NCompas builds AI-native web applications — with streaming copilots, RAG knowledge systems, conversational UX, computer vision, and real-time AI inference — delivered in production in 3–6 weeks.
Why NCompas
The gap between an AI application demo and a production AI application is wider than for any other class of software. Here's how we close it.
We design AI into the UX from the first wireframe — not added as an afterthought widget. That means AI copilots that understand application context, personalisation that uses real session signals, and interfaces designed around how LLMs work, not around how form builders work.
From the React component streaming tokens to the Python RAG pipeline ingesting your documents to the vector index serving semantic search — NCompas engineers own the entire AI application stack. No handoffs between a frontend team and a data science team who've never met.
We've built RAG systems across Azure AI Search, pgvector, Pinecone, and Weaviate — and we know which tradeoffs matter in production: chunking strategies, embedding model selection, hybrid search tuning, re-ranking, and citation accuracy. Your knowledge system will actually work.
We don't build web applications that make the user wait for the AI to finish thinking. Server-Sent Events, optimistic rendering, token streaming, and skeleton loaders are the default — because perceived performance is real performance when the model takes 8 seconds to respond.
Rate limiting, content moderation, PII detection, audit logging, cost controls, guardrails, and prompt injection protection built into the architecture from the start. AI applications in production have failure modes that traditional web apps don't — we design for them.
AI application scaffolding, component libraries, RAG templates, streaming primitives, and deployment pipelines are pre-built. A production-ready AI-enhanced web application doesn't take 6 months — it takes 3–6 weeks. We've done it repeatedly across industries.
Six AI web capabilities
Six production-ready AI capability patterns, each designed to be embedded natively into your web application's UX — not bolted on as a chatbot widget.
Retrieval-Augmented Generation embedded in your web product — so your application can answer questions from your documents, your knowledge base, your database, and your proprietary data with LLM-quality responses. Azure AI Search, Pinecone, pgvector, and hybrid search architectures used in production.
Context-aware AI assistants embedded directly into your web application — not a floating chatbot, but a copilot that understands what the user is doing, what data they're looking at, and what they're trying to achieve. Streaming responses, tool-calling, multi-turn memory, and function execution built in.
Retrieval-Augmented Generation embedded in your web product — so your application can answer questions from your documents, your knowledge base, your database, and your proprietary data with LLM-quality responses. Azure AI Search, Pinecone, pgvector, and hybrid search architectures used in production.
Web applications that learn from every interaction — adapting content, layout, recommendations, and workflows to each user's behaviour patterns, preferences, and intent signals. Not rule-based segmentation, but ML-driven personalisation that improves with every session.
Web applications that can see — image classification, object detection, OCR, document processing, visual search, and real-time camera analysis running in-browser or via API. From manufacturing defect detection to e-commerce visual search to medical image annotation, computer vision is now a web-native capability.
Replace form-heavy workflows with natural language — search that understands intent, filters driven by conversation, data entry via voice or text, and interfaces that let users describe what they want instead of clicking through nested menus. NLP-first UX design patterns that reduce friction by an order of magnitude.
Web applications with live AI inference — streaming LLM token output, real-time anomaly detection, live dashboards powered by ML, and event-driven AI pipelines that surface insights as data arrives. Server-Sent Events, WebSocket connections, and edge inference bring AI latency from seconds to milliseconds.
Architecture
AI-enhanced web applications span three distinct engineering layers. NCompas owns and builds all three — no handoffs, no gaps in accountability.
React / Next.js AI-native UI layer
Orchestration, RAG, tools & routing
Foundation models, embeddings, storage
Delivery approach
Five stages from discovery to deployed — built to get AI in users' hands fast, with the infrastructure to scale.
Week 1: map the user journeys where AI creates the most value. Define the AI capabilities needed, the data sources, latency requirements, and compliance constraints. Output: AI application architecture blueprint and a prioritised feature roadmap.
Week 1–2: ingest your documents, databases, and knowledge sources. Build embedding pipelines, configure vector indices, and validate retrieval accuracy before a single UI component is written. The AI foundation is engineered first.
Week 2–3: wire the LLM APIs, tool-calling functions, and streaming endpoints. Build the AI middleware layer — prompt templates, context injection, guardrails, and cost controls — with full observability via LangSmith or Azure AI Studio.
Week 3–5: build the React / Next.js application with streaming components, optimistic UI, and AI-native interaction patterns. Every AI feature is user-tested against the non-AI baseline to validate it actually improves task completion.
Week 5–6: CI/CD pipeline, production deployment on Vercel or Azure Static Web Apps, monitoring dashboards, cost alerts, and a handover knowledge session for your engineering team. AI observability included so you can see what users are asking and what the model is doing.
Client outcomes
Four organisations that replaced traditional web interfaces with AI-native applications — and the measurable outcomes that followed.
The Challenge
Asset management firm with 120 financial analysts spending 4–6 hours daily reading research reports, earnings transcripts, and regulatory filings before generating client recommendations. Knowledge trapped in unstructured documents with no way to query across it.
What We Built
RAG-powered research intelligence platform — analysts type a natural language question ("What did [company] say about margin compression in the last 3 earnings calls?") and get a sourced, synthesised answer drawn from a 50,000-document corpus. Built on Azure OpenAI, pgvector, and a streaming React frontend. Deployed in 5 weeks.
Asset management firm with 120 financial analysts spending 4–6 hours daily reading research reports, earnings transcripts, and regulatory filings before generating client recommendations. Knowledge trapped in unstructured documents with no way to query across it.
NHS Trust with a patient-facing web portal where patients frequently abandoned before completing appointment bookings, medication requests, and referral forms — 63% abandonment rate on multi-step forms with clinical terminology unfamiliar to patients.
Fashion e-commerce platform with 850,000 SKUs and a text search that returned irrelevant results for style-based queries ("dresses for a garden wedding in summer that aren't too formal"). High bounce rate from search results. No visual discovery capability.
Global manufacturer's engineering team using a web portal to file non-conformance reports — 45-minute average completion time due to complex forms, manual equipment lookup, and a separate system for attaching failure photos with no automatic classification.
Technologies we build with
higher user engagement for AI-enhanced web applications vs. traditional equivalents — because interfaces that understand intent feel fundamentally different to use, and users return to them.
answer accuracy for RAG-powered knowledge systems using hybrid search + re-ranking — vs. 47% for keyword search and 61% for pure vector search in enterprise document corpora.
production delivery time for AI-enhanced web applications built on NCompas's AI-native development stack — vs. 4–6 months for teams standing up AI infrastructure from scratch.
the year AI copilots and in-app assistants became the #1 most-requested feature in enterprise web application procurement — ahead of mobile responsiveness and SSO for the first time (Gartner, 2025).
Insights from real-world engineering, cloud, and AI leaders - helping you make better decisions, faster.
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Start with a free AI Application Design Review — we'll look at your existing web application, identify the three highest-impact AI enhancements, and sketch an architecture that gets them into production in weeks, not months.