A 45-minute session with a solution architect. You leave with a ranked shortlist and a rough build envelope.
Book an AI Strategy CallMost enterprise AI pilots never reach production. They stall on data access, integration, governance and cost — not on the model. Ezulix is the engineering partner that takes an AI use case from assessment through to a system your operations team actually runs on.
Ezulix has been building production business systems since 2015 — CRMs, ERPs, fintech platforms, marketplaces. That matters, because most enterprise AI projects fail on integration and data plumbing, not on the model.
Ezulix works at the layer where AI meets your existing estate: your CRM, your ERP, your database, your permissions model, your auditors.
A demo can be built in a week. A system that handles a Monday morning volume spike, respects role-based permissions, logs every decision, degrades safely when a model provider has an outage and costs a predictable amount per transaction — that is engineering work, and it is where our ten years of building business-critical platforms matter more than prompt craft.
We are model-agnostic by design. Selection is driven by your accuracy bar, data-residency constraints and unit economics.
Each links to a deeper page with architecture, integrations and scope detail.
Systems that reason over a goal, call your tools and complete multi-step work with approval gates.
Explore →AgenticPlanning, memory, delegation and supervision patterns for autonomous and multi-agent workloads.
Explore →GenAIContent, code, document and image generation wired into real business workflows.
Explore →LLMModel selection, prompt architecture, fine-tuning and evaluation harnesses.
Explore →RAGGrounded answers from your own documents, with citations and permission-aware retrieval.
Explore →ChatWeb, WhatsApp and in-product assistants that qualify, resolve and hand off cleanly.
Explore →VoiceInbound reception and outbound calling with sub-second turn latency and live CRM writes.
Explore →AutomationEnd-to-end workflow automation across sales, support, HR, finance and operations.
Explore →VisionDetection, classification, inspection and identity matching on images and video.
Explore →DocsLayout-aware extraction from invoices, KYC packs, contracts and claims.
Explore →PredictForecasting, churn, risk and fraud models with monitoring and retraining built in.
Explore →IntegrateAdd AI to the software you already run instead of replacing it.
Explore →Rules-based automation breaks the moment reality varies. An agent reasons about the goal, decides which tool to call, and knows when to stop and ask a person.
The distinction that matters commercially: a workflow tool executes the path you drew. An agent chooses the path, within boundaries you set. That makes agents suitable for work where inputs are messy — inbound leads, support tickets, exception handling, research — and unsuitable for anything that must be deterministic.
We build agents with explicit tool schemas, scoped credentials, bounded retries, spend caps and a human approval gate on every irreversible action.
Content generation is only useful in an enterprise when it is grounded, on-brand, reviewable and consistent at volume.
We build generation systems with a defined evaluation set from day one — a sample of real inputs and the outputs your team considers acceptable. Prompts and models are versioned against that set, so a change can be proven better or worse rather than argued about.
Typical work: proposal and RFP drafting, product description generation at catalogue scale, personalised outbound copy, report summarisation, and internal drafting copilots.
Retrieval-augmented generation lets a model answer from your private knowledge without retraining it — provided the retrieval layer is built properly.
The failure mode is always retrieval, not generation. Poor chunking, no metadata filtering, no re-ranking and no hybrid search produce confident answers from irrelevant passages. We build the pipeline as a search problem first and a language problem second.
Permissions are inherited from your source systems. If a user cannot open a document in SharePoint, the assistant cannot quote it to them.
A chatbot that cannot see your order system is a search box. We build assistants with read and write access to the systems that hold the answer.
Across web, WhatsApp, in-product and telephony, the same core applies: grounded knowledge, real tool access, a confidence threshold, and a clean handoff to a human with full context attached.
Voice adds a latency budget. Speech-to-text, reasoning, tool calls and text-to-speech have to complete inside the window where a caller does not feel the pause — which constrains architecture choices considerably.
The value is rarely one AI step. It is the chain: classify, decide, enrich, write to the system of record, notify the right person.
A representative chain: a lead arrives from your website; the agent scores it against your ICP; enriches company and headcount data; creates or updates the CRM record; drafts a researched first-touch email; sends a WhatsApp follow-up on no-reply; offers calendar slots; and posts a summary to the sales channel. Each step has a fallback and a log line.
We build the same shape for support triage, HR screening, invoice processing, procurement and operations exception handling.
Where the input is pixels or time series rather than text, the work moves back to classical ML and vision engineering — with the same production discipline.
Vision projects live or die on data collection and labelling strategy. Predictive projects live or die on feature engineering and honest evaluation. We plan both before promising an accuracy number, and we will tell you when your data is not yet sufficient.
Every model ships with monitoring: drift detection, performance tracking against a holdout set, and an agreed retraining cadence.
You should not have to replace a working ERP to add intelligence to it. Most of our engagements are integrations, not rebuilds.
We work through the interfaces your systems already expose — REST and GraphQL APIs, webhooks, message queues, database views, file drops — and add an AI service layer alongside. Your system of record stays the system of record.
Where no interface exists, we build one, with the same authentication, rate limiting and audit expectations as the rest of your estate.
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 →We are deliberately model-agnostic. Selection is driven by your accuracy requirements, data-residency constraints and cost per transaction — not by a vendor relationship.
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.