Now building Web, Blockchain & AI systems for founders worldwide
This is the AI development page — we integrate LLMs into your product or internal workflows, from RAG-powered search to fine-tuned models and generated-content pipelines.
What this means
Most LLM integrations are shallow — a chat window calling the OpenAI API with a basic prompt. That works for demos. It doesn't work for products that need accurate, grounded responses from your specific data.
RAG systems change this. Your documents, database records, and knowledge base become the model's context. Responses are accurate because they're grounded in your data — not the model guessing from training data.
We build production LLM integrations — with proper evaluation, cost controls, streaming, and observability. AI features that your users trust and your finance team doesn't fear.
This page is for adding AI into a product — RAG, search, summarisation, generation, and other LLM features. Autonomous agents that take actions are covered on AI Agent Development. Phone conversations are covered on Voice Agent Development. Fixed or AI-enhanced pipelines across existing tools are covered on AI Workflow Automation. Broader buyer questions: how to choose an AI development company.
RAG
grounded responses from your own data, not hallucinations
Eval
accuracy, relevance, hallucination rate, and latency measured before production
Any
LLM provider — OpenAI, Anthropic, open-source, or self-hosted
Problems we solve
This is AI development for a defined job inside your product or workflow — not an agent that runs on its own, and not a generic chatbot dropped on the side.
Most LLM integrations are shallow — a chat window calling an API with a basic prompt. That works for demos. It does not work for products that need accurate, grounded responses from your specific data.
RAG systems change this. Your documents, database records, and knowledge bases become the model's context so answers can be cited and kept up to date — instead of the model guessing from training data.
We implement caching, prompt optimization, model routing, token budgeting, and usage monitoring so spend stays predictable and output quality is measured — not hoped for.
We add AI features directly into existing Next.js, React, or Node.js applications — chat interfaces, AI search, document summarisation, and content generation — without a product rewrite.
Services
From architecture to deployed feature. One team, no subcontractors, end to end.
Retrieval Augmented Generation pipelines that ground LLM responses in your own documents, databases, and knowledge bases — accurate, cited, and up to date.
Included on all projects
OpenAI, Anthropic, Mistral, and open-source model integrations — including streaming, function calling, structured outputs, and multi-modal capabilities.
Included on all projects
AI-powered features built directly into your existing Next.js, React, or Node.js application — chat, search, summarisation, generation — without a product rewrite.
Included on all projects
Structured prompt design, evaluation frameworks, and automated testing pipelines to measure output quality, catch regressions, and improve performance over time.
Included on all projects
Caching strategies, prompt compression, and intelligent model routing so you use the cheapest model that meets quality requirements — costs stay predictable at scale.
Included on all projects
LLM call logging, cost dashboards, latency tracking, and alerting. You always know what your AI is doing, what it costs, and when something is wrong.
Included on all projects
Use cases
These are example use cases for LLM integration — not published client case studies. This site does not currently have a dedicated AI case study.
Your knowledge base, made searchable
RAG · Citation · Instant answers
RAG System — Professional Services
A RAG-powered AI assistant built on 5 years of internal documentation, SOPs, and client records — allowing staff to find accurate answers in seconds instead of searching through hundreds of documents.
Example use caseSearch that understands intent
Semantic · Ranked · Explained
LLM Feature — E-commerce SaaS
Semantic search built into an existing product catalogue — customers describe what they need in natural language and the system returns accurate, ranked results with AI-generated explanations.
Example use caseContracts reviewed in minutes
Ingest · Extract · Summarise · Flag
Document Summarisation — Legal Tech
An automated pipeline that ingests legal documents, extracts key clauses, generates structured summaries, and flags risk indicators — turning a 4-hour review into a 10-minute task.
Example use caseHow it works
LLM integrations that skip the evaluation phase ship features that hallucinate under load. We prototype and measure before we commit to production — every time.
Everything in-house. Architecture, development, evaluation, and deployment — all delivered by the Nextelligentia team. One team, kickoff to launch.
We define exactly what the AI needs to do, what data it needs access to, what good output looks like, and what failure modes are unacceptable.
We design the retrieval pipeline, embedding strategy, model selection, and integration architecture. You review and approve before development starts.
A working prototype against your real data. We measure accuracy, relevance, hallucination rate, and latency before committing to a production build.
Full implementation with streaming, error handling, cost controls, caching, and observability. Built to run reliably at the query volumes your product demands.
We deploy, set up monitoring, and run an ongoing evaluation cycle. AI systems improve with iteration — we build the feedback loops that make that happen.
How we build it
The process above is the sequence. These are the parts of an LLM integration a buyer should be able to inspect before a build starts.
We design the retrieval pipeline, embedding strategy, model selection, and integration architecture before development starts. You review and approve that plan. Production builds include streaming, error handling, cost controls, caching, and observability.
Providers already used on this work: OpenAI, Anthropic, Mistral, Gemini, and open-source models such as Llama 3 — including self-hosted when data privacy requires it. Retrieval and embeddings: LangChain, LlamaIndex, Pinecone, Weaviate, pgvector, OpenAI Embeddings, Cohere, and HuggingFace. Application layer: Next.js, Node.js, Python, and FastAPI.
A working prototype is measured against your real data for accuracy, relevance, hallucination rate, and latency before a production build. We also implement output validation, citation grounding for RAG systems, fallback logic for edge cases, and evaluation frameworks that catch regressions.
We deploy, set up monitoring, and run an ongoing evaluation cycle. Observability already included: LLM call logging, cost dashboards, latency tracking, and alerting — so you know what the AI is doing, what it costs, and when something is wrong.
Fit check
We'd rather tell you now than waste 30 minutes on a call.
We're probably right for you if…
We're probably not the right fit if…
Tech stack
LLM Providers
RAG & Retrieval
Embeddings
Frameworks
Observability
Common questions
Answered directly — including when the answer is that we're not the right fit.
Related services
Related Insights
RAG vs AI agents: which architecture does your product need?
Read Insight →
Blockchain for Real Estate: How Property Transactions Are Moving On Chain in 2026
Read Insight →
Blockchain in Healthcare: Real Use Cases That Are Actually Working in 2026
Read Insight →
AI Agents for Healthcare: What They Are and What They Actually Do
Read Insight →
DeFi vs Traditional Finance: What Is the Real Difference in 2026?
Read Insight →
Tell us what you want the AI to do and what data it needs to work with. We'll tell you the right architecture and what it will take to build it properly.