Applied AI that automates real operational work — not a novelty chatbot bolted on the homepage.

Why serious operators are wary of the AI gold rush

'AI' projects that produce a demo and never ship

Your innovation team built a Streamlit demo. It looked clever in the boardroom. Twelve months later it still hasn't reached production. The integration, the data pipeline, the access control, the cost model, the monitoring — none of it got built. We don't do demos. We ship into your operational stack.

Chatbots that hallucinate, with no traceability

Generic LLM bots make confident, plausible, wrong statements. For an auditor, a regulator or a customer-facing context, that's a non-starter. We build RAG (retrieval-augmented generation) grounded in your own data, with citations on every answer — so every claim points back to the source document or database row.

Data going where it shouldn't

OpenAI's default endpoint is a US data flow. For an Irish food producer or a regulated UK SME, that's a data-residency conversation that hasn't been had. We architect for EU residency by default and bring in private-tenant Azure OpenAI, AWS Bedrock EU regions, or open-weights deployed on Hetzner / OVH where data sensitivity demands it.

Cost models that explode in production

A demo costs €30 to run. The same workflow against 50,000 documents a month costs €18,000. We size and benchmark cost before any code ships, choose the right model for each step (small + cheap for routing, large + expensive only for the hard work), and put per-tenant cost monitoring in from day one.

What we actually build with AI / ML

Retrieval-Augmented Generation (RAG) over your own data

Conversational search across your SharePoint, Confluence, S3, MongoDB, Postgres or document store. Every answer cites the source — auditable, not magical. Used for internal knowledge ops, customer-facing self-service, and structured Q&A over contracts, SOPs, audit evidence and product data.

• Document ingestion + chunking + embedding (Azure OpenAI / AWS Bedrock / open-weights).
• Vector search (Pinecone / Weaviate / pgvector / Mongo Atlas Vector).
• Citation-grounded answers — every claim links back to a source paragraph.
• Role-based access control: users only see answers grounded in documents they're permitted to read.
• Continuous re-index on document update; staleness budget configurable.

Computer vision for QC and production

Vision models for food / drink / luxury producers — defect detection, fill-level checking, label-and-pack verification, batch-code OCR. Edge deployment on the line, not a round-trip to the cloud. Trained on your data, not somebody else's.

• Defect detection (under-fill, contamination, label misalignment).
• Batch-code and date-code OCR with confidence thresholds + escalation.
• Label-and-pack verification against the expected SKU layout.
• Edge deployment (NVIDIA Jetson / Coral / x86 with GPU) for sub-100ms response.
• Confidence-driven escalation — vision flags, human operator approves.

Document automation — invoices, consignment notes, COAs

Reading the paper that lands in your finance inbox or your dispatch desk. Extract supplier invoices into Sage / AccountsIQ draft purchase invoices. Read inbound shipping manifests. Pull lot data from supplier COAs into your batch traceability system. Always with a human-in-the-loop approval step.

• Invoice OCR + line-item extraction with confidence scores.
• Approval queue UI — your finance team confirms what the AI proposes.
• Auto-post to Sage / AccountsIQ / Xero / NetSuite only after sign-off.
• COA / certificate-of-analysis ingestion for food, drink and chemical producers.
• Reject + audit trail for low-confidence reads; nothing silent.

Forecasting & operational ML

Time-series forecasting for sales / demand / staff rota / waste-reduction. Not AutoML black boxes — explainable models (Prophet, XGBoost, lightweight neural) with clear feature attribution. Decisions you can defend in a management meeting.

• Demand forecasting at SKU / location / week granularity.
• Staff-rota planning against forecast footfall (hospitality use cases).
• Waste-reduction modelling for food producers and short-shelf-life retail.
• Explainable feature contributions — no 'because the model said so'.

Model choice, model risk & EU data residency

We pick the cheapest model that actually does the job — small open-weights for routing, mid-tier (GPT-4o-mini, Claude Haiku, Gemini Flash) for most work, frontier (GPT-5.2, Claude Opus, Gemini Pro) only where complexity demands it. EU residency on Hetzner, AWS Dublin, Azure West Europe, or private-tenant deployments where data sensitivity demands it.

• Model selection matrix per pipeline step — small/cheap by default.
• Per-tenant cost monitoring with budget alerts.
• EU residency by default; on-prem / air-gapped for sensitive cases.
• Model-risk documentation suitable for NIS2 / GDPR Art. 35 DPIA evidence.
• Eval harness — accuracy / hallucination / latency tracked between versions.

Integration, observability & long-term ownership

AI that lives in your operational stack — not a separate Streamlit toy. APIs, webhooks, queues, the boring parts. Observability from day one (latency, cost, accuracy, drift). Long-term retainer so the system doesn't rot when a vendor changes an API.

• OpenAPI-spec'd backend; calls from your existing applications.
• Async queue (Redis / RabbitMQ) for batch jobs; streaming for real-time.
• Observability: latency, cost, accuracy, drift — exported to your stack.
• Quarterly model-quality review; updates when a new model meaningfully wins.
• Vendor-portable — swap models / providers without rewriting the application.

Where we work

Applied AI / ML projects delivered remote-first, with on-site discovery and integration support for clients in Ireland and the UK.

• Ireland — Dublin, Cork, Limerick, Galway, Waterford, Tipperary (Cashel, Thurles)
• Irish Midlands — Portlaoise, Athlone, Mullingar, Tullamore
• United Kingdom — Cambridge, Suffolk, London, Birmingham, Manchester, Edinburgh
• European Union — remote, EU data residency

Applied AI development Ireland — Dublin, Cork, Galway, Limerick, Tipperary, Cashel, Portlaoise and the Midlands. UK delivery from Bury St Edmunds.

Why operators pick Intellix for applied AI

• We ship into your operational stack. No demos that gather dust. Production AI that integrates with Sage / AccountsIQ / your ERP / your line on day one.
• Vendor-portable by design. OpenAI today, Anthropic tomorrow, open-weights on your own GPU next year. We don't tie you to one provider.
• EU residency unless you say otherwise. Your data stays in EU jurisdiction. Documented for your DPO and your insurer.
• Citation-grounded, not magical. Every answer points to source. Auditable for regulated contexts (food, finance, healthcare).
• Cost-engineered. Small models do routing; frontier models do the hard work. We size cost before code, not after a surprise bill.

Discuss an AI / ML project

Email [email protected] with a paragraph describing the operational pain (invoice processing, defect detection, internal Q&A, forecasting, etc.) — or use the form on the live page. We come back within two working days with whether AI is genuinely the right tool, or whether classical software would do the job for a tenth of the price.

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