From LLM integrations and RAG knowledge bases to autonomous AI agents and full custom software — N3XUS engineers production-grade AI systems that solve real business problems. Serving US and global clients from at world-class quality.
From frontier LLMs to production infrastructure — we use the right tools for each job and stay current with the rapidly evolving AI landscape.
Every AI system we build is production-grade, well-documented, and designed for long-term reliability — not just a prototype that breaks at scale.
We build applications on top of the world's most capable language models — Claude, GPT-4o, Gemini, and open-source Llama 3. This goes far beyond simple API calls. We architect full context management strategies, intelligent prompt engineering, structured output parsing, tool-use/function-calling, multi-modal inputs, and streaming interfaces.
Whether you need a customer-facing AI assistant, an internal knowledge tool, a document processing pipeline, or a creative generation engine — we engineer it to production standards.
Retrieval-Augmented Generation (RAG) connects your LLM to your own data. Instead of guessing or hallucinating, the model retrieves exact information from your documents, databases, and knowledge sources before generating its response.
We build complete RAG pipelines: document ingestion and chunking, embedding generation, vector store setup, hybrid search (semantic + keyword), reranking, and the generation layer. The result is an AI that genuinely knows your business.
AI agents go beyond chatbots — they plan, reason, use tools, execute multi-step workflows, and operate autonomously to complete complex tasks. We build multi-agent systems where specialised AI agents collaborate: a research agent, a writing agent, a QA agent, each handling what they do best.
Using LangChain, LangGraph, CrewAI and AutoGen, we architect agentic systems that integrate with your existing tools (APIs, databases, email, CRMs) and handle real business processes end-to-end.
Full-stack applications with AI at the core — not bolted on as an afterthought. We design and build internal tools, SaaS products, enterprise dashboards, and business platforms that use AI for intelligent automation, decision support, and user experience enhancement.
This includes the complete stack: backend APIs (FastAPI, Node.js), frontend (React, Next.js), database architecture, authentication, and deployment — all production-ready and maintainable.
Not generic chatbot builders — we build genuinely intelligent conversational AI systems trained on your specific knowledge, aligned to your brand voice, and integrated into your real systems. Deployed on web, WhatsApp, Slack, or any channel your customers use.
Our chatbots go beyond FAQ: they qualify leads, book appointments, process requests, escalate to humans at the right moment, and learn from each conversation. All with full analytics.
Machine learning models that predict what matters to your business: which leads will convert, which customers are about to churn, which inventory items will run out, which transactions look fraudulent. We build custom prediction systems trained on your historical data.
From classical ML (scikit-learn, XGBoost) to deep learning (PyTorch, TensorFlow) to time series forecasting — we select and implement the right approach for your specific prediction problem.
Most AI value is unlocked through integration — connecting AI capabilities to the systems you already run. We build clean, well-documented APIs and integration layers that connect LLMs and AI services to your CRM, ERP, data warehouse, marketing stack, or custom software.
We handle the complexity: authentication, rate limiting, webhook management, error handling, monitoring, and the business logic that makes AI fit seamlessly into your existing workflows.
Not sure where AI creates the most value in your business? We run structured AI discovery engagements: mapping your processes, identifying the highest-ROI automation and augmentation opportunities, and building an implementation roadmap you can actually execute — with realistic timelines and budget estimates.
We also conduct technical AI audits of existing systems: reviewing architecture, identifying bottlenecks, and recommending improvements to prompting, retrieval, or model selection.
Every industry has processes that read, classify, generate, or predict. Every one of those processes can be accelerated, automated, or enhanced with AI. Here's where we've built:
Clinical documentation automation, patient intake chatbots, symptom triage assistants, medical coding support, appointment scheduling AI.
Risk analysis tools, compliance document review, fraud detection signals, personalised financial advice assistants, automated reporting.
Contract review and redlining, legal research assistants, document summarisation, clause extraction, compliance monitoring systems.
Product recommendation engines, AI customer service, dynamic pricing analysis, inventory demand forecasting, review analysis.
Intelligent property matching, AI inquiry handling, automated valuation explanations, contract generation, market analysis tools.
Personalised learning assistants, automated grading support, curriculum generation, student progress analysis, parent communication AI.
Document processing (BOLs, invoices), route optimisation inputs, driver communication automation, supplier query handling.
AI content generation at scale, brand voice assistants, ad copy optimisation, audience analysis, automated social media management.
Don't see your industry? We've likely built something similar. Every business with data and processes has AI opportunity.
Most "AI products" in the market are thin wrappers around a chat API call. They break under real usage, hallucinate on business-critical questions, and have no architecture for scale or reliability.
We build differently. A production RAG system, for example, involves: document ingestion workers, intelligent chunking strategies tuned for document type, embedding pipelines running best-in-class models, vector stores with proper indexing, hybrid retrieval combining semantic similarity with keyword search, reranking passes for precision, and only then the generation layer with carefully engineered system prompts and output validation.
We follow a structured engineering process that minimises risk and maximises the chance your AI system actually solves the problem it was built for.
We start by deeply understanding your problem — not jumping to a solution. What does success look like? What data do you have? What are the edge cases? We produce a detailed technical scoping document with architecture options and fixed-price estimates.
