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AI Technology Stack
We are deliberately model-agnostic and cloud-flexible. What follows is what we build with and, more usefully, how we decide between the options.
How we choose, and why it is not a vendor decision
Every component below is replaceable. That is the point — the architecture is layered so a model or database swap is a configuration change, not a rewrite.
Model selection is benchmarked against a sample of your real tasks, not against public leaderboards. A model that leads a general reasoning benchmark can lose badly on your specific extraction task, and the difference in cost per thousand requests at production volume is often larger than the difference in quality.
The same applies to vector databases. For most workloads under a few million chunks, PostgreSQL with pgvector is the correct answer and adds no new operational surface. Dedicated vector databases earn their place at scale, or where you need specific hybrid-search and filtering behaviour.
- Benchmarked against your data before selection
- Model provider abstracted behind a service layer
- Two providers configured for failover on critical paths
- Open-weight models where residency requires it
- Cost per transaction modelled at projected volume
- No component chosen because of a partner incentive
Reference architecture
The shape most of our production systems take, regardless of use case.
The AI technology stack we build on
We are deliberately model-agnostic. Selection is driven by your accuracy requirements, data-residency constraints and cost per transaction — not by a vendor relationship.
Large Language Models
AI Frameworks
Vector & Search
Backend
Frontend
Data
Cloud
Infrastructure & MLOps
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
Which LLM should we use?
Which vector database do you recommend?
Do you build in .NET or Python?
Is Ezulix a certified partner of these providers?
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.