Best AI Development Platforms in Canada - Page 11
View:
Compare the Top AI Development Platforms in Canada as of February 2026 - Page 11
Sort By:
-
1
Open Agent Studio
Cheat Layer
Open Agent Studio is not just another co-pilot it's a no-code co-pilot builder that enables solutions that are impossible in all other RPA tools today. We believe these other tools will copy this idea, so our customers have a head start over the next few months to target markets previously untouched by AI with their deep industry insight. Subscribers have access to a free 4-week course, which teaches how to evaluate product ideas and launch a custom agent with an enterprise-grade white label. Easily build agents by simply recording your keyboard and mouse actions, including scraping data and detecting the start node. The agent recorder makes it as easy as possible to build generalized agents as quickly as you can teach how to do it. Record once, then share across your organization to scale up future-proof agents. -
2
Anon
Anon
Anon offers two powerful ways to integrate your applications with services that lack APIs, enabling you to build innovative solutions and automate workflows like never before. The API packages pre-built automation on popular services that don’t offer APIs and are the simplest way to use Anon. The toolkit to build user-permissions integrations for sites without APIs. Using Anon, developers can enable agents to authenticate and take actions on behalf of users across the most popular sites on the internet. Programmatically interact with the most popular messaging services. The runtime SDK is an authentication toolkit that lets AI agent developers build their own integrations on popular services that don’t offer APIs. Anon simplifies the work of building and maintaining user-permission integrations across platforms, languages, auth types, and services. We build the annoying infra so you can build amazing apps. -
3
StartKit.AI
Squarecat.OÜ
StartKit.AI is a boilerplate designed to speed up the development of AI projects. It offers pre-built REST API routes for all common AI tasks: chat, images, long-form text, speech-to-text, text-to-speech, translations, and moderation. As well as more complex integrations, such as RAG, web-crawling, vector embeddings, and much more! It also comes with user management and API limit management features, along with fully detailed documentation covering all the provided code. Upon purchase, customers receive access to the complete StartKit.AI GitHub repository where they can download, customize, and receive updates on the full code base. 6 demo apps are included in the code base, providing examples on how to create your own ChatGPT clone, PDF analysis tool, blog-post creator, and more. The ideal starting off point for building your own app!Starting Price: $199 -
4
Context Data
Context Data
Context Data is an enterprise data infrastructure built to accelerate the development of data pipelines for Generative AI applications. The platform automates the process of setting up internal data processing and transformation flows using an easy-to-use connectivity framework where developers and enterprises can quickly connect to all of their internal data sources, embedding models and vector database targets without having to set up expensive infrastructure or engineers. The platform also allows developers to schedule recurring data flows for refreshed and up-to-date data.Starting Price: $99 per month -
5
Redactive
Redactive
Redactive's developer platform removes the specialist data engineering knowledge that developers need to learn, implement, and maintain to build scalable & secure AI-enhanced applications for your customers or productivity use cases for your employees. Built with enterprise security needs in mind so you can focus on getting to production quickly. Don't rebuild your permission models just because you're starting to implement AI in your business. Redactive always respects access controls set by the data source & our data pipeline can be configured to never store your end documents, reducing your risk on downstream technology vendors. Redactive has you covered with pre-built data connectors & reusable authentication flows to connect with an ever-growing list of tools, along with custom connectors and LDAP/IdP provider integrations so you can power your AI use cases no matter your architecture. -
6
Dynamiq
Dynamiq
Dynamiq is a platform built for engineers and data scientists to build, deploy, test, monitor and fine-tune Large Language Models for any use case the enterprise wants to tackle. Key features: 🛠️ Workflows: Build GenAI workflows in a low-code interface to automate tasks at scale 🧠 Knowledge & RAG: Create custom RAG knowledge bases and deploy vector DBs in minutes 🤖 Agents Ops: Create custom LLM agents to solve complex task and connect them to your internal APIs 📈 Observability: Log all interactions, use large-scale LLM quality evaluations 🦺 Guardrails: Precise and reliable LLM outputs with pre-built validators, detection of sensitive content, and data leak prevention 📻 Fine-tuning: Fine-tune proprietary LLM models to make them your ownStarting Price: $125/month -
7
Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
8
Byne
Byne
