A 45-minute session with a solution architect. You leave with a ranked shortlist and a rough build envelope.
Book an AI Strategy CallA model that scores well in a notebook and a model that holds its performance for a year in production are different engineering achievements. We are hired for the second.
Three causes, all preventable, all commonly ignored.
Training-serving skew: features computed one way in training and another way in production. We prevent it by using one feature pipeline for both, which sounds obvious and is skipped constantly.
Drift: the world changes and yesterday's patterns stop holding. We monitor input distributions and live performance against outcomes as they arrive, and alert before anyone in the business notices something is off.
Silent failure: a model returning plausible but wrong predictions produces no error and no alert. Monitoring against realised outcomes is the only defence.
Churn, risk, eligibility, categorisation and routing.
Pricing, valuation, duration and quantity prediction.
Demand, capacity and revenue over a horizon.
Fraud, faults and outliers in transactions and telemetry.
Product, content and next-best-action ranking.
Customer and behavioural segmentation for targeting.
Eight stages. Most failed ML projects skipped one of the last three.
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.