Guided setup on the customer environment to validate RocketAiFlow in a real pilot
During early access, RocketAiFlow is configured through a guided setup on an environment agreed with the customer. Before pilot launch we define telephony, workflow, APIs, processed data, recordings, transcripts, access, monitoring, operating limits, and management responsibilities.
Early access pilot
The pilot setup defines control, data, and validation quality
- Guided setup on the customer environment or agreed technical environment
- Scope defined before pilot launch: telephony, workflow, APIs, data, and access
- AI outbound campaigns with scheduling, automatic callbacks, priority, and live monitoring
- Governed inbound AI for repetitive calls, overflow, and data collection
- Recordings, transcripts, and call data configured according to customer operating policies
- Actual performance to validate in production based on infrastructure, telephony, providers, APIs, and configuration
At this stage we are not selling a generic self-service install: we build guided pilots with clear technical scope and real workflows.
Guided setup based on how you want to validate the workflow
RocketAiFlow supports both paths without forcing one model on every team and without treating operational visibility as an afterthought.
Guided setup on the customer environment
We configure RocketAiFlow together with the customer team on an agreed environment, defining telephony, APIs, data, recordings, transcripts, access, and operating limits before pilot launch.
Operational pilot with agreed scope
The pilot is built on a concrete, measurable phone flow: outbound campaigns, automatic callbacks, minute-level scheduling, repetitive inbound, transcripts, recordings, and live monitoring.
Privacy, recordings, and data control defined before pilot launch
RocketAiFlow is designed for guided pilots on real phone workflows, where call data, transcripts, recordings, access, connected systems, and operating rules are defined before pilot launch. The customer keeps control over contact lists, legal basis, notices, required checks, and internal policies.
Lists and legal basis
Before the pilot we define which lists can be used, for what purpose, and which operating checks are needed before calls start.
Recordings and transcripts
Recordings and transcripts are enabled only if included in the pilot scope. We define what is saved, for how long, and who can access it.
Access, logs, and history
Access, logs, call history, and post-call data help the team maintain traceability and operational control.
AI agent transparency
Workflows can include opening messages to inform the person that they are speaking with an AI voice agent.
Outbound rules and local requirements
For commercial outbound campaigns, rules on opt-out lists, consent, and notices are defined within the customer operating scope.
Define reliability, recovery, and visibility before the AI voice agent goes live
Guided pilots follow different operating models, so monitoring, logs, traces, recovery, workflow continuity, provider fit, telephony fit, and dashboard availability should be defined intentionally.
Live monitoring from day one
The team should be able to control active calls, outcomes, call rhythm, load, saturation, callbacks, and campaign trends while the system is running.
Data control during the pilot
Recordings, transcripts, call data, and access are configured according to customer operating policies and the agreed pilot scope.
Pricing on request for guided pilots
The proposal is built around the workflow to validate and may include pilot setup, platform fee, and variable consumption tied to telephony, LLM, speech providers, transcripts, recordings, storage, and actual call volume.
Internal tests with real traffic
In internal tests with real phone traffic in a controlled environment, RocketAiFlow handled up to 300 simultaneous calls on an 8 GB RAM machine, with startup peaks of up to 100 call attempts per second and list loading up to 1 million contacts. These tests validate the technical base for high-volume outbound scenarios. Actual production performance depends on infrastructure, telephony, providers, configuration, recordings, transcripts, connected APIs, and campaign duration.
Performance to validate on the real use case
Actual production performance depends on infrastructure, telephony, providers, configuration, recordings, transcripts, connected APIs, and campaign duration.
Logs, traces, and post-call review
Every pilot should make it possible to analyze what happened in the call, where the workflow worked, and when agent, API, telephony, or operator handoff needs improvement.
Validate RocketAiFlow on a real phone workflow
Request an operational demo to build a guided pilot on the customer environment: AI outbound campaigns, repetitive inbound, minute-level scheduling, automatic callbacks, contact priority, call data, transcripts, recordings when enabled, and live monitoring.