AI & Machine Learning
We integrate AI where it actually makes a difference — not as a buzzword, but as a feature that saves your users time or surfaces insights they couldn't get otherwise. From LLM-powered features to custom ML pipelines, we build AI that works in production.
About this service
Everybody is adding AI to their product right now. Very few of them are doing it well. The difference between AI that delights users and AI that embarrasses you in demos comes down to two things: choosing the right technique for the problem, and engineering it carefully enough that it actually works at production load.
We've been integrating machine learning into production software since before the current wave of LLM hype. We know when to reach for a fine-tuned model versus a RAG system versus a simple classifier, and we know how to build the evaluation infrastructure that tells you whether your AI is actually working. We work with OpenAI, Anthropic Claude, open-source models (Llama, Mistral), and purpose-built ML pipelines depending on what the problem calls for.
Our AI work ranges from LLM-powered candidate matching in recruitment platforms, to intelligent job classification in data pipelines, to document processing automation for enterprise workflows. We build AI that runs reliably in production — not just in a notebook.
Common use cases
- →LLM-powered search
- →Document processing
- →Intelligent matching
- →Recommendation engines
- →Automated classification
- →Chatbots and assistants
- →AI-powered workflows
What we deliver
LLM integration
GPT-4, Claude, Mistral, and open-source model integration into your existing product with proper prompt engineering and evaluation.
RAG systems
Retrieval-augmented generation pipelines that let your AI answer questions using your own data, not just training data.
AI-powered automation
Intelligent workflows that reduce manual work — document processing, classification, routing, and data extraction.
ML pipelines
Data ingestion, feature engineering, model training, evaluation, and inference infrastructure built for production.
Vector search
Semantic search using embeddings — find similar content, not just exact keyword matches.
AI evaluation & observability
Evals, monitoring, and feedback loops so you know your AI is working — and catch regressions before users do.
How we work
01
Problem framing
We start by defining what success looks like — not in AI terms, but in product terms. What does a user need to be able to do that they can't do today? That frames every technical decision that follows.
02
Technique selection
We choose the right approach for the problem: LLM prompting, RAG, fine-tuning, a classical ML model, or a combination. We don't default to the most complex solution.
03
Build & evaluate
We build the pipeline and evaluation infrastructure in parallel — so we have a rigorous way to measure whether what we built actually works before shipping it to users.
04
Production & monitoring
We deploy with proper observability — latency tracking, cost monitoring, output sampling, and feedback collection — so the AI keeps working as usage grows.
Tech stack
Innovibe is a Vancouver-based AI development team helping BC companies build LLM-powered features, intelligent automation, and machine learning pipelines into their products. We work with companies in Vancouver, Surrey, Burnaby, Delta, Langley, and across British Columbia — and we bring the same engineering rigour to AI features that we bring to everything else we build.
Frequently asked questions
Do we need a lot of data to build an AI feature?+
Not necessarily. LLM-based approaches (RAG, prompt engineering) work well with limited proprietary data and can deliver value fast. If you need a custom model trained on your data, we'll tell you what you realistically need and whether the investment makes sense.
Can you help us add AI to an existing product?+
Yes — most of our AI work is adding LLM-powered features to existing web or mobile products. We integrate with your current API and data sources rather than requiring a rebuild.
How do you make sure the AI actually works before shipping?+
We build evaluation infrastructure alongside the AI feature itself — a test set of inputs and expected outputs, automated evals that run in CI, and human review processes for edge cases. We treat AI like any other production system: it needs tests.
Are you familiar with Canadian AI regulations and privacy requirements?+
Yes. We design AI systems with PIPEDA and BC PIPA compliance in mind, including data residency requirements, user consent for AI processing, and appropriate data retention policies.
Can you build AI features for Vancouver or BC-based clients quickly?+
Yes. Being BC-based means we're in your timezone, available for quick calls, and can come on-site if needed. We've shipped AI features for BC clients in as few as 4–6 weeks for well-scoped problems.
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Need ai & machine learning?
Tell us what you're building. We'll tell you if we can help — and we're always honest if we're not the right fit.
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