Service
AI-Powered Applications
We build production-grade AI systems — from LLM integrations and RAG pipelines to custom-trained ML models. Responsible, explainable, and built to run reliably at scale on Canadian infrastructure.
What's included
LLM Integration
Connect your platform to GPT-4o, Claude, Mistral, or self-hosted models. We handle prompt engineering, context windows, memory, and safety guardrails.
Custom ML Models
We train classification, regression, recommendation, and anomaly detection models on your own data using PyTorch, TensorFlow, or scikit-learn.
RAG Pipelines
AI that searches your documents, policies, or knowledge base and answers accurately with source citations — built using vector databases and semantic search.
NLP & Text Analysis
Entity extraction, sentiment analysis, document summarization, and intent classification — tuned to your specific domain and language.
AI Automation Agents
Autonomous agents that complete multi-step workflows, call external APIs, and escalate to humans when uncertain. Built with LangChain, CrewAI, or custom frameworks.
Observability & Monitoring
Real-time dashboards for model performance, latency, token usage, error rates, and data drift — so you always know how your AI is performing.
How we work
Discovery & Data Audit
We start by understanding your problem, your existing data, and your constraints. We assess what AI can realistically solve — and what it cannot. This stage prevents building the wrong thing.
Model Selection & Architecture
We choose the right approach: fine-tuned LLM, RAG pipeline, custom ML model, or a hybrid. Architecture decisions are documented and explained — no black boxes.
Data Preparation & Training
We clean, label, and structure your data. For custom models we handle the full training pipeline. For LLM integrations we build the prompting strategy, context windows, and memory systems.
Integration & API Layer
We connect the AI system to your existing platform via secure, versioned APIs. Every integration is load-tested and documented for your engineering team.
Evaluation & Red-Teaming
We rigorously test accuracy, edge cases, hallucination rates, and safety. Adversarial prompting and structured evaluation benchmarks are run before any deployment.
Deployment & Monitoring
We deploy to Canadian cloud infrastructure and set up observability dashboards tracking model performance, latency, token usage, error rates, and data drift in real time.
Technical stack
Models
- GPT-4o
- Claude 3.5
- Mistral / Mixtral
- Llama 3
- Custom fine-tuned
Frameworks
- LangChain
- LlamaIndex
- CrewAI
- PyTorch
- scikit-learn
Vector DBs
- Pinecone
- Weaviate
- pgvector
- Chroma
- Qdrant
Infrastructure
- AWS ca-central-1
- Docker / K8s
- API Gateway
- CloudWatch
- GitHub Actions
Frequently asked questions
Do you work with OpenAI, Anthropic, or open-source models?
Yes — we work across the full landscape. We use GPT-4o, Claude, Mistral, Llama 3, and others depending on your requirements around cost, privacy, and capability. For data-sensitive applications we prefer self-hosted or Canadian-hosted models.
What is RAG and do I need it?
Retrieval-Augmented Generation allows an AI to search your own knowledge base before answering — rather than relying solely on its training data. If you want an AI that accurately answers questions about your documents, policies, or products, you almost certainly need RAG.
Will the AI hallucinate or give wrong answers?
All current AI systems can produce incorrect outputs. We use retrieval grounding, structured output validation, and confidence thresholds to minimize this risk. We are transparent about accuracy rates and recommend human-in-the-loop review for high-stakes decisions.
Is my data used to train public models?
No. Your data stays on Canadian infrastructure and is never shared with third-party model providers for training purposes. We sign data processing agreements and clearly document every data flow.
How long does an AI project take?
A focused LLM integration can be delivered in 4–8 weeks. A custom ML model with full data pipeline typically takes 10–20 weeks depending on data availability and complexity. We scope clearly before starting.
What happens if the AI underperforms after launch?
We include a post-launch monitoring period in every engagement. If model performance drops below agreed thresholds, we investigate and retrain at no additional cost within the warranty period.
Ready to build with AI?
Let's talk about your use case. We'll be honest about what AI can and cannot do for you — and build something that actually works.
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