A 45-minute session with a solution architect. You leave with a ranked shortlist and a rough build envelope.
Book an AI Strategy CallA departmental pilot and an enterprise rollout are different engineering problems. The second one has procurement, identity, audit, cost allocation and twelve stakeholders who each need a different guarantee.
The pilot proved the model works. Production asks a different set of questions, and nobody owns the answers.
The blockers are consistent across the organisations we work with: no clear data owner, no identity integration, no cost attribution model, no agreed quality threshold, and no one accountable for what the system does when it is wrong.
We treat these as design inputs rather than post-launch surprises. The architecture document names the data owner, the approval authority and the escalation path for every automated decision before development begins.
Usually sequenced — one department proves the platform, then it is extended.
Permission-aware RAG across your document estate, with citations and per-department tuning.
Explore →AgentsA common runtime, tool registry and approval framework so each team is not rebuilding the plumbing.
Explore →SupportMulti-channel deflection with containment reporting and clean escalation into your existing helpdesk.
Explore →DocumentsHigh-volume extraction with confidence scoring and review queues feeding your ERP.
Explore →CopilotsSales, HR, finance and legal assistants sharing one governed AI service layer.
Explore →ModernisationIntelligence added to systems that cannot be replaced, through APIs and data views.
Explore →The economics only work when retrieval, evaluation, logging and governance are built once and reused.
The model is the easy part. The difference between sectors is the data, the integrations and the regulatory line you cannot cross.
Fraud signals, KYC document extraction, collections agents and support copilots inside regulated data boundaries.
Explore →Clinical documentation, intake triage and prior-authorisation paperwork with PHI handled under strict access control.
Explore →Lead qualification agents, listing generation and document review across portals, CRM and WhatsApp.
Explore →Semantic product search, support deflection, catalogue enrichment and demand forecasting.
Explore →Shipment exception agents, document processing at the dock, and ETA prediction.
Explore →Contract review, clause extraction, and precedent search over privileged repositories.
Explore →Claims intake, FNOL triage, fraud scoring and policy-document question answering.
Explore →Adaptive tutoring, assessment generation and student-support assistants with age-appropriate guardrails.
Explore →Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
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.
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
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.
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
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.
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.
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Representative scopes drawn from the categories Ezulix works in. Client names and outcome figures are withheld until verified.
Tier-1 ticket deflection using RAG over product documentation and past resolved tickets, with confidence-gated handoff to human agents and full conversation audit.
An agent that qualifies inbound leads against ICP criteria, enriches company data, writes a researched first-touch email and books directly into rep calendars.
Permission-aware assistant over SharePoint, Confluence and a contract repository, with hybrid retrieval, re-ranking and mandatory source citation on every answer.
[CASE STUDY METRIC] and [PROJECT RESULT] with signed-off figures, and add [CLIENT NAME] only where you hold written permission.No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
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.