Ship AI features. Faster than your competitors.
Vancouver's tech ecosystem is maturing fast — with a dense cluster of SaaS companies, AI-native startups, and scale-ups competing for the same enterprise contracts. The companies that win are the ones that embed AI deeply into their product, not as a demo feature but as a core capability that drives retention and expansion revenue. We help you get there.
AI feature integration timeline
Less hallucinations with RAG
Better 30-day activation rates
Cost reduction with right model
What We Build
AI capabilities for product and operations
AI Feature Integration
Embed LLM-powered features directly into your product — intelligent search, AI writing assistants, automated summaries, smart recommendations, and contextual Q&A — using GPT-4o, Claude, or open-source models depending on your cost and data requirements.
typical AI feature integration timeline
RAG & Knowledge Systems
Retrieval-Augmented Generation systems that let your product answer questions from proprietary data — docs, wikis, support tickets, codebases. Pinecone or Weaviate vector stores, semantic search, and citation-grounded answers that don't hallucinate.
reduction in irrelevant AI responses with RAG
AI-Powered Onboarding & Activation
Personalized onboarding flows that adapt to user behaviour — AI that detects where users get stuck, surfaces relevant help content, and triggers the right prompts at the right moment to improve time-to-value and reduce early churn.
improvement in 30-day user activation rates
Internal Operations Automation
AI that handles the ops overhead of a growing startup — automated support ticket triage, AI-generated release notes from commit history, sales email personalization at scale, and automated reporting from your data warehouse.
ops time saved per week for a 15-person team
AI Evaluation & Model Selection
Technical consulting on which models and architectures are right for your product — evaluating GPT-4o vs Claude vs Gemini vs open-source on your specific tasks, building evaluation harnesses, and setting up evals-driven development.
typical cost reduction vs default model selection
Data Pipeline & MLOps
Production-grade data pipelines that feed your AI systems — ETL from your application database, feature stores, model serving infrastructure, and monitoring dashboards that alert you when model performance drifts.
uptime SLA for production AI systems we operate
The stack we actually use
We are not wedded to any vendor. We evaluate the right model and infrastructure for your specific use case — optimizing for latency, cost, accuracy, and your existing cloud environment.
OpenAI GPT-4o / o3
LLM
Anthropic Claude 3.5
LLM
Llama 3.1 / Mistral
Open Source
LangChain / LlamaIndex
Frameworks
Pinecone / Weaviate
Vector DB
AWS Bedrock
Cloud AI
Vercel AI SDK
Frontend
PostgreSQL pgvector
Vector DB
Not sure which model to use? We run structured evaluations on your actual task data — not benchmarks — to find the model that performs best at your price point. Most startups are significantly overpaying for frontier models on tasks where a smaller, cheaper model performs equivalently.
Worried about vendor lock-in? We architect AI systems with model-agnostic interfaces so you can swap providers without rewriting your application. We have helped teams migrate off OpenAI to significantly reduce costs with no product impact.
Explore Related Industries
Marketing Agencies AI
AI-powered campaign tools, content generation, analytics automation.
E-Commerce AI
Recommendation engines, demand forecasting, retention AI for DTC.
AI Consulting
Custom AI strategy and implementation roadmap for your startup.
Enterprise AI
Scale your AI infrastructure as you grow — production-grade systems.
Ready to ship your next AI feature?
Book a free 30-minute technical strategy session. We will review your current architecture, identify the highest-value AI integration opportunities, and give you a realistic roadmap — not a sales pitch.
Book Your Free Strategy Call