Retrieval-augmented generation, agents, and more start building in the cloud and deploying on your server. We charge a flat fee per request. There are two types of requests: document indexation and generation. Document indexation is the addition of a document to your knowledge base. Document indexation, which is the addition of a document to your knowledge base and generation, which creates LLM writing based on your knowledge base RAG. Build a RAG workflow by deploying off-the-shelf components and prototype a system that works for your case. We support many auxiliary features, including reverse tracing of output to documents, and ingestion for many file formats. Enable the LLM to use tools by leveraging Agents. An Agent-powered system can decide which data it needs and search for it. Our implementation of agents provides a simple hosting for execution layers and pre-build agents for many use cases.Starting Price: 2¢ per generation request -
9
PromptQL
Hasura
PromptQL is an enterprise-grade AI platform that builds reasoning models with near-perfect accuracy, tailored to each organization’s unique context. Unlike generic AI tools, PromptQL learns your business rules, tacit knowledge, and internal language to act like a trusted analyst or engineer. It empowers companies to deploy specialized AI that not only delivers correct answers but also signals confidence levels and learns continuously from feedback. Within 14 days, enterprises can go from setup to real-world rollout, unlocking measurable results faster than traditional AI deployments. Used by Fortune 100 companies and global enterprises, PromptQL consistently outperforms warehouse-native AI solutions in accuracy and reliability. Designed for adoption, not obsolescence, PromptQL enables organizations to build AI that truly understands their business. -
10
Distyl
Distyl
Distyl builds AI Systems that the F500 trusts to reliably power and automate their core operations. We deploy production-ready software in months. Distyl’s AI Native methodology puts AI into every facet of your operations. We rapidly generate, refine, and deploy scalable solutions that transform your business processes. AI creates automated processes with human feedback. This accelerates time-to-value from months to days. Our AI systems are customized to your organization’s business context and SME expertise, providing understandable transparency and actionable insights, explainable AI, and no black box. Our world-class team of engineers and researchers are forward-deployed to own the outcome alongside you. Our AI uses organizational assets and SME business context to automatically create AI native workflows called “routines”. SMEs can iterate on and evolve the routines, with all changes versioned, reviewable, and end-to-end deployment tested to ensure system reliability. -
11
PartyRock
Amazon
PartyRock is a space where you can build AI-generated apps in a playground powered by Amazon Bedrock. It’s a fast and fun way to learn about generative AI. PartyRock, launched by Amazon Web Services (AWS) in November 2023, is a user-friendly platform that enables users to create generative AI-powered applications without any coding experience. By simply describing the desired app, users can build a variety of applications, from simple text generators to sophisticated productivity tools that combine multiple AI capabilities. Since its inception, over half a million apps have been built by users worldwide. artyRock operates as a playground powered by Amazon Bedrock, AWS's fully managed service that provides access to foundational AI models. The platform offers a web-based interface, eliminating the need for an AWS account, and allows users to sign in with their existing social credentials. Users can explore hundreds of thousands of published apps, categorized by functionality. -
12
Prompt flow
Microsoft
Prompt Flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
13
Bria.ai
Bria.ai
Bria.ai is a powerful generative AI platform that specializes in creating and editing images at scale. It provides developers and enterprises with flexible solutions for AI-driven image generation, editing, and customization. Bria.ai offers APIs, iFrames, and pre-built models that allow users to integrate image creation and editing capabilities into their applications. The platform is designed for businesses seeking to enhance their branding, create marketing content, or automate product shot editing. With fully licensed data and customizable tools, Bria.ai ensures businesses can develop scalable, copyright-safe AI solutions. -
14
Intel Open Edge Platform
Intel
The Intel Open Edge Platform simplifies the development, deployment, and scaling of AI and edge computing solutions on standard hardware with cloud-like efficiency. It provides a curated set of components and workflows that accelerate AI model creation, optimization, and application development. From vision models to generative AI and large language models (LLM), the platform offers tools to streamline model training and inference. By integrating Intel’s OpenVINO toolkit, it ensures enhanced performance on Intel CPUs, GPUs, and VPUs, allowing organizations to bring AI applications to the edge with ease. -
15
Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. Built on Amazon DataZone, it integrates various AWS analytics and AI/ML services, such as Amazon EMR, AWS Glue, and Amazon Bedrock, into a single platform. Users can discover, access, and process data from various sources like Amazon S3 and Redshift, and develop generative AI applications. With tools for model development, governance, MLOps, and AI customization, SageMaker Unified Studio provides an efficient, secure, and collaborative environment for data teams.
