SOLUTIONS / INTELLIGENCE
Enterprise AI and intelligent automation.
Production AI services — grounded knowledge assistants, intelligent document processing and agent-assisted workflows — engineered with security, evaluation, cost control and operational readiness from day one.
What we deliver
End-to-end delivery of an enterprise AI capability — from use-case selection through to a monitored, cost-controlled production service — on Azure or private infrastructure.
From pilot to production
Many organisations have AI pilots; few have AI services they can rely on. The gap is engineering: permission-aware data access, retrieval quality, evaluation, monitoring, cost management and a defined fallback when the model cannot help. That gap is where we focus.
Typical use cases
We prioritise use cases where value is measurable and risk is controllable.
- Knowledge assistants over policies, procedures, contracts and technical documentation, with citations and security trimming
- Intelligent document processing: classify, extract and validate data from invoices, forms and correspondence with confidence thresholds and human review
- Service-desk and case triage: summarisation, routing and drafted responses for agent approval
- Agent-assisted workflows that call internal APIs through permissioned tools and explicit approval steps
- Private AI on locally hosted open-weight models where data must not leave your environment
Reference architecture
A typical enterprise AI service on Azure is composed of the following layers.
- Channels: Microsoft Teams, web applications or existing line-of-business systems
- Orchestration: Microsoft Agent Framework, Semantic Kernel or LangGraph, hosted on Container Apps or Azure Functions
- Retrieval: Azure AI Search with hybrid (keyword + vector) search, semantic ranking and document-level security filters
- Models: Azure OpenAI or Azure AI Foundry deployments, or a private vLLM endpoint for open-weight models
- Gateway: Azure API Management AI gateway policies for token limits, load balancing, semantic caching and cost attribution
- Trust: Microsoft Entra ID authentication, Azure AI Content Safety, tracing with OpenTelemetry and continuous evaluation
Delivery phases
We deliver AI in phases with explicit exit criteria, so investment follows evidence.
- Phase 1 — Use-case selection and data readiness (1–2 weeks)
- Phase 2 — Proof of value with real data and an evaluation set; agreed thresholds for accuracy, latency and cost (3–6 weeks)
- Phase 3 — Production hardening: security review, load and cost testing, monitoring, support processes
- Phase 4 — Controlled rollout and continuous evaluation; ideas that miss thresholds are stopped early
Problems we address
- Stalled or untrusted generative AI pilots
- High volumes of documents processed manually
- Staff spending time searching for information across systems
- Concerns about data leakage, accuracy, permissions and cost
Technologies and approaches
- Azure OpenAI and Azure AI Foundry
- Azure AI Search
- Azure AI Document Intelligence
- Microsoft Agent Framework and Semantic Kernel
- LangChain and LangGraph
- n8n and Logic Apps
- vLLM and Ollama for private models
- Azure API Management AI gateway
- Microsoft Entra ID
Typical engagement activities
- Use-case prioritisation and value modelling
- Data readiness and retrieval design
- Proof of value with evaluation thresholds
- Production architecture, security and threat review
- Incremental build, rollout and cost tuning
Deliverables
- Prioritised use-case backlog with value estimates
- Production AI service integrated with your systems
- Evaluation suite with baseline and target metrics
- Governance, security and data-handling documentation
- Operational runbook, dashboards and cost model
Potential business outcomes
- Measurable time savings on targeted processes
- Consistent, cited answers to common questions
- Controlled, attributable AI operating costs
- A reusable platform for subsequent AI use cases
Contact: contact@nexaarislabs.com