A 45-minute session with a solution architect. You leave with a ranked shortlist and a rough build envelope.
Book an AI Strategy CallGenerative AI is easy to demo and hard to operationalise. The engineering problem is not producing output — it is producing output that is consistently accurate, on-brand and reviewable at volume.
Unmeasurable quality, unbounded cost, and no review path.
Quality first: without an evaluation set — real inputs paired with outputs your team considers acceptable — every prompt change is an argument rather than a measurement. We build the evaluation harness before the feature.
Cost second: a generation feature that works beautifully at demo volume can become a five-figure monthly line item at production volume. We model cost per generation at projected volume and route simpler tasks to cheaper models.
Review third: anything customer-facing needs a review path, and that path needs to be fast enough that people actually use it rather than routing around it.
Assembled from your past submissions, pricing rules and case library, with every claim traceable.
Descriptions, specifications and metadata generated consistently across thousands of SKUs.
Recurring reports drafted from your data with commentary, ready for review.
Customer messaging that reflects actual account state rather than merge fields.
Policy, contract and specification first drafts grounded in your templates.
Scaffolding, tests and migration assistance inside your own repositories.
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.