Now building Web, Blockchain & AI systems for founders worldwide
We integrate AI into your existing product or workflows — from RAG-powered search to fine-tuned models and AI-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.
RAG
grounded responses from your own data, not hallucinations
60%+
typical reduction in AI operational costs with model routing
Any
LLM provider — OpenAI, Anthropic, open-source, or self-hosted
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
Our work
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.
90% faster information retrievalSearch 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.
35% higher conversionContracts reviewed in minutes
Ingest · Extract · Summarise · Flag
AI Workflow — 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.
4h → 10min document reviewHow 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.
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.
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.