-
16
Amazon Bedrock Guardrails
Amazon
Amazon Bedrock Guardrails is a configurable safeguard system designed to enhance the safety and compliance of generative AI applications built on Amazon Bedrock. It enables developers to implement customized safety, privacy, and truthfulness controls across various foundation models, including those hosted within Amazon Bedrock, fine-tuned models, and self-hosted models. Guardrails provide a consistent approach to enforcing responsible AI policies by evaluating both user inputs and model responses based on defined policies. These policies include content filters for harmful text and image content, denial of specific topics, word filters for undesirable terms, sensitive information filters to redact personally identifiable information, and contextual grounding checks to detect and filter hallucinations in model responses. -
17
NVIDIA NeMo Guardrails
NVIDIA
NVIDIA NeMo Guardrails is an open-source toolkit designed to enhance the safety, security, and compliance of large language model-based conversational applications. It enables developers to define, orchestrate, and enforce multiple AI guardrails, ensuring that generative AI interactions remain accurate, appropriate, and on-topic. The toolkit leverages Colang, a specialized language for designing flexible dialogue flows, and integrates seamlessly with popular AI development frameworks like LangChain and LlamaIndex. NeMo Guardrails offers features such as content safety, topic control, personal identifiable information detection, retrieval-augmented generation enforcement, and jailbreak prevention. Additionally, the recently introduced NeMo Guardrails microservice simplifies rail orchestration with API-based interaction and tools for enhanced guardrail management and maintenance. -
18
Llama Guard
Meta
Llama Guard is an open-source safeguard model developed by Meta AI to enhance the safety of large language models in human-AI conversations. It functions as an input-output filter, classifying both prompts and responses into safety risk categories, including toxicity, hate speech, and hallucinations. Trained on a curated dataset, Llama Guard achieves performance on par with or exceeding existing moderation tools like OpenAI's Moderation API and ToxicChat. Its instruction-tuned architecture allows for customization, enabling developers to adapt its taxonomy and output formats to specific use cases. Llama Guard is part of Meta's broader "Purple Llama" initiative, which combines offensive and defensive security strategies to responsibly deploy generative AI models. The model weights are publicly available, encouraging further research and adaptation to meet evolving AI safety needs. -
19
Foundry Local
Microsoft
Foundry Local is a local version of Azure AI Foundry that enables local execution of large language models (LLMs) directly on your Windows device. This on-device AI inference solution provides privacy, customization, and cost benefits compared to cloud-based alternatives. Best of all, it fits into your existing workflows and applications with an easy-to-use CLI and REST API. -
20
Knapsack
Knapsack
Knapsack is a digital production platform that connects design and code into a real-time system of record, enabling enterprise teams to build, govern, and deliver digital products at scale. It offers dynamic documentation that automatically updates when code changes occur, ensuring that documentation remains current and reducing maintenance overhead. Knapsack's design tokens and theming capabilities allow for the connection of brand decisions to style implementation in product UIs, ensuring a cohesive brand experience across portfolios. Knapsack's component and pattern management provides a birds-eye view of components across design, code, and documentation, ensuring consistency and alignment as systems scale. Its prototyping and composition features enable teams to use production-ready components to prototype and share UIs, allowing for exploration, validation, and testing with code that ships. Knapsack also offers permissions and controls to meet the complex workflow. -
21
Atla
Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more. -
22
The Oracle AI Data Platform unifies the complete data-to-insight lifecycle with embedded artificial intelligence, machine learning, and generative capabilities across data stores, analytics, applications, and infrastructure. It supports everything from data ingestion and governance through to feature engineering, model training, and operationalization, enabling organizations to build trusted AI-driven systems at scale. With its integrated architecture, the platform offers native support for vector search, retrieval-augmented generation, and large language models, while enabling secure, auditable access to business data and analytics across enterprise roles. The platform’s analytics layer lets users explore, visualize, and interpret data with AI-powered assistance, where self-service dashboards, natural-language queries, and generative summaries accelerate decision making.
