AI Development Company in Toronto — Intelligent Systems Built for Production
We build AI-powered platforms, retrieval systems, autonomous agents, and document processing pipelines that are wired into your business logic — not bolted on as a chatbot wrapper. Based in Ontario, serving clients across North America.
AI that does real work — not demos
Every AI engagement starts with one question: what decision does this system need to make, and what data does it need to make it well? We build intelligent systems that answer that question in production.
RAG Systems & Knowledge Retrieval
Retrieval-augmented generation over your proprietary documents, products, and history. AI that answers from your data, not from generic training.
AI Agents & Workflow Automation
Autonomous agents that handle multi-step business workflows without human intervention. Built to your process logic, not a generic template.
Document Processing & Intelligent Extraction
Automated ingestion, classification, and structured data extraction from PDFs, contracts, invoices, and unstructured documents at scale.
Geospatial & Spatial Intelligence
Real-time machine learning over geographic data. We built CrimeLens — AI neighbourhood intelligence over Canadian crime data — as proof of concept in production.
Predictive Analytics & Machine Learning
Custom ML models for forecasting, risk scoring, classification, and anomaly detection. Trained on your data, deployed in your infrastructure.
LLM Integration & Custom AI Layers
Connecting OpenAI, Anthropic, and open-source models to your existing systems, APIs, and databases — with proper prompt engineering and evaluation.
CrimeLens: Real-time AI over geospatial crime data
We built CrimeLens — an AI-powered neighbourhood intelligence platform for Canadian real estate. Real-time ingestion from public crime feeds, geospatial indexing with PostGIS, and an ML risk scoring model operating at the block level across 12 Canadian cities.
Four steps. No surprises.
Discover
Map your business problem before writing code. Workflows, gaps, and real leverage points.
Architect
System design and technology selection. You approve the plan before we build.
Build
Iterative delivery with continuous progress. You see real output every week.
Scale
Systems designed to grow. Ongoing retainers as your business evolves.
The technology behind our AI systems
Which AI problem are you solving?
Building an AI-native product from scratch
We help you choose the right architecture — RAG vs fine-tuning, agents vs workflows — before you commit to a path that costs you six months to undo.
Adding intelligent automation to existing operations
We build AI layers over your existing systems. Document processing, automated decisions, knowledge retrieval — without replacing what already works.
AI over geospatial or structured datasets
We have direct experience building production AI over real-world data at geographic scale. CrimeLens is the working example.
Questions about ai development
Is this just ChatGPT wrapped in a different UI?
No. We build AI systems wired into your data, APIs, and business logic. That means custom retrieval pipelines, fine-tuned prompting, proper evaluation frameworks, and production infrastructure — not a chat widget bolted to a website.
What is RAG and why does it matter for my business?
Retrieval-Augmented Generation lets an AI system answer questions from your own documents and data rather than its generic training. This means your AI knows about your products, policies, and history — and doesn't hallucinate facts that aren't there.
How long does an AI development project take?
A focused RAG or document AI engagement typically takes 6–12 weeks from discovery to production. More complex agent systems or ML model training can take 3–6 months. We scope before we start, so you know what you're committing to.
Do you build AI agents?
Yes. We build autonomous agents that handle multi-step workflows: research, data retrieval, decision-making, and action — without requiring human input at each step. We use LangChain and custom orchestration depending on the complexity.
What happens when the AI model I'm using gets updated or deprecated?
We design systems with model abstraction layers, so switching from one LLM to another doesn't require rebuilding everything. We've seen the market shift quickly and we build for that reality.
Can you integrate AI into our existing software?
That's often the most valuable engagement. We can add AI retrieval, document processing, or automation layers to your existing platform — without rebuilding what already works.