Active Engagements.
Three industries.
We share the problem, the approach, and the outcomes. In each case our engineers were embedded inside the client's environment, not building from a distance.
US RF & Microwave Components Manufacturer
A niche electronics company with ~10,000 RF filter SKUs was losing hours every day to repetitive technical inquiries from engineers. 50% of inbound requests required custom or semi-custom specifications. The catalogue was deep, the data lived across PDFs and ERP exports, and senior engineers spent their best hours fielding lookups. Nomicore embedded inside the client environment to capture the domain vocabulary, structure the catalogue, and build an internal sales assistant that matches engineer specs to components — including tolerance-based fallbacks when no exact match exists.
SKUs matched by AI to engineer specs with tolerance-based fallback.
Target reduction in repetitive manual technical inquiries.
To full domain vocabulary, data audit, and architecture proposal.
European Licensed Merchandise Retailer
A specialist pop-culture merchandise retailer with 10 regional storefronts and a catalogue spanning hundreds of franchises had an off-peak conversion rate of 0.96%. Customers couldn't find products — no semantic search, no character-level filters, no gift-finding experience. Nomicore built a semantic search and fan/gift assistant layer that handles intent-led queries like "something for a Star Wars fan under £40", and deployed a customer service co-pilot inside the existing Zendesk helpdesk to draft policy-grounded replies. Eight weeks from start to A/B test live.
Covered by the AI layer — single deployment, market-aware behaviour.
From engagement start to semantic search POC with A/B test live.
Semantic search, fan/gift assistant, and helpdesk co-pilot.
Automotive Documentation Platform
An automotive manual platform needed to make 400,000+ vehicle manuals instantly queryable by mechanics — handling natural language questions about repair procedures, torque specs, wiring diagrams, and diagnostic codes across every make, model, and year. Off-the-shelf tools couldn't handle the document scale or the VIN-aware retrieval needed. Nomicore designed and built the full stack — Next.js front-end, RAG pipeline, VIN decoder integration, OCR for legacy scans, staging environment, and production CI/CD — delivered across three milestones. Full IP transferred at each milestone.
Ingested into the RAG pipeline with VIN-aware retrieval.
Pipeline, chat UI, beta launch — IP transferred at each.
Next.js, RAG, VIN decoder, OCR, staging, and production CI/CD.
Start with the AI Readiness Audit.
A 2–3 week diagnostic that maps the expertise inside your business and tells you exactly where AI creates the most value. Output is a one-page scorecard and a prioritised action plan. No build commitment required.