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AI SDR Agent
Mass-personalised outbound has stopped working because everyone is doing it badly. An AI SDR agent is worth building only if the research behind each message is real — which is exactly the part we engineer.
Why most AI outbound fails
The technology made it trivial to send more. It did nothing to make the messages worth reading.
A merge field with a company name is not personalisation, and buyers now pattern-match it instantly. The agents we build are constrained to send only when they have found a specific, verifiable reason to reach out — a role opening, a funding event, a technology change, a published statement. No hook, no send.
The second failure is deliverability. Volume without domain warming, list verification and reply-rate monitoring burns your sending domain, and that damage takes months to undo. We build sending controls as a first-class part of the system.
- No verified hook, no message — enforced in the runtime
- Bounce and catch-all filtering before any send
- Domain warming schedule and per-domain volume caps
- Reply and complaint rates monitored with automatic pause
- Opt-out honoured across every channel and list
- Suppression of existing customers and open opportunities
What the SDR agent does
ICP-based list building
Source and verify accounts and contacts matching your criteria, deduplicated against CRM.
Trigger monitoring
Watch for hiring signals, funding, leadership changes and tech-stack shifts that justify outreach.
Research briefs
One specific, true, checkable fact per account, cited to its source so a human can verify.
Sequence writing
Multi-touch sequences across email and LinkedIn-style channels, drafted per account rather than templated.
Reply classification
Interested, not now, wrong person, unsubscribe — routed accordingly, with referrals followed up.
Meeting handover
Book directly into an AE calendar with the full research brief attached.
What it plugs into
- CRM and sales engagement platforms
- Email infrastructure with per-domain sending controls
- Data and enrichment providers
- Calendar and routing rules by territory or segment
- Slack or Teams for reply alerts
- Suppression lists and consent records
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 outbound compliant with GDPR and CAN-SPAM?
How many messages a day can it send?
Does it replace our SDR team?
Can it write in languages other than English?
Related AI services
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.