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AI Model Integration
You have a working application. You want AI inside it. This is the engagement that puts a model behind a feature without destabilising what already works.
Integrate at the boundary, not in the codebase
Model calls scattered through application code become impossible to change, measure or cost.
We put AI behind a service boundary with its own deployment and scaling. Your application calls one internal endpoint; everything about provider choice, prompt version, caching, retry policy and cost control lives on the other side of it.
That means swapping providers, adding a fallback or changing a prompt is a deployment of the AI service, not a release of your core application. It also means one place to look when latency or cost moves.
- One internal API rather than SDK calls scattered through your code
- Provider swappable without touching application code
- Prompt versions deployable independently
- Latency, cost and error rate measured in one place
- Timeouts and circuit breakers so a provider outage degrades rather than breaks
- Feature flags for gradual rollout
Common integration work
Search upgraded to semantic
Replace keyword matching with embeddings-based retrieval inside your existing search UI.
Smart form filling
Extract and pre-populate fields from uploaded documents in your current workflow.
In-app assistant
A contextual assistant embedded in your product that knows the user's current state.
Content generation features
Drafting and summarisation surfaced inside the screens users already work in.
Classification and routing
Automatic categorisation of incoming records within your existing pipeline.
Legacy modernisation
An AI layer over a system that will not be replaced for years.
Applications we integrate with
- ASP.NET, Java, PHP, Node.js, Python and Ruby applications
- React, Angular, Vue and server-rendered front ends
- Mobile applications via API
- SaaS platforms through documented APIs and webhooks
- Legacy systems via database views or an integration layer
- Data warehouses and event streams
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
Do you need access to our codebase?
How long does AI integration take?
Will it slow our application down?
What if we want to change model providers later?
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.