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AI Calling Agent
Outbound calling is the most heavily regulated AI channel there is. Building it well means building the compliance controls first and the conversation second.
Compliance is the architecture, not a checkbox
The rules differ by country, by state and by call purpose — and penalties are per call.
US federal and state telemarketing rules, do-not-call registries, calling-hour restrictions, consent requirements, call recording consent that is one-party in some states and two-party in others, and emerging AI-disclosure requirements — all of it has to be enforced by the system rather than trusted to a campaign setting.
We build the gating into the dialler itself: no consent record, no call. Outside permitted hours for that number's time zone, no call. On a suppression list, no call. Disclosure is scripted and verified in the transcript.
- Consent and lawful basis verified before every dial
- Do-not-call and suppression lists enforced at dial time
- Calling hours by the recipient's time zone, not yours
- Recording consent handled per jurisdiction
- AI identity disclosed at the start of the call
- Opt-out honoured immediately and permanently
- Full recording, transcript and disposition retained
Outbound programmes
Speed-to-lead callback
Call inbound web enquiries within seconds, while intent is live.
Appointment confirmation
Confirm, reschedule and reduce no-shows at scale.
Payment and renewal reminders
Polite, compliant follow-up with payment options.
Quote follow-up
Chase outstanding proposals with a real conversation.
Customer surveys
Structured feedback collection with coded responses.
Reactivation campaigns
Re-engage dormant customers with a relevant offer.
Document and information chasing
Collect what is missing to complete an application.
Voice pipeline architecture
Every stage has a latency budget. Exceed the total and the conversation stops feeling like one.
- Sub-second perceived turn latency as a design target
- Barge-in supported throughout
- Warm transfer to a human with context
- Full recording and transcript retained per your policy
Ten stages from first call to a system your team trusts
Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
Discovery
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
AI Opportunity Assessment
We score candidate use cases on data readiness, volume, error tolerance and payback, then rank them. Some come back "do not use AI for this" — you get that answer too.
Solution Architecture
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
Proof of Concept
A narrow build against your real data to prove accuracy on the cases that matter, typically 2–4 weeks. Go / no-go decision at the end.
MVP
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Production Development
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Integration
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Security Testing
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Deployment
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Monitoring & Optimization
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
Security-conscious architecture, from the first design review
Enterprise AI fails on governance more often than on models. Every system we build is designed to support enterprise security requirements and to give your risk team answers rather than assurances.
Data privacy & residency
Your data stays in the region and tenancy you nominate. We architect for no-training-on-your-data configurations and document exactly which vendor endpoints see which fields.
Role-based access control
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
Authentication & authorization
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
Encryption
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
API security
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Audit logging
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Data isolation
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
Secure prompt handling
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
PII protection
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
Human approval workflows
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Monitoring & anomaly detection
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Rate limiting & abuse control
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Secure deployment
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Questions enterprise buyers ask us first
Is AI cold calling legal?
What does AI calling cost per minute?
What happens when someone asks for a human?
Can it handle objections?
Talk to an AI solution architect
No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
- Response within one business day
- Mutual NDA signed before detailed discussion
- Written scope, one price, one delivery date
- You own all source code, models and IP at launch
Bring us the process that is costing you the most.
Book a 45-minute call with a solution architect. You leave with a use-case shortlist, a reference architecture sketch and a realistic build envelope — whether or not you build it with Ezulix.