(05)Technology
Tools we use.
Model providers, frameworks and vector databases, chosen per project for accuracy, cost and privacy.
- OpenAI
Anthropic
LangChain- LlamaIndex
Hugging Face
PyTorch
TensorFlow
FastAPI- Pinecone
- ChromaDB
- AWS Bedrock
n8n
AI & automation
We integrate language models, retrieval and machine learning into real products and workflows, with the testing, guardrails and monitoring that keep them reliable.
(01)Capabilities
Systems that plug into your existing stack and pay back quickly.
Embed GPT, Claude and Gemini into your product: chatbots, copilots, summarization and custom assistants with streaming responses.
Retrieval-augmented generation that answers from your documents, PDFs, databases and wikis, with source citations.
End-to-end automation with n8n, Make and custom Python agents that run around the clock across your tools.
Models trained on your data for classification, regression, anomaly detection and recommendation engines.
Turn unstructured text into insight: sentiment, entities, topics and multi-language processing at scale.
Visual AI for images and video: object detection, OCR, document parsing and automated quality control.
Production-grade AI services with auth, rate limiting, caching and monitoring, ready to plug into any stack.
Forecast demand, churn and pricing with models trained on your history and enriched with market signals.
(02)Use cases
Typical outcomes from AI systems we have shipped.
70% ticket deflection
A GPT assistant trained on your docs, FAQs and ticket history that resolves routine questions automatically.
98% extraction accuracy
Contracts, invoices and reports are extracted, classified and structured in seconds, at 500+ documents a day.
3× conversion rate
An ML model that ranks inbound leads by conversion probability from behavioural and firmographic signals.
180% SEO growth
On-brand SEO content, product descriptions and social copy at scale, with automated quality checks.
35% less overstock
Stock forecasts 30–90 days ahead from sales history, seasonality and market trends.
<0.5% defect escape
Computer vision that inspects products on a production line in real time.
(03)Process
The same framework every time, covering accuracy, cost and latency from the first workshop to production.
We map your workflows, find the highest-ROI opportunities and define success metrics before writing code.
A scalable, cost-aware design with the right models, vector stores and infrastructure for your needs.
Iterative builds with frequent demos, accuracy testing, hallucination checks and edge-case validation.
Production rollout with latency dashboards, cost tracking, alerting and drift monitoring.
(04)Approach
Most AI projects stall after the demo. Ours are engineered for accuracy, security and deep integration.
Senior engineers who have shipped LLM, vision and ML pipelines that hold up under real traffic.
API settings that keep your data out of model training, PIPEDA- and GDPR-minded design and full audit trails. On-premise deploys when needed.
Most integrations go live in 2–4 weeks using proven frameworks and patterns.
Documented, maintainable code with no black boxes. You own all of it.
Guardrails, output validation and confidence scoring in every production system.
Streaming, caching and async processing for responses that feel instant.
(05)Technology
Model providers, frameworks and vector databases, chosen per project for accuracy, cost and privacy.
AnthropicLangChainHugging FacePyTorchTensorFlowFastAPIn8nStart here
Most clients begin with one focused project, live in weeks, then grow from there.
(06)Clients
“Their AI integration work automated 40% of our support tickets and cut our response time from 6 hours to under 10 minutes. Remarkable.”
(07)FAQ
Still have a question?
Talk to a person, not a bot. Message us on WhatsApp at +1 (514) 919-5536 or send a note, and we will reply within one business day.
Most AI integrations, such as workflows, pipelines and agents, go live in 2–4 weeks. Custom ML model training typically takes 4–8 weeks, depending on data availability.
Not for RAG systems or LLM integrations. We use your existing documents and content. Custom ML models need historical data, and we can help with synthetic data where useful.
No. We use provider API settings that keep prompts, documents and outputs out of model training, and zero-data-retention agreements where the provider offers them. Fully on-premise deployments are available.
We build in guardrails, confidence scoring, output validation and human-in-the-loop review at critical points. RAG answers include source citations so users can verify them.
Yes. We ship AI as headless services with REST APIs that connect to Salesforce, HubSpot, Zendesk, Slack, custom databases and almost any system via webhooks or SDKs.
Every AI project includes 3 months of post-deployment support: bug fixes, drift monitoring and performance tuning. Ongoing managed retainers are available.
Next step
Book a free 30-minute discovery call. We will look at your workflows and show you where AI saves time and money, with no commitment.