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NexMena AI Lab

Applied AI, engineered like infrastructure

The Lab is where we design, evaluate and ship AI systems for organisations that need them to work on Tuesday mornings, not in demos. Method over magic; evidence over adjectives.

Book an AI discovery sessionHow our consulting gates work

Nine focus areas

The Lab concentrates where enterprise value is actually being created. Areas with a detailed page link to it; the rest are described honestly here until theirs exist.

  • AI Agents

    Tool-using systems that plan, act and stay inside authority you define — with human approval where writes happen.

  • Enterprise AI

    LLM capability embedded in your workflows and permission model, not bolted beside them.

  • RAG

    Retrieval-augmented generation over your documents, with citations and permission filtering at retrieval time.

  • AI Automation

    Ops telemetry correlated and acted on — noise down, runbooks automated, humans kept for judgement.

  • AI Chatbots

    Assistants grounded in your content with refusal behaviour designed, not hoped for.

  • Computer Vision

    Detection and inspection on camera and sensor feeds, tuned to your site's footage — never a demo reel's.

  • Predictive Analytics

    Forecasting and scoring with backtesting against the boring baseline first — beating it is the bar.

  • AI Integration

    Model capability wired into ERP, CRM and line systems through contracts, idempotency and audit — the unglamorous part that decides success.

  • Custom AI Platforms

    The data platform, evaluation harness and MLOps under all of the above, built once and reused.

Enterprise RAG, drawn honestly

This is the architecture we actually build — including the parts vendor diagrams omit: guardrails on both sides of the model, and the evaluation loop that keeps retrieval honest after launch.

Enterprise RAG reference architecture: ingestion pipeline, query path, and the evaluation loopIngestion pipeline: Sources → Parse & Clean → Chunking → Embedding → Vector Store. Query path: Query → Retrieval → Rerank → Generation → Response, with Permission filter (caller identity) applied at retrieval, Input guardrails before generation and Output guardrails after it. Evaluation loop: Gold set + scoring scores every change and feeds back into chunking, embedding and generation.Ingestion pipelineQuery pathEvaluation loopSourcesParse & CleanChunkingEmbeddingVector StoreQueryRetrievalRerankGenerationResponsePermission filter (caller identity)Input guardrailsOutput guardrailsGold set + scoring
Enterprise RAG reference architecture: ingestion pipeline, query path, and the evaluation loop
  • Permission filtering happens at retrieval time, against the caller's identity — filtering after generation is a leak with extra steps.
  • Reranking sits between retrieval and generation because vector similarity is a shortlist, not an answer: a cross-encoder reorders the shortlist by actual relevance.
  • The evaluation loop is offline and continuous: a gold set your experts agreed on, scored on every change to chunking, embeddings or prompts — so quality regressions are caught by CI, not by users.

How AI ships here

Five phases, each with an exit it must earn. The gates are where bad projects die cheaply — which is the method working, not failing.

  1. Discovery

    Candidates ranked by value and data reality. Exit: one or two use-cases worth proving, or an honest none.

  2. PoC

    The decisive technical question answered on your data in weeks — retrieval quality, signal strength, feasibility. Exit: a measured result against a pre-agreed bar.

  3. Pilot

    Real users, real workflow, agreed metric. Exit: the number beats the baseline in production conditions, or we stop and write down why.

  4. Production

    Hardening, permissions, cost ceilings, failure modes, rollback. Exit: the system survives your security review and ours.

  5. MLOps

    Drift watched, evaluations run on change, costs attributed, retraining triggered by evidence. Exit: none — this phase is the operating state.

Three ways to engage

Sized to the certainty you have. Each one ends with something you keep, whatever you decide next.

  • Start here if AI is a question

    AI Readiness Assessment

    A short, structured look at your data, workflows and constraints. You keep the ranked candidate map and the honest gaps list.

  • Start here if you have a candidate

    Four-week PoC

    One decisive question, your data, a pre-agreed measure. You keep the code, the evaluation harness and the numbers — pass or fail.

  • Start here after a proven pilot

    Full build

    Pilot through production and into MLOps, on the methodology above, with the gates priced separately so stopping stays cheap.

What you will not find on this page

No accuracy percentages, no client logos, no claims about deployed systems. Numbers we can evidence go in proposals, where they can be checked; a methodology honestly described is the only benchmark a serious buyer should trust from a website.

Book an AI discovery session

Bring one workflow that frustrates you. We will bring the questions that decide whether AI belongs in it.

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