A prompt carries too much data
Identifiers, logs and business documents can enter model context or debugging traces. Before each call, define permitted data, masking and retention.
Generative AI platforms
Design measurable, governed LLM workflows without confusing a prototype with production operations.
A model that answers a few examples cannot guarantee confidentiality, cost or safe behavior when it is wrong. Architecture gives the LLM a defined place in a process where permissions and decisions remain explicit.
Expertise
YOUR CONTEXT
When this expertise matters
A proof of concept without fallback
Timeouts, depleted quota, malformed outputs and new models can interrupt a workflow. A sensitive recommendation must not become an automatic action just because a demo works under ideal conditions.
Tokens without a measure of value
Large contexts, repeated calls and long responses inflate spending. Track cost by use case against result quality, latency and time actually saved.
THREE PILLARS
Capabilities brought to bear
LLM architecture and Azure AI Foundry
Separate application, orchestration, data and model access. Azure AI Foundry and Azure OpenAI support deployment and evaluation; choose the model, region and capacity for a confirmed need. Bounded API timeouts and validated inputs and outputs make integration more predictable.
Security and human approval
Minimize data, use suitable identities and protect secrets. The model proposes; deterministic rules check the action and an authorized person can approve, reject or request a revision. Record who made the decision and why.
Cost, evaluation and operations
Build representative examples and track quality, format errors, refusals, latency and cost by version. Correlate events, prompts, models and decisions without unnecessarily retaining sensitive data. Plan budgets and a manual path when quota or provider is unavailable.
TECHNICAL DECISIONS
Trade-offs that shape the solution
Synchronous or event-driven?
A queue decouples ingestion and inference where latency allows and supports retries. For immediate responses, bound timeouts and retries; either way, make processing idempotent before writes.
How much autonomy should a model have?
A valid response schema checks form, not truth. Changes to security policy or business processes need deterministic checks, human approval and rollback.
WAFGuard AI: research and experimentation
WAFGuard AI R&D explores an Azure workflow combining WAF events, LLM analysis via Azure AI Foundry and Azure OpenAI, and recommendations subject to human approval. This experiment examines traceability, latency and guardrails before any rule changes. It is internal work, not a client engagement or a production deployment.
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Related services
This page describes capabilities. Each service sets out its scope, deliverables and process.
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