AI that solves real operational problems — not demos, not experiments. We build integrations that reduce manual work, surface insights, and speed up workflows inside the systems your business already runs on.
The gap between an AI demo and AI that actually works in production is where most projects fail. Getting a language model to answer a question is easy. Getting it to answer the right question, from your data, inside your workflow, reliably — that is the hard part. That is what we build.
We work across the full AI integration stack: cloud-hosted models, local inference, retrieval-augmented generation, and purpose-built pipelines that connect AI capabilities directly into your existing tools and processes.
01
We connect large language models — GPT-4o, Claude, Mistral, LLaMA, and others — to your data, tools, and workflows. That means more than an API call: prompt architecture, context management, graceful failure handling, and the surrounding system that makes the model useful in a real operational environment. Use cases include internal assistants, customer-facing chat, automated content generation, intelligent search, classification, and decision-support tooling built around your domain.
02
A language model only knows what it was trained on. RAG fixes that. We build retrieval that pulls from your documents, databases, and knowledge sources at query time — so answers come from your data, not generic training. We handle ingestion, chunking, embeddings, vector stores, retrieval tuning, and the model layer — whether knowledge lives in PDFs, SharePoint, databases, or Confluence.
03
For sensitive data, compliance, or high volume, running models locally is often the right call — better privacy, no per-token costs, full control over behaviour. We deploy open models (LLaMA 3, Mistral, Phi, Qwen, and others) with Ollama, vLLM, or llama.cpp: hardware sizing, quantization, APIs, and integration so local inference behaves like any other service in your stack.
04
Invoices, POs, contracts, and forms hold data your systems need but cannot read. We build pipelines that ingest, parse, classify, and extract structured data — routing into the right system without manual handling. Built on OCR, layout analysis, and model-based extraction, these pipelines replace manual entry loops in accounting, procurement, logistics, and compliance.
05
We replace repetitive, rules-based, or judgment-light work with reliable automated flows — triggered by events, schedules, or incoming data — with exception handling and integration into your existing tools via API or direct connection. The goal is concrete — hours saved per week, fewer errors, faster response times.
06
Sometimes the right answer is a focused internal tool that uses AI where it genuinely helps — not a generic chatbot. We build narrow, high-value tools: proposal generators that know your pricing, RFQ summarizers, inventory anomaly detectors, meeting summarizers that write to your CRM, and similar workflows built around how your team actually works.
We know Odoo at a deep technical level and we know how to ship production AI. That combination opens integrations most partners cannot deliver — AI inside the modules your team already uses, not an external tool they have to switch to.
Invoices, POs, and delivery documents processed and entered without manual handling.
Surface the right context with approval requests so decisions get made faster.
Summarize threads, classify leads, and populate fields from unstructured communication.
RAG-powered search across internal Odoo documents and attached files.
Anomaly detection and demand signals fed into manufacturing and inventory planning.
Model outputs surfaced as native Odoo UI inside any module.
There is no universal answer between cloud APIs and local inference. The right choice depends on data sensitivity, volume, latency, and budget — we help you decide with a clear view of tradeoffs, and we build confidently on either side.
Many production systems use both — cloud models for general tasks, local models for sensitive or high-volume workloads.
We start with the problem, not the technology. If AI is the right tool, we use it. If simpler automation solves it better, we build that instead — and we explain why. Every engagement begins with scoping: we map the workflow, identify where a model fits, evaluate available data, and define success metrics before development. You pay for a defined deliverable that solves a defined problem — not open-ended experimentation.
Map the workflow, data, constraints, and whether AI is the right lever — or if automation without an LLM is enough.
Retrieval design, model choice, hosting model, integrations, and acceptance criteria locked before build.
Iterative delivery with evaluation against real data, error handling, monitoring hooks, and handoff documentation.
AI work needs close collaboration, fast iteration, and business context — not tickets routed overseas. We are a Toronto-based team in your timezone with direct access to the people building your system from day one.