Stack AI

What is Stack AI?

Stack AI is an enterprise-grade, no-code/low-code platform for building, testing, and deploying AI-powered workflows and agents, centered around advanced large language models (LLMs). Founded by Antoni Rosinol and Bernardo Aceituno, both former MIT PhDs, Stack AI addresses the challenge of connecting business data with cutting-edge AI models.

The platform enables organizations to connect disparate data sources and business systems, automate complex processes, and deploy AI assistants at scale—all without requiring deep programming expertise. Stack AI targets enterprises needing secure, compliant, and scalable AI-driven operations.

Key Characteristics:

No-code/low-code builder: Drag-and-drop workflow creation, modular steps, and plain English descriptions scaffold automation

LLM Integration: Native support for OpenAI, Anthropic Claude, Google Gemini, and Cohere models

Security/compliance: SOC2, HIPAA, and GDPR compliant

Integration ecosystem: 100+ SaaS, cloud, database, and API connectors

Flexible deployment: Cloud, on-premise, VPC, web, chat, API, Slack, Teams, embedded widgets

Enterprise focus: Designed for regulated industries and large-scale automation with granular access controls

How Stack AI is Used

Core Usage Patterns

Stack AI streamlines knowledge-driven, repetitive, or labor-intensive business workflows by combining LLMs with a visual workflow builder:

AI Agent Creation: Build custom chatbots, document analyzers, knowledge assistants, and RAG bots through a modular builder

Document Processing: Extract, classify, and process data from PDFs, contracts, spreadsheets at scale

Workflow Automation: Orchestrate business logic—ingesting emails, querying CRMs, generating reports, posting to Slack

Data Integration: Connect unstructured data from SharePoint, Salesforce, Notion, Snowflake, Google Drive

Custom Interfaces: Deploy through web widgets, forms, Slack/Teams bots, or API endpoints

Example Workflow:
Read PDF from Google Drive → Extract key data with OCR → Analyze with Claude or GPT-4 → Write summary to Salesforce → Notify team on Slack

Who Uses Stack AI

Finance & Insurance: Automate document review and compliance

Healthcare: Build HIPAA-compliant co-pilots for physicians, automate charting, and EHR extraction

Education: AI tutors, knowledge assistants, administrative bots

Agencies & Marketing: Content QA, onboarding flows, knowledge bots

IT & Operations: Back-office automation, RFP management, data extraction

Non-technical users: No-code interface enables business analysts and operations managers to build automations

Technical leads: API and deep integration support for developers

Customer Examples: HP, SmartAsset, Red Bull, IBM, MIT

Key Features

No-Code Workflow Builder

Visual drag-and-drop: Compose workflows from modular “nodes” (data sources, logic, AI models, outputs)

Quick Start/Custom modes: Use templates or design from scratch

Conversational builder: Scaffold workflows by describing them in plain English

Advanced logic: Parallel, conditional, and multi-source aggregation supported

Reusable components: Save and reuse workflow templates across the organization

Integration Ecosystem

Over 100 integrations with SaaS, cloud storage, databases, communication platforms, and APIs:

Data: Google Drive, OneDrive, Snowflake, SharePoint, Notion, MongoDB, Airtable, AWS S3, BigQuery

Business apps: Salesforce, HubSpot, Zendesk, GitHub

Messaging: Slack, