Unsampled device data access
Lets AI agents query unsampled mobile observability data so they can work from real device behavior instead of sampled logs alone.
bitdrift
bitdrift is a mobile observability product that gives AI agents real-time access to unsampled device data so they can investigate crashes, performance issues, and user journeys. It is aimed at teams that want richer customer-device context for debugging and incident response.
What bitdrift is
bitdrift is a mobile observability product built to help AI agents inspect what is happening on customer devices in real time. Its main claim is that agents can query unsampled data, giving them access to more complete behavioral and performance context than sampled telemetry alone.
The homepage frames the product around investigation and remediation: agents can find mobile issues before customers do, analyze crashes and performance problems, and work across large fleets without sampling limits. The site also points readers to documentation and API references, suggesting the product is intended to be used alongside agent skills, docs, and programmatic workflows.
Core capabilities
Real-time anomaly and pattern detection
Positions the system as a real-time reasoning layer that continuously looks for anomalies and patterns in customer journeys.
Agent-assisted incident handling
Supports triage, diagnosis, and fix workflows for performance issues, crashes, and alerts, with outputs intended for engineer review.
Agentic investigations
Can automatically pull additional signal from relevant devices when an agent asks a question, helping refine answers without a human in the loop.
Automatic instrumentation
Uses a control plane that can add visibility across the fleet instantly, without a release or App Store approval.
Practical use cases
Investigating mobile crashes
Use bitdrift when an AI agent needs to inspect crash behavior using real device data instead of relying on sampled telemetry or summarized dashboards.
Diagnosing performance issues
Apply it to performance troubleshooting where agents need to triage alerts, identify patterns, and prepare a diagnosis for engineer review.
Analyzing user journeys
Use it to understand how users move through an app by querying real customer-device journeys and looking for anomalies or drop-offs.
Rolling out visibility quickly
Use it when teams want broader observability coverage across a fleet without waiting for a new release or App Store approval to add visibility.
Pros and Cons
Pros
- Provides real-time access to unsampled mobile observability data.
- Focuses on customer-device behavior rather than only backend signals.
- Designed to help agents investigate crashes, performance issues, and user journeys.
- Includes automatic instrumentation language that suggests faster rollout of visibility across a fleet.
- Points to documentation and API resources for implementation and usage guidance.
Cons
- The collected source does not include pricing, so buyers cannot evaluate cost from the available page evidence.
- Integration and platform details are limited in the source, so supported devices, SDKs, and third-party connections are not clear from this material alone.
FAQ
What does bitdrift do?
The site presents bitdrift as a way to give AI agents real-time access to unsampled mobile observability data so they can investigate crashes, performance issues, and user journeys from customer devices.
How is it used in an investigation workflow?
The homepage says agents can use bitdrift to triage, diagnose, and fix performance issues, crashes, and alerts, with results ready for an engineer to review in minutes.
Where do I find setup and documentation?
The source material points to docs at docs.bitdrift.io and API documentation at docs.bitdrift.dev/api. It also references bitdrift agent skills and a setup guide in GitHub.
Is pricing published on the site?
A pricing page URL exists, but it returns a 404 and no pricing details are provided in the collected source.
Quick Facts
- Category
- Mobile observability
- Primary use
- AI-assisted investigation of app crashes, performance issues, and user journeys
- Data model
- Unsampled data from customer devices
- Source domain
- bitdrift.ai
- Docs
- Docs and API links are referenced on the homepage
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