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Indigo Bots brings AI Voice Bots and Propensity Bots together to help businesses automate customer conversations and make outreach more relevant to individual customer needs.
Inbound and outbound conversations using configured workflows, conversational AI, and business knowledge.
Use customer and business signals to estimate the likelihood of a relevant action, such as responding to an offer, renewing a policy, or making a payment.
Availability of individual propensity models, real-time decisions, and automated agent selection depends on implementation maturity and the supported model configuration.
Start with industry-specific voice agents and workflows instead of designing every workflow from scratch.
Customer engagement across lending, insurance, servicing, and collections.
Admission engagement and student communication.
Patient communication and appointment workflows.
Customer communication across bookings and guest journeys.
Prioritize leads and coordinate sales activities.
Customer communication around orders, services, and repeat purchases.
Reusable starting points that can be configured around business workflows, customer segments, and operational requirements.
Establish controlled automation first, then introduce more advanced predictive capabilities.
Explicit business rules, thresholds, eligibility criteria, and fallback workflows.
Predictive models trained on suitable historical data to estimate a defined outcome.
Estimate who is likely to change behaviour because of an intervention.
Decisions that use current signals, with monitoring, guardrails, and fallback logic.
This roadmap describes the planned evolution of the intelligence layer, not a claim that every stage is currently live.
The planned catalogue documents how propensity models are defined, evaluated, and used across industries.
The customer outcome the model supports.
Rules-based, supervised, uplift, or another approach.
Customer attributes, interaction history, and operational signals.
Score bands and the business meaning of each.
The recommended agent, flow, or next step for a tier.
A rules-based approach until a suitable model is ready.
A measurable business outcome for the use case.
The catalogue connects model outputs to practical business actions instead of treating propensity scores as standalone predictions.
Each industry can have a worked scorecard showing how signals, indicative scores, tiers, and voice actions fit together.
Example: a renewal use case can prioritize customers by renewal timing and prior engagement, then route each to a reminder, a retention-oriented conversation, or a human follow-up.
Scores and thresholds should be validated with appropriate data before production use. Illustrative scorecards are design examples, not validated predictive models.
Lead qualification, appointments, renewals, payments, or support.
Conversation flow, knowledge, qualification logic, escalation.
Customer information, business rules, and validated model output.
Agent and approach chosen by your decision policy.
Run the call and record outcomes in connected systems.
Refine workflows and models with monitoring and validation.
Propensity-led decisioning with business context.
Pre-built use cases adapted to your operations.
Voice, business rules, workflow logic, and integrations.
Start with rules, add predictive capabilities as data allows.
Voice workflows linked to supported CRM and business systems.
Connect voice workflows with supported CRM, helpdesk, and commerce systems.
Connector availability and capabilities should be confirmed for your deployment.
Move beyond repetitive calling with Cloudflick Indigo Bots. Combine AI voice automation, industry-specific workflows, and a path toward propensity-led personalization to build more relevant customer interactions.