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LLM Development
Building on an LLM is mostly not prompt writing. It is selection, structure, validation, routing, caching and measurement — the parts that determine whether the thing survives contact with production volume.
What production LLM work actually involves
The prompt is maybe ten percent of the effort.
The engineering sits around the model call. Structured output with schema validation so downstream code can rely on the shape. Retries with different strategies when validation fails. Caching so identical requests do not incur repeated cost. Routing so a classification task does not run on your most expensive model. Timeouts and fallback to a second provider. Rate limiting so one user cannot exhaust the quota. Logging so any output can be reconstructed.
None of it is glamorous. All of it is why the system still works at ten thousand requests a day.
- Typed, schema-validated outputs rather than free text
- Retry strategies specific to failure type
- Semantic and exact-match caching
- Task-based routing between models to control cost
- Second-provider fallback on outage
- Per-user and per-tenant rate limits
- Full request and response logging with trace IDs
LLM application patterns
Classification and routing
High-volume categorisation of tickets, emails, documents and transactions.
Extraction
Structured data pulled from unstructured text with confidence per field.
Summarisation
Calls, threads, documents and case histories condensed to a consistent format.
Question answering
Grounded responses over your knowledge, with citation.
Transformation
Rewriting, translating and reformatting content at scale within constraints.
Reasoning over data
Interpreting results, explaining anomalies and drafting analysis for review.
Models and providers we build with
Selection is benchmarked per task on your data. Ezulix claims no partner status with any provider.
- OpenAI and Azure OpenAI Service
- Anthropic Claude
- Google Gemini
- Meta Llama (self-hosted or managed)
- Mistral
- Hugging Face model hosting
- Open-weight models deployed in your own cloud tenancy
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
Which LLM should we use?
How do you control LLM costs?
What happens if our model provider has an outage?
Can we run models on our own infrastructure?
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.