the requests and conversations behind it. Look at how a task was framed, what the agent did and what needs a closer review.
Were the goal, constraints and checks clear — and was the result verified?
See which engineers and models account for usage. Investigate unusual patterns alongside the work being done, and use provider billing to understand actual costs.
Request a demoLook at usage by engineer, model and period.
Review the relevant conversation and ask what the work required.
Reconcile available estimates with provider charges and plan details.
Use this proposed pilot brief as a starting point. Agree the scope, connection, data rules, duration and fixed quote before any work begins.
One engineering team. A sample of captured conversations and usage. Choose one practice to investigate, such as task instructions, context or verification.
This is a proposed evaluation process, not an automated product report or a promise of improved delivery.
For example: do sampled tasks include clearer constraints and verification evidence after introducing a shared task template? Review the outcomes with the engineers before drawing conclusions.
If the available data cannot answer your question, do not expand the pilot. No performance rankings or guaranteed ROI.
A useful pilot needs a supported connection and explicit data rules. These deployment details must be confirmed; they are agreed for each deployment.
Confirm the authentication method and its permitted use for your setup before connecting any account. Use must meet the provider’s terms.
Document prompt and code visibility, storage location, retention, deletion and authorised reviewers. Resolve client contractual requirements first.
Explain the purpose to participating engineers. Agree how they add context and challenge interpretations. Approve any client-facing material separately.
Deployment-specific access controls and client-facing permissions are not demonstrated by this prototype. If a required safeguard cannot be confirmed, client data should not be connected.
Open an engineer’s tasks, compare delivery stages and inspect acceptance evidence. Then review the AI practices behind the work. The concept makes unknown data explicit.
Explore engineering performanceAn engineer could explain a difficult task, flag missing context or describe a useful practice. Their lead — or a client reviewing approved examples — gets a clearer discussion of progress and support needed.
What happened between the first change and the release?
165 of 168 elapsed hours sit in waiting stages. Is review availability or the release schedule holding delivery back?
The visibility gap, the current product and a starting point for improving how your team works with AI.
Have a specific workflow in mind?
Request a demo ↗Vibergizer gives engineering teams visibility into their use of Claude Code through usage, request activity and conversations. You can review the work behind the numbers, discuss friction with engineers and identify practices to improve.
The customer workspace includes usage by engineer and model, request timing and status, conversation history, available tool events, configured limits and administrative history. The operator view adds subscription quota observations and reset times. The examples on this site are recreated with fictional data.
No. Request timestamps show interactions with AI, not a full working day. Token usage and conversation length do not establish productivity or billable hours. Assess progress with the actual task, the engineer’s context and verified results.
Working examples can make it easier to explain a difficult task, ask for help and share an approach that worked. Reviewing those examples together gives engineers a chance to add the context that usage totals cannot provide.
It can provide examples and activity context for a more informed discussion. Decide what the client should see and review that material with the team. This is not proof of hours worked, automatic delivery reporting or a replacement for task acceptance and code review.
No automatic prompt-quality or engineer-performance score is being offered here. Conversation visibility gives your team material to review: the task instructions, relevant context, constraints and checks. Whether the result is correct still needs verification.
Vibergizer adds team visibility around use of Claude Code. Before a pilot, we confirm your current setup, the supported connection and what activity can be captured. It does not replace Claude Code.
Conversation visibility is part of the current workspace. The collection scope, storage location, retention and access arrangements need to be confirmed for your deployment before a pilot. Client work may have additional requirements that need to be addressed.
The product brings usage, requests and conversation review into a team workspace. We can compare the relevant views with the analytics you already use and establish whether they help answer a question your team currently struggles with.
Usage by engineer and model can help you investigate consumption. Available estimates need to be checked against provider billing and pricing coverage. This is supporting context for cost decisions, not a guaranteed saving or a measure of engineering value.
Engineering performance and weekly review are interactive concepts. Task stages, acceptance and release status need verified tracker, Git and deployment connections. These views are not released integrations or automatic assessments of engineers.
Scope, duration and pricing are agreed before work begins. Start with the team, its current workflow and the question you want to answer.
Tell us about your team and what you want to understand. We’ll walk through the relevant product views and discuss whether a pilot fits your setup.