StackAI
StackAI is an enterprise AI automation platform built to help organizations create end-to-end internal tools and processes with AI agents. Unlike point solutions or one-off chatbots, StackAI provides a single platform where enterprises can design, deploy, and govern AI workflows in a secure, compliant, and fully controlled environment.
Using its visual workflow builder, teams can map entire processes — from data intake and enrichment to decision-making, reporting, and audit trails. Enterprise knowledge bases such as SharePoint, Confluence, Notion, Google Drive, and internal databases can be connected directly, with features for version control, citations, and permissioning to keep information reliable and protected.
AI agents can be deployed in multiple ways: as a chat assistant embedded in daily workflows, an advanced form for structured document-heavy tasks, or an API endpoint connected into existing tools. StackAI integrates natively with Slack, Teams, Salesforce, HubSpot, ServiceNow, Airtable, and more.
Security and compliance are embedded at every layer. The platform supports SSO (Okta, Azure AD, Google), role-based access control, audit logs, data residency, and PII masking. Enterprises can monitor usage, apply cost controls, and test workflows with guardrails and evaluations before production.
StackAI also offers flexible model routing, enabling teams to choose between OpenAI, Anthropic, Google, or local LLMs, with advanced settings to fine-tune parameters and ensure consistent, accurate outputs.
A growing template library speeds deployment with pre-built solutions for Contract Analysis, Support Desk Automation, RFP Response, Investment Memo Generation, and InfoSec Questionnaires.
By replacing fragmented processes with secure, AI-driven workflows, StackAI helps enterprises cut manual work, accelerate decision-making, and empower non-technical teams to build automation that scales across the organization.
Learn more
Vertex AI
Completely managed machine learning tools facilitate the rapid construction, deployment, and scaling of ML models tailored for various applications.
Vertex AI Workbench seamlessly integrates with BigQuery Dataproc and Spark, enabling users to create and execute ML models directly within BigQuery using standard SQL queries or spreadsheets; alternatively, datasets can be exported from BigQuery to Vertex AI Workbench for model execution. Additionally, Vertex Data Labeling offers a solution for generating precise labels that enhance data collection accuracy.
Furthermore, the Vertex AI Agent Builder allows developers to craft and launch sophisticated generative AI applications suitable for enterprise needs, supporting both no-code and code-based development. This versatility enables users to build AI agents by using natural language prompts or by connecting to frameworks like LangChain and LlamaIndex, thereby broadening the scope of AI application development.
Learn more
LangChain
LangChain is a versatile framework that simplifies the process of building, deploying, and managing LLM-based applications, offering developers a suite of powerful tools for creating reasoning-driven systems. The platform includes LangGraph for creating sophisticated agent-driven workflows and LangSmith for ensuring real-time visibility and optimization of AI agents. With LangChain, developers can integrate their own data and APIs into their applications, making them more dynamic and context-aware. It also provides fault-tolerant scalability for enterprise-level applications, ensuring that systems remain responsive under heavy traffic. LangChain’s modular nature allows it to be used in a variety of scenarios, from prototyping new ideas to scaling production-ready LLM applications, making it a valuable tool for businesses across industries.
Learn more
Base AI
Uncover the easiest way to build serverless autonomous AI agents that possess memory functionalities. Start your endeavor with local-first, agent-centric pipelines, tools, and memory systems, enabling you to deploy your configuration serverlessly with a single command. Developers are increasingly using Base AI to design advanced AI agents with memory (RAG) through TypeScript, which they can later deploy serverlessly as a highly scalable API, facilitated by Langbase—the team behind Base AI. With a web-centric methodology, Base AI embraces TypeScript and features a user-friendly RESTful API, allowing for seamless integration of AI into your web stack, akin to adding a React component or API route, regardless of whether you’re utilizing frameworks such as Next.js, Vue, or plain Node.js. This platform significantly speeds up the deployment of AI capabilities for various web applications, permitting you to build AI features locally without incurring any cloud-related expenses. Additionally, Base AI offers smooth Git integration, allowing you to branch and merge AI models just as you would with conventional code. Comprehensive observability logs enhance your ability to debug AI-related JavaScript, trace decisions, data points, and outputs, functioning much like Chrome DevTools for your AI projects. This innovative methodology ultimately guarantees that you can swiftly implement and enhance your AI features while retaining complete control over your development environment, thus fostering a more efficient workflow for developers. By democratizing access to sophisticated AI tools, Base AI empowers creators to push the boundaries of what is possible in the realm of intelligent applications.
Learn more