Model selection, data flow design, component architecture, integration strategy, and evaluation criteria. We decide which LLMs, frameworks, and infrastructure are right for your specific use case — not just what we know best.
We build a working prototype first — fast. You see the AI in action before full development begins. This is where we surface surprises, validate assumptions, and lock the production architecture. No surprises at delivery.
Full development with proper testing, error handling, security, and documentation. Every system we ship has: unit tests, integration tests, AI evaluation suites, API documentation, and a deployment runbook.
Live deployment with monitoring, alerting, and cost tracking. After launch we monitor system performance, analyse failure modes, and iterate. AI systems improve significantly in the first 30 days of production traffic.
We serve clients globally — understanding diverse business standards, communication norms, code quality expectations, and project management preferences. Our team works across time zones to stay aligned with your business hours.
Based in , we deliver engineering talent that competes globally at 40–60% of equivalent US agency rates — without the quality trade-offs of race-to-bottom offshore shops.
The AI landscape changes weekly. We invest heavily in staying current — testing new models, frameworks, and techniques as they emerge. You benefit from the best available tools, not what we learned two years ago.
Every system ships with documentation, tests, monitoring, and a clear architecture. We write code that your own engineers can maintain — not black boxes that only we can modify.
Most AI shops can only do the ML layer. We handle the full stack: AI models, APIs, databases, frontend, cloud infrastructure, and ongoing operations. One team, not four vendors.
Uniquely, N3XUS also runs marketing campaigns. Your AI system can be connected to your SEO, ads, and content strategy — closing the loop from AI acquisition to AI-powered customer experience.
Fixed-price scoping documents provided after discovery call. All prices are in USD. We accept international transfers, credit cards, and cryptocurrency.
All prices are project-based with fixed-price scoping documents provided before any commitment. Ongoing AI support retainers available from $500/mo.
Choose the right chatbot tier for your business stage. All tiers use frontier LLMs — the difference is depth of integration and capability.
| Capability | Starter from $1,500 |
Professional from $3,500 |
Enterprise from $8,000 |
|---|---|---|---|
| Website chat widget | ✓ | ✓ | ✓ |
| Custom AI personality & voice | ✓ | ✓ | ✓ |
| RAG knowledge base | Basic | ✓ | ✓ |
| CRM integration | ✓ | ✓ | ✓ |
| WhatsApp / Slack channel | ✗ | ✓ | ✓ |
| Booking flow automation | ✗ | ✓ | ✓ |
| Multi-step agent workflows | ✗ | ✗ | ✓ |
| Voice interface | ✗ | ✗ | ✓ |
| Human escalation + context | Basic | Full | Enterprise |
| Conversation analytics | Basic | Advanced | Enterprise |
| API access for custom logic | ✗ | ✓ | ✓ |
We work with all major frontier LLMs: Anthropic Claude (our primary recommendation for reasoning tasks), OpenAI GPT-4o and o1, Google Gemini, Meta Llama 3 (open source, self-hosted), and Mistral. For orchestration: LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen. Vector databases: Pinecone, Weaviate, Chroma, pgvector. Infrastructure: Python, TypeScript, FastAPI, Docker, AWS/GCP/Azure. We select the right stack for your specific use case — not what's easiest for us.
Yes — we serve clients across the US, Europe, and globally. We structure our processes around your time zone, maintain overlap hours with your business day, and use tools you already know (Slack, Notion, Linear, GitHub). Our base means you often receive progress updates overnight — work happens while you sleep. Most clients find the remote working relationship seamless after the first week.
RAG (Retrieval-Augmented Generation) connects an LLM to your private data — documents, databases, knowledge bases — so it answers from your information rather than just general training. You need a RAG system if you want an AI that answers questions about your specific products, policies, or history without hallucinating. It's the standard architecture for any business knowledge assistant. We build production RAG systems using LlamaIndex, Pinecone or Weaviate, and hybrid retrieval strategies that dramatically reduce hallucinations.
Timelines depend on complexity. A basic AI chatbot integration: 1–2 weeks. A RAG system with custom ingestion and UI: 3–5 weeks. A full multi-agent workflow with enterprise integrations: 8–12 weeks. We always start with a scoping engagement (1–3 days) that produces a detailed project plan with fixed timelines. You know exactly when you'll have a working system before we start building.
Three things: (1) Engineering rigour — we build production-grade systems with tests, documentation, monitoring, and clear architecture. Not prototypes dressed as products. (2) Full-stack capability — we handle AI, APIs, databases, frontend, and infrastructure. One team, end-to-end. (3) Marketing integration — we also run digital marketing and LLM marketing campaigns, so your AI system connects to your growth strategy, not just your operations. This combination is genuinely rare.
Yes. Every project includes 30–90 days of post-launch support depending on tier. For ongoing maintenance, monitoring, model updates, and iterative improvement, we offer monthly AI support retainers starting from $500/mo (USD). AI systems require ongoing attention — model APIs change, performance drifts, and usage patterns reveal opportunities for improvement. We keep your system sharp.
Book a free AI consultation. We'll map your processes, identify the highest-impact AI opportunities, and give you a clear architecture recommendation — whether you work with us or not.
