2025 will be remembered as the year AI stopped being a side experiment and started becoming real infrastructure. Big investments made it clear that AI is now a long-term priority, not a short-term trend. Projects like the ~$500 billion Stargate initiative showed how seriously governments and large organizations are taking AI compute, data, and scale.
At the same time, new models and tools kept coming fast and are still coming. Latest developments in LLM like GPT-5.2 raised expectations around reasoning, multimodal input, and day-to-day productivity. Ideas like autonomous agents and new ways of writing software gained attention, and teams across industries started testing how far AI could go beyond simple chat or summaries.
But alongside all this progress, a familiar pattern remained. Many enterprises built demos and pilots, but struggled to move them into production as part of daily workflows. Models worked in isolation, lived outside business tools, or stalled during reviews around data access, safety, and control.
That gap is where AI Squared stays focused. Our mission is to help organizations move AI from proof of concept into production. We are building an AI infrastructure platform with familiar workflows and a no-code approach, so teams can test, deploy, and run AI inside their own environments and existing applications. The goal is not to chase every new idea. It is to make AI usable, manageable, and part of everyday work.
Our product focus areas
The current product updates centers on six core areas that align directly with our roadmap and customer goals:
- Workflows and automation
- Access to AI in daily work
- Governance, safety, and compliance
- Trust, visibility, and feedback
- Control inside AI workflows
- Platform reliability and infrastructure
Let’s dive into each:
Automating & streamlining your daily business workflows
Every team has a touchpoint in their process where data, decisions, and outputs need to flow together smoothly. When these parts don’t connect well, it can mean manual steps, missed context, and slow responses across business processes.
AI Squared’s workflow builder helps teams bring structure to these tasks without writing code or building everything from scratch. It gives you a visual canvas where you can drag and link components that pull in end user input, run AI models, get data from sources, and shape outputs into dynamic charts or plain text as needed. Workflows can start with a chat prompt, run retrieval-augmented queries, and show results as tables or visual summaries, all from a single interface.
This means teams can automate workflows that match real business needs without heavy engineering work. Instead of hard-coding paths or stitching tools together, the workflow builder lets you combine modular pieces in a way that works for you. The result is an AI workflow that feels like part of everyday tools and business activity.
Access to AI where work already happens: Slack
AI insights often live outside the tools teams use every day. When people have to switch apps just to run a workflow or check results, usage drops and AI stays on the sidelines.
AI Squared focuses on meeting users where they already work. By bringing workflows into familiar tools, teams can act on AI without changing how they work on daily basis. This removes friction and makes AI part of normal work, not a separate task.
By embedding custom workflows or chat assistants into Slack, teams can run and interact with AI workflows directly from channels or DMs. Workflows can be triggered, monitored, and used inside Slack, making AI available in the same place where teams already collaborate and make decisions.
Built-in governance, safety, and compliance
As AI usage grows, teams worry about who can run workflows, what data gets accessed, and whether prompts follow internal rules/compliance checks. Without clear controls, adoption slows or gets blocked by security reviews.
AI Squared builds governance directly into workflows so teams can move forward without losing oversight. Security checks happen at the right moments, not after problems appear.
With guardrails and workflow governance, end user inputs are checked for policy violations before execution. Access rules control who can run workflows and what data can be retrieved, based on identity and metadata. Teams can also define custom compliance rules to match internal policies.
Trust, visibility, and feedback on AI usage
When AI runs in the background, leaders struggle to answer basic questions. Who is using it. What is working. Where and when things fail. Without this clarity, trust stays low and decisions get delayed.
AI Squared makes AI activity visible and easy to review. Teams can see how workflows perform and understand where and when users place confidence in the AI results.
With workflow analytics, teams can track runs, errors, success rates, and run times over defined periods. Chat responses can also show source citations, so users know where answers come from and can verify them when needed.
Control and decision-making inside AI workflows
Enterprise AI workflows rarely run in a straight line. Inputs change, outputs vary, and edge cases show up more often than teams expect. When workflows cannot respond to these changes, they fail or require manual intervention, slowing everything down.
AI Squared addresses this by giving teams more control over how workflows behave as they run. With conditional components, AI Squared evaluates outputs during execution and routes the workflow to the right next step. If no condition is met, a default path runs automatically, so execution continues without interruption.
Embedded AI experiences for end users
AI often feels disconnected when it lives outside the product or portal people use every day. Users lose context, past conversations disappear, and the experience feels inconsistent across tools.
AI Squared brings AI directly into existing applications, so it feels like part of the product. This helps users stay in flow while keeping conversation history and session context intact.
With embeddable chat assistants, teams can place full chat experiences inside third-party applications without having to switch between the windows. These assistants support chat history, session browsing, and new sessions, so users can continue conversations without starting over each time – improving the overall end-user experience for enhanced productivity.
Platform reliability and foundational infrastructure
AI teams often spend more time setting up and maintaining infrastructure than using it. Managing vector databases, connectors, and reliability issues slows progress and pulls focus away from real use cases.
AI Squared reduces this operational load by handling core infrastructure within the platform. Teams can rely on built-in components instead of stitching together separate systems.
With an in-house hosted vector store, admins can set up and manage vector storage directly in AI Squared. Tables, connectors, and sync paths are created automatically, making it easier to use vector data across workflows without extra setup.
Closing thoughts
These updates reflect a clear direction. AI that fits into daily work. AI that is governed, visible, and reliable. AI that moves from idea to impact.
That is how organizations bring AI into production.
If you want to see how these updates support real business workflows, book a demo. We will walk through what is new and how teams are using it today.
