AI services
AI-Native Product Development
A product designed around the model from day one, with the platform engineering to run it: multi-tenancy, queues, observability and cost control.
At a glance
- From idea to working prototype in weeks
- Agent, API, web app and data pipelines from one team
- Multi-tenant, observable and cost-controlled from the start
- Handover to your team, or ongoing operation
An AI-native product is a product designed around the model from day one: the model is in the critical path, so latency, cost, grounding and failure modes shape it from the first screen. Momentaj, a Toronto AI engineering firm, has built three of them since 2024, and the platforms that run them. An AI-native product is not an existing app with a chat box, so the agent, the app, the data pipelines and the platform are designed together.
Who it's for
- Founders with an AI product idea and a deadline
- Companies productising an internal AI capability for their customers
- Teams whose prototype worked and now needs to become a product
What we build
- The agent or model layer, designed as described under AI agents and RAG.
- The product around it. Web and mobile-first apps in Blazor, SvelteKit or React, in the languages your users speak, with onboarding, accounts, billing and admin.
- The data layer. ETL pipelines, a modelled database, and the integrations the product depends on, with queues between every stage.
- The platform. Multi-tenancy, identity, rate limits, observability with OpenTelemetry, per-tenant cost accounting, and the evaluation harness that keeps quality from drifting as models change.
How it's done
A small senior team, usually the founder plus one or two engineers, working in short cycles against a real user flow. The first milestone is always a working prototype on real data, because that is where an AI product's real risks show up. From there we harden what proved valuable and cut what did not.
From prototype to production
A prototype proves the risky part on real data in two to six weeks. Production turns it into a system your team can run, your auditors can read, and that still works at month six. The gap is rarely the model; it is everything around it.
| Concern | In the prototype | In production |
|---|---|---|
| Grounding | The reply schema and server-side resolution, from the start | Tests that replay chats with bad identifiers |
| Identity and access | Access for the people testing it | Your identity provider, per-user permissions in every tool call, approval gates |
| Evaluation | A small gold set of real requests | The gold set scored before and after every change to prompts or models |
| Observability | Tracing from the first prototype | Dashboards, alerts, budgets per tool call and cost per tenant |
When we take over a prototype, the parts that do not survive production are usually grounding, data handling and observability. MayAI reached a working prototype and stopped at that stage, before real ordering or payment. Stopping after a prototype is a legitimate outcome: every stage ends with something real, so you can stop or change direction after any of them.
Proof
How we built them is written up in Keeping AI agents honest, From public data to market intelligence and RAG for code and structured material.
MayAI: a consumer agent in English and Arabic, from a chat message to restaurants, menus, a cart and checkout, built to a working prototype by an architect and one developer. Valco AI: data pipelines over public property records, forecasting models, conversational recommendations and a dashboard, delivered to the client. DevGuardian AI: a multi-agent platform we built as a product of our own and licensed.
Engagement shape
Discovery, a prototype milestone, then production in fixed-scope increments. We can hand the product to your team or keep operating it. See how an engagement works.
Frequently asked questions
Which stack do you use for AI-native products, and can it run in our own cloud?
.NET and Blazor or SvelteKit on the product side and Python where the models live, with Microsoft Agent Framework, Azure OpenAI and pgvector or Azure AI Search underneath. We pick for your team's ability to maintain it, not for novelty. It can run in your own cloud, and inside your own subscription is usually right: the keys, the data and the logs stay yours. Azure is the most common; AWS and self-hosted models also work.
How fast can we see a working AI product prototype?
A working prototype on real data within two to six weeks of starting, depending on integrations. MayAI went from a chat message to a working order flow in that window.
Can you take over an existing AI prototype?
Yes. We assess it first, keep what holds up, and replace what will not survive production, which is usually grounding, data handling and observability.
How much does AI-native product development cost?
Each stage has its own price, agreed before it starts. Discovery and the prototype are fixed-price; production is scoped and quoted once the prototype has shown what it takes.
Who owns the code and IP in an AI product you build for us?
Usually Momentaj keeps the IP and the client gets a licence to use and change what we built for them; the terms are set in each contract.
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.
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Talk to the people who would build it
Tell us what you are trying to build. We will come back with an honest take and a plan.
Book an AI consultation