LLM Integration
We integrate large language models (OpenAI, Anthropic Claude, open-source) into your product — with proper prompting, context management, and safety guardrails.
Custom AI applications, intelligent workflows, and knowledge systems built for your exact use case — not generic off-the-shelf tools. Vancouver-based, built at our cost first.
Practical AI applications built on proven architectures — designed for real business problems, not demos.
We integrate large language models (OpenAI, Anthropic Claude, open-source) into your product — with proper prompting, context management, and safety guardrails.
Retrieval-Augmented Generation (RAG) lets your AI tool answer questions from your own documents, databases, and knowledge bases — accurately, with citations.
Branded AI assistants for customer support, internal knowledge, sales qualification, or any use case requiring natural language interaction.
AI-powered automation that handles repetitive tasks, processes documents, classifies data, and integrates with your existing tools via APIs.
Multi-step autonomous agents that can plan, use tools, and complete complex tasks — from data research to code generation to content production.
Every AI system we build includes evaluation frameworks, output filtering, hallucination reduction, and monitoring so it works reliably in production.
Real AI products — RAG systems, intelligent assistants, and workflow automation — built for Canadian businesses.
AI Tool
RAG-based AI assistant for a Canadian professional services firm — monitors federal and provincial regulatory databases, extracts relevant updates, and answers compliance questions with cited source paragraphs. Scoped, built, and live in 6 weeks with zero upfront cost to the client.
Regulatory research time cut by 80%AI Tool
AI-powered contract review tool for a Vancouver B2B services firm — extracts key clauses, flags non-standard terms, scores risk exposure by category, and generates plain-English summaries for non-legal stakeholders reviewing agreements.
Contract review time: 4 hours → 20 minutesAI Tool
Private knowledge base assistant deployed across a BC company's internal documentation — onboarding guides, HR policies, and operations manuals — with department-level permission controls and an admin dashboard for document management.
Support ticket volume reduced by 45%Our build-first model applies to every service we offer.
Step 01
Book a free AI scoping call. We review your use case, recommend the right architecture (RAG vs fine-tuning vs agents), estimate the build, and begin immediately — no upfront cost.
Step 02
Our team designs and develops your product. You review progress and give feedback at every stage.
Step 03
Your Development. launches with real functionality and a polished, tested user experience.
Step 04
A simple monthly subscription begins after launch. You pay for a working product — not a promise.
Anyone can build a chatbot demo. We build AI systems with evaluation frameworks, output monitoring, hallucination controls, and fallback logic — things that matter in production.
We work with OpenAI, Anthropic Claude, Google Gemini, and open-source models. We recommend the right model for your use case — not the one we're most familiar with.
Most AI tools that need to answer from your own data need RAG. We implement vector databases, embedding pipelines, and retrieval strategies that keep answers accurate and grounded.
We build custom AI applications using large language models (LLMs) — including RAG-based knowledge assistants, customer support chatbots, document processing tools, AI agents, and workflow automation. We work with OpenAI, Anthropic Claude, and open-source models.
RAG (Retrieval-Augmented Generation) is a technique that gives AI tools access to your specific documents, data, or knowledge base so they can answer questions accurately — without hallucinating or making things up. If you want an AI that knows your business, RAG is almost always the right choice.
A focused AI tool — a chatbot, a document Q&A system, or a workflow automation — typically takes 4–8 weeks. More complex multi-agent systems or products requiring custom fine-tuning can take 10–16 weeks.
Yes. We implement proper data handling practices, use enterprise API tiers with no training on your data, and can deploy models on your own infrastructure if required. We discuss data residency and privacy requirements on the scoping call.
Model updates are included in your ongoing monthly subscription. When a new model version improves performance for your use case, we evaluate and migrate. You don't need to manage model providers yourself.
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