EXPERTISE / AI

Enterprise AI engineering: agents, retrieval and LLMOps.

We design and build grounded AI assistants, agentic systems and AI-enabled workflows — on Azure, with open-source frameworks or with private, locally hosted models — with measurable quality, clear boundaries and human oversight.

What we deliver

Production-grade AI capabilities — knowledge assistants, document and workflow automation, tool-using agents and private model deployments — with security, evaluation and operations designed in from the start.

Our position on AI

Language models are powerful and fallible. We treat them as components inside an engineered system: their inputs are controlled, their actions are permissioned, their outputs are evaluated and their failures are expected and handled. That discipline is what makes AI safe to put in front of employees and customers — and it applies whichever model or framework is used.

Orchestration and agent frameworks

An orchestrator coordinates model calls, tools, memory and control flow. We choose the framework to fit your team's language, hosting model and need for control, and we keep business logic outside framework-specific code so it can be changed later.

• Microsoft Agent Framework and Semantic Kernel — first-class .NET and Python support, Azure integration, plugins and multi-agent patterns; a natural fit for .NET estates
• LangGraph — explicit, stateful agent graphs with checkpointing, retries and human-in-the-loop approval steps for long-running processes
• LangChain and LlamaIndex — broad model, retriever and vector-store integrations for Python-based retrieval and data pipelines
• Model Context Protocol (MCP) — standard servers that expose tools and context to any compliant agent or assistant, with authorisation enforced on the server
• Deterministic workflows where an agent is unnecessary: many "agent" problems are better solved by a fixed sequence with one or two model calls

Workflow automation with n8n, Logic Apps and Power Automate

Not every AI use case needs custom code. Low-code workflow tools can place a model step — classification, extraction, summarisation, drafting — inside an existing business process with approvals, retries and audit.

• n8n: self-hostable workflow automation with AI agent and LangChain-based nodes, useful when data must stay on your infrastructure; we review its Sustainable Use Licence against your intended use
• Azure Logic Apps and Power Automate: managed connectors and enterprise governance within the Microsoft ecosystem
• Clear hand-off points between automation and people, with every model decision logged

Context engineering and retrieval

Most answer quality comes from what the model is given, not from the model itself. Context engineering decides what information reaches the model, in what form, within what budget — and who is allowed to see it.

• Retrieval-augmented generation (RAG) with hybrid keyword and vector search, semantic re-ranking and metadata filters
• Chunking and enrichment strategies tested against your documents, not defaults
• Permission-aware retrieval (security trimming) so users only receive content they are entitled to
• Conversation memory and summarisation within explicit token budgets; prompt caching to reduce cost and latency
• Citations and source links on every grounded answer

Prompt engineering as software

Prompts are code. We version them, review them, test them against evaluation sets and release them through the same pipeline as the application.

• Clear system instructions, role separation and few-shot examples where they measurably help
• Structured outputs constrained by JSON schema, validated before use
• Separation of trusted instructions from untrusted content to reduce prompt-injection risk
• Prompt templates parameterised and stored alongside the code that uses them

Model selection, inference and cost

The largest model is rarely the right default. We select models per task, measure quality against cost and latency, and design the inference path so it can change as models improve.

• Azure OpenAI and the Azure AI Foundry model catalogue (OpenAI, Meta Llama, Mistral, Microsoft Phi and others)
• Deployment choices: standard pay-as-you-go, global, provisioned throughput (PTU) and batch, matched to traffic shape
• Model routing — small, fast models for simple steps and larger models only where needed
• AI gateway patterns with Azure API Management: token limits, load balancing across deployments, semantic caching and usage attribution

Local and private language models

Open-weight models can run on your own hardware or private cloud when data residency, offline operation or predictable cost matter more than access to the very largest models.

• Serving with vLLM (high-throughput, OpenAI-compatible API), llama.cpp and Ollama for development and edge use
• Open-weight model families such as Llama, Mistral, Phi, Qwen and Gemma, selected by licence, size and task fit
• Quantisation (for example GGUF 4/8-bit, AWQ, GPTQ) to fit models to available GPU or CPU memory, with quality measured after each change
• Honest trade-offs: GPU capacity, patching, scaling and evaluation become your responsibility

Fine-tuning and model adaptation

Fine-tuning is powerful but often unnecessary. We start with prompting and retrieval, and fine-tune only when evaluation shows a persistent gap in style, format or domain behaviour.

• Parameter-efficient methods (LoRA, QLoRA) for open-weight models
• Azure OpenAI supervised fine-tuning and, for supported models, preference optimisation
• Training data curation, de-identification and held-out evaluation sets
• Distillation of large-model behaviour into smaller, cheaper models for high-volume tasks

Evaluation, safety and LLMOps

AI systems are non-deterministic, so quality is measured, not assumed. We put evaluation and observability in place before the first user sees the system.

• Offline evaluation sets for groundedness, relevance, task success and tool-call accuracy, run on every change
• Tracing with OpenTelemetry generative-AI conventions, Azure AI Foundry tracing or LangSmith
• Azure AI Content Safety, input/output filtering and red-team testing for misuse and injection
• Dashboards for cost per task, latency and failure modes, with alerts on regressions

Problems we address

• AI pilots that impressed in a demo but cannot be trusted in production
• Knowledge spread across documents, systems and teams that people struggle to find
• Repetitive, rules-heavy work that consumes skilled people's time
• Data-residency or confidentiality constraints that rule out public AI services
• Rising model costs with little visibility of what drives them
• Uncertainty about data protection, permissions and governance for generative AI

Technologies and approaches

• Azure OpenAI and Azure AI Foundry
• Microsoft Agent Framework and Semantic Kernel
• LangChain, LangGraph and LlamaIndex
• Model Context Protocol (MCP)
• n8n, Logic Apps and Power Automate
• Azure AI Search and vector databases
• vLLM, llama.cpp and Ollama
• Hugging Face open-weight models
• LoRA / QLoRA fine-tuning
• Azure API Management AI gateway
• Azure AI Content Safety
• OpenTelemetry, LangSmith and Foundry tracing
• .NET and Python

Typical engagement activities

• Use-case discovery and value/feasibility assessment
• Data readiness, retrieval design and permission model
• Framework, model and hosting selection (cloud, private or hybrid)
• Agent, tool and workflow design with explicit permission boundaries
• Evaluation dataset creation and automated quality measurement
• Build, integration, controlled rollout and cost tuning

Deliverables

• AI solution architecture and decision records
• Working assistant, agent or automated workflow integrated with your systems
• Evaluation harness with baseline quality, cost and latency metrics
• Prompt, model and retrieval configuration under version control
• Responsible-AI, security and data-handling documentation
• Operational runbook, monitoring dashboards and cost model

Potential business outcomes

• AI features that users trust because answers are grounded and traceable
• Reduced manual effort on well-defined, repetitive tasks
• Predictable model costs through routing, caching and right-sized models
• Governance that satisfies security and data-protection stakeholders
• A reusable foundation for subsequent AI use cases

Contact: contact@nexaarislabs.com

Nexaaris Labs Ltd is a private limited company registered in England and Wales, company number 16746745. Registered office: 2 Shepton Road, Broughton, Milton Keynes, Buckinghamshire, MK10 7AH, United Kingdom.