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AI Sales Agent
Speed to lead decides most inbound deals. An AI sales agent responds in seconds at 2am with a researched, relevant message — and books the meeting straight into the right rep's calendar.
The gap this closes
Inbound leads decay fast, and most teams cannot respond fast enough or research deeply enough at volume.
The economics are uncomfortable: a rep researching an account properly spends fifteen minutes. At forty inbound leads a day, that is a full-time role doing work that produces no relationship value. Meanwhile, leads that arrive outside business hours sit until morning.
The agent handles the mechanical part — qualification, enrichment, research, CRM hygiene, first-draft messaging — and hands the rep a prepared conversation. It does not replace the rep. It removes the reason they are not calling.
- Sub-minute response to inbound, including out of hours
- Consistent qualification against your ICP, not rep intuition
- Account research attached to every lead before the first call
- CRM records complete and deduplicated by default
- Reps spend their time in conversations, not in tabs
- Every disqualification logged with a reason
What the agent handles
Inbound qualification
Score against ICP criteria — size, sector, geography, stated need, budget signals — and route or disqualify with a logged reason.
Account research
Pull recent news, funding, headcount trend and tech stack into a short brief attached to the CRM record.
Personalised first touch
A draft referencing something specific and true about the account, queued for rep approval.
Meeting booking
Offer live calendar slots from the correct owner and write the invite with the brief attached.
Pipeline hygiene
Detect stale opportunities, missing fields and duplicate records, and propose fixes.
Deal-room prep
Compile a pre-call brief from CRM history, past emails and call notes.
Sales stack integration
The agent works inside the tools your team already uses.
- Salesforce, HubSpot, Zoho, Pipedrive, Dynamics 365
- Gmail and Microsoft 365 with per-user OAuth
- Calendly, Google Calendar and Outlook scheduling
- WhatsApp Business API for regions where it is the default channel
- Enrichment providers and firmographic data sources
- Slack or Teams for rep notifications
- Your website forms, chat and call tracking
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
Does the AI sales agent send emails without approval?
Will prospects know they are talking to AI?
How is this different from our CRM's built-in AI?
What does an AI sales agent cost to build?
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.