# AI Strategy & Readiness URL: https://momentaj.com/services/ai-strategy/ Updated: 2026-10-09 Category: AI services Provider: Momentaj Inc., Toronto AI engineering firm; remote across Canada, the United States and the Middle East, in person across the Greater Toronto Area A short, hands-on assessment of where AI pays off in your operation, what data and guardrails you need, and a build plan with honest estimates. At a glance: - One to two weeks, fixed price - Opportunity map ranked by value, risk and data readiness - Architecture options with monthly running costs - A prototype scope you could hand to any team An [AI readiness assessment](https://momentaj.com/glossary/#ai-readiness-assessment) checks whether the workflow, data, rules and people around a process are ready for AI before anyone builds. Momentaj, a Toronto AI engineering firm, runs an AI strategy and readiness assessment: AI consulting from the engineers who would build the system. Most AI projects don't fail on the model. They fail on the data that wasn't ready, the workflow nobody mapped, or the [guardrail](https://momentaj.com/glossary/#guardrails) nobody specified until the demo went wrong. Our assessment exists to find those problems before you spend on a build. There is no separate advisory team, and everything in the report is something we would be willing to implement. ## Who it's for - Companies with a real operation (sales, support, operations, compliance, engineering) and a hunch that AI could take work off it - Product teams deciding whether to add an AI assistant, an agent or an MCP server to what they already sell - Leaders who have seen a prototype and need to know what it would take to run it safely in production ## What you get A one-to-two-week engagement with five deliverables: 1. **Opportunity map.** Your workflows, ranked by how much an agent or model could take on, how costly a wrong answer is, and what data each one needs. 2. **Data and guardrail requirements.** What has to be true of your data, access control and review process before an AI system can act on it, including privacy (PIPEDA) and audit considerations. 3. **Architecture options.** Two or three ways to build the first system, with the trade-offs spelled out: Azure OpenAI or another provider, agent framework, retrieval design, hosting, and the monthly running cost of each. 4. **A prioritised build plan.** What to build first, what to defer, and what not to build at all, with estimates we will stand behind. 5. **A prototype scope.** A two-to-six-week prototype on your real data, specified closely enough that you could hand it to any team. See [how an engagement works](https://momentaj.com/how-we-work/). ## How it's done - **Week 1.** Interviews with the people doing the work, a review of the systems and data involved, and a look at anything you have already tried. - **Week 2.** We draft the architecture options, test the riskiest assumption (usually retrieval quality or data access) with a small experiment, and write the plan. - **Readout.** A working session with your team, then the written report. ## What we bring - Hands-on AI engineering: [agents](https://momentaj.com/services/ai-agents/), [MCP servers](https://momentaj.com/services/mcp-development/), [retrieval systems](https://momentaj.com/services/rag-knowledge-systems/) and [applied machine learning](https://momentaj.com/services/applied-machine-learning/), on Microsoft Agent Framework, LangChain, Azure OpenAI and OpenAI-compatible models - Our founder's twenty years of production systems where downtime and data loss were not acceptable: banking, federal defence R&D, online travel, lending - Current research: our founder publishes comparisons of MCP tooling and AI host requirements, so the plan reflects how the ecosystem works today, not last year - The questions we ask, written up as [an AI readiness checklist for Canadian businesses](https://momentaj.com/insights/ai-readiness-checklist-for-canadian-businesses/) that you can run yourself, with more in our [notes on AI strategy](https://momentaj.com/insights/tag/ai-strategy/) See how this plays out in practice in [MayAI](https://momentaj.com/case-studies/mayai/), a consumer agent taken from a chat message to a working prototype, and in our other [case studies](https://momentaj.com/case-studies/). ## Frequently asked questions **Do we need an AI readiness assessment, or can we just start building?** If you already know the workflow, have the data in one place, and have someone who can say what a wrong answer costs, start with a prototype. If any of those is unclear, the assessment is cheaper than finding out mid-build. **Does an AI strategy assessment only recommend what you would build?** We recommend what fits. Sometimes that is an off-the-shelf product, or nothing yet. When a build is the answer we can do it, but the report is yours either way. **Which AI models and clouds do you work with?** Azure OpenAI and Azure AI Foundry most often, because many of our clients already run on Microsoft. Also OpenAI, Anthropic Claude and open-weight models through OpenAI-compatible APIs, on AWS or your own infrastructure. **How much does an AI readiness assessment cost?** A fixed price, agreed before we start, based on the number of workflows and systems in scope. Ask us for a quote. **How long does an AI readiness assessment take?** One to two weeks. It ends with an opportunity map, the data and guardrail requirements, two or three architecture options with their running costs, a prioritised build plan, and a prototype scope you could hand to any team. **Do you work with companies outside Toronto?** Yes. We work remotely with clients across Canada, the United States, the Middle East and beyond, and in person across the Greater Toronto Area.