-
23
Oracle Generative AI Service
Oracle
Generative AI Service Cloud Infrastructure is a fully managed platform offering powerful large language models for tasks such as generation, summarization, analysis, chat, embedding, and reranking. You can access pretrained foundational models via an intuitive playground, API, or CLI, or fine-tune custom models on your own data using dedicated AI clusters isolated to your tenancy. The service includes content moderation, model controls, dedicated infrastructure, and flexible deployment endpoints. Use cases span industries and workflows; generating text for marketing or sales, building conversational agents, extracting structured data from documents, classification, semantic search, code generation, and much more. The architecture supports “text in, text out” workflows with rich formatting, and spans regions globally under Oracle’s governance- and data-sovereignty-ready cloud. -
24
TABS
TABS
TabStack is a web-data API designed to empower AI agents and automation workflows to interact with the live web; it enables users to extract structured content from any URL (HTML, Markdown, JSON), transform raw web pages into usable formats (for example converting product listings into comparison tables or blog posts into social-ready snippets), perform complex browser-style automations (clicking, scrolling, submitting forms) and run deep research queries that surface insights and summaries across hundreds of sources. It is built for production-scale reliability and low latency, optimizing fetches by parsing only what’s necessary and escalating to full-page rendering only when needed, and features built-in resilience (automatic retries, adaptation to flaky HTML) to ensure robustness in real-world web environments. -
25
Amazon SageMaker HyperPod
Amazon
Amazon SageMaker HyperPod is a purpose-built, resilient compute infrastructure that simplifies and accelerates the development of large AI and machine-learning models by handling distributed training, fine-tuning, and inference across clusters with hundreds or thousands of accelerators, including GPUs and AWS Trainium chips. It removes the heavy lifting involved in building and managing ML infrastructure by providing persistent clusters that automatically detect and repair hardware failures, automatically resume workloads, and optimize checkpointing to minimize interruption risk, enabling months-long training jobs without disruption. HyperPod offers centralized resource governance; administrators can set priorities, quotas, and task-preemption rules so compute resources are allocated efficiently among tasks and teams, maximizing utilization and reducing idle time. It also supports “recipes” and pre-configured settings to quickly fine-tune or customize foundation models. -
26
NexaSDK
NexaSDK
Nexa SDK is a unified developer toolkit that lets you run and ship any AI model locally on virtually any device with support for NPUs, GPUs, and CPUs, offering seamless deployment without needing cloud connectivity; it provides a fast command-line interface, Python bindings, mobile (Android and iOS) SDKs, and Linux support so you can integrate AI into apps, IoT devices, automotive systems, and desktops with minimal setup and one line of code to run models, while also exposing an OpenAI-compatible REST API and function calling for easy integration with existing clients. Powered by the company’s custom NexaML inference engine built from the kernel up for optimal performance on every hardware stack, the SDK supports multiple model formats including GGUF, MLX, and Nexa’s proprietary format, delivers full multimodal support for text, image, and audio tasks (including embeddings, reranking, speech recognition, and text-to-speech), and prioritizes Day-0 support for the latest architectures. -
27
Universal Commerce Protocol (UCP)
Universal Commerce Protocol (UCP)
The UCP and AP2 documentation describes how the Universal Commerce Protocol (UCP) integrates with the Agent Payments Protocol (AP2) to support secure, verifiable transactions initiated by AI agents or platforms on behalf of users, making it possible for commerce systems to handle discovery, checkout, and payment without intermediaries. UCP is fully compatible with AP2, which acts as the trust layer for agent-led transactions by requiring a secure, cryptographically verifiable exchange of intent and authorization between platforms and businesses using Verifiable Digital Credentials (VDCs); this ensures businesses receive signed checkout commitments that can’t be altered mid-flow and platforms issue proofs of payment authorization tied specifically to a cart state, reducing fraud and making transactions final and authentic. -
28
HyperFlow AI
HyperFlow AI
HyperFlow AI is a unified generative AI development platform that lets users design, build, test, scale, and deploy AI-powered applications and workflows with minimal coding by transforming domain expertise into powerful AI solutions via intuitive interfaces and visual tools; it supports prompt crafting for large language models and offers a no-code/low-code environment so teams can create custom AI apps and services quickly and iteratively. It emphasizes accessibility and democratizing AI creation, enabling users to develop advanced AI applications without traditional software engineering barriers while retaining control over their models and outputs. It provides a visual, drag-and-drop workflow design environment where users can configure and automate AI-driven processes, integrate data and external systems, and manage deployments from development through production. -
29
Daria
XBrain
Daria’s advanced automated features allow users to quickly and easily build predictive models, significantly cutting back on days and weeks of iterative work associated with the traditional machine learning process. Remove financial and technological barriers to build AI systems from scratch for enterprises. Streamline and expedite workflows by lifting weeks of iterative work through automated machine learning for data experts. Get hands-on experience in machine learning with an intuitive GUI for data science beginners. Daria provides various data transformation functions to conveniently construct multiple feature sets. Daria automatically explores through millions of possible combinations of algorithms, modeling techniques and hyperparameters to select the best predictive model. Predictive models built with Daria can be deployed straight to production with a single line of code via Daria’s RESTful API. -
30
Snorkel AI
Snorkel AI
AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.
