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GenAI Development Services
This page is about the engagement model rather than the technology: what a GenAI programme actually involves from first prototype through to the operational discipline that keeps it working six months later.
The part nobody budgets for
GenAI systems degrade quietly. Model providers deprecate versions, your data changes, and quality slips without anyone noticing until a customer complains.
Operating a GenAI system is a discipline in its own right — GenAIOps. It means scheduled evaluation runs against a golden set, alerting on quality regression, tracking cost per unit of work, testing new model versions before providers force migration, and a defined process for prompt changes.
We build this in from the start and can operate it for you, or hand it to your team with documentation and dashboards. What we will not do is deliver a system with no way to tell whether it is still working.
- Scheduled evaluation runs with regression alerting
- Model deprecation tracked and tested ahead of forced migration
- Cost per unit of work reported by team
- Prompt changes versioned and reviewed like code
- Drift detection on input distribution
- A documented rollback path
Service lines
Use-case selection
Which GenAI use case will pay back first, scored on volume, error tolerance and data readiness.
Explore →Rapid prototyping
A working prototype against your real data in two to three weeks, with an honest go/no-go at the end.
Evaluation engineering
A golden set, scoring rubric and automated harness — the artefact that makes every later decision measurable.
Production build
Hardening, validation, review queues, fallbacks, rate limiting and observability.
Systems integration
Wiring into CRM, ERP, CMS, document stores and your own product.
Explore →GenAIOps
Ongoing evaluation, cost management, model migration and quality reporting.
Ten stages from first call to a system your team trusts
Every AI engagement runs this sequence. Small projects compress stages; regulated projects expand them. Nothing gets skipped silently.
Discovery
A working session with your operations and engineering leads to map the process, the systems it touches, and where the cost actually sits.
AI Opportunity Assessment
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.
Solution Architecture
Model selection, retrieval design, tool boundaries, data flow, failure modes and hosting topology, documented before code.
Proof of Concept
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.
MVP
One workflow, end to end, in the hands of real users. Evaluation sets and quality thresholds are defined here, not retrofitted.
Production Development
Hardening: error handling, retries, fallbacks, cost controls, rate limits, observability, and a human escalation path for every automated decision.
Integration
Wiring into your CRM, ERP, HRMS, data warehouse, ticketing and messaging channels through APIs, webhooks and event queues.
Security Testing
Prompt-injection testing, access-control verification, PII handling review, dependency scanning and penetration testing before go-live.
Deployment
Staged rollout on your cloud or ours, with CI/CD, versioned prompts and models, and rollback in place from day one.
Monitoring & Optimization
Quality dashboards, drift detection, cost-per-transaction tracking and a retraining or re-prompting cadence agreed in writing.
Security-conscious architecture, from the first design review
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.
Data privacy & residency
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.
Role-based access control
Retrieval and tool permissions inherit your existing roles. A user cannot surface a document through the AI that they could not open directly.
Authentication & authorization
SSO via OIDC/SAML, short-lived tokens for agent tool calls, and per-tool scopes so an agent holds the narrowest possible privilege.
Encryption
TLS in transit, AES-256 at rest, managed keys via your cloud KMS, and encrypted vector stores for embedded content.
API security
Gateway-level authentication, signed webhooks, IP allowlisting, request validation and quota enforcement on every exposed endpoint.
Audit logging
Every prompt, retrieval, tool call, model version and human override is logged with a trace ID, so any output can be reconstructed months later.
Data isolation
Per-tenant separation at the storage, index and key level for multi-entity groups and regulated environments.
Secure prompt handling
System instructions are server-side, user content is treated as untrusted input, and we test against prompt-injection and tool-abuse patterns.
PII protection
Detection, masking or tokenisation of personal data before it reaches a model, with configurable redaction policies per field.
Human approval workflows
High-impact actions — payments, refunds, contract sends, record deletion — route to a named approver instead of executing autonomously.
Monitoring & anomaly detection
Alerting on unusual tool usage, cost spikes, refusal rates and quality regressions.
Rate limiting & abuse control
Per-user and per-tenant throttles, spend caps and circuit breakers so a runaway loop cannot become a runaway invoice.
Secure deployment
Private networking, secrets in a managed vault, immutable builds, dependency scanning, and infrastructure as code.
Questions enterprise buyers ask us first
What is GenAIOps?
Can you operate the system for us after launch?
How quickly can we see something working?
What if the prototype does not work?
Related AI services
Talk to an AI solution architect
No junior sales rep, no discovery deck. The person on the call is the person who will design the system.
- Response within one business day
- Mutual NDA signed before detailed discussion
- Written scope, one price, one delivery date
- You own all source code, models and IP at launch
Bring us the process that is costing you the most.
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.