Name: EzyConn
Brand: EzyConn

The enterprise difference

A 20-person SaaS can deploy a website AI chatbot in a week. A 10,000-person enterprise can't, not because the tech is harder, but because the surface area is. Security review takes six weeks. Legal needs to sign off on data residency. Comms has to coordinate with 14 product lines. You're building a program, not a product.

Security & compliance baseline

Non-negotiables

SOC 2 Type II
ISO 27001
SSO (SAML/OIDC)
SCIM user provisioning
Audit log export
Encryption at rest & in transit

Often missed

Data residency (EU, UK, APAC)
Model-data separation (no training on your content)
PII masking in prompts & logs
Role-based access to transcripts
Retention policies per data class
Vendor SOC 2 for sub-processors

For regulated industries, add GDPR/HIPAA compliance requirements and security best practices.

The 10-week rollout plan

Weeks 1 to 2

Scoping & Security Review

Run a joint kickoff with IT, Security, Legal, and the sponsor business unit. Agree on the three use cases in scope, data classes allowed in prompts, and the escalation matrix. Start the vendor security review in parallel; it's the longest path.

Weeks 3 to 4

Data & Knowledge Architecture

Map every source: product docs, support articles, order systems, account APIs. Decide what's in the RAG index vs. what's live-queried. Build the RAG pipeline with tenancy-aware retrieval, so no data leaks across business units.

Weeks 5 to 6

SSO, SCIM & Governance

Wire up SSO, SCIM provisioning, and RBAC. Define who can see transcripts, who can edit prompts, who can deploy. Turn on audit logging end-to-end. If you skip this, you'll rebuild in six months.

Weeks 7 to 8

Pilot with One BU

Pick the least-risky business unit. Go live with a capped audience (5 to 10% of traffic). Measure: resolution rate, CSAT, escalation rate, hallucination incidents. Read transcripts by hand. Fix the top three problems.

Weeks 9 to 10

Ramp & Expand

Move to 100% of the pilot BU. Start scoping BU #2. Lock the metrics dashboard for exec review. Document the "known gotchas" for the next rollout.

Governance that actually scales

Enterprise AI needs a governance body, usually a monthly council with product, security, legal, and the executive sponsor. Their job is to approve new use cases, review incidents, and retire ones that aren't working. Without this, every BU reinvents the wheel.

The artifacts that matter: an AI use-case registry (what's live, where, and why), a data flow map (who touches what), and a model card for each deployment (which model, which prompt, which guardrails).

Integration depth

Help desk: Zendesk, ServiceNow, Salesforce Service Cloud: native tickets, not email forwarding.
Identity: Okta, Azure AD, Google Workspace for SSO + SCIM.
Data warehouse: Snowflake, BigQuery, Databricks: push every transcript and metric.
Workplace surfaces: Microsoft Teams, Slack, SharePoint.
Observability: Datadog, Splunk, or your SIEM for audit logs and error rates.

The real ROI math

A 2,000-agent enterprise handling 1.2M tickets/year typically sees:

Deflection (year 1)

42%

of Tier-1 volume

Cost per contact

$8.40 → $0.90

AI-resolved

Annual savings

$3.8M

net of platform cost

Payback period

~7 months

fully loaded

Use our ROI calculator to plug in your own numbers, then weigh the savings against our plans.

The enterprise mistakes we see most

Choosing a vendor before running a security review; the pick gets vetoed in week 8.
Trying to launch across five BUs at once. Pick one, ship it, repeat.
No executive sponsor: the project stalls the first time priorities compete.
Measuring volume instead of resolution. You'll celebrate noise.
Treating the model as the hard part. The hard part is the knowledge, the integrations, and the change management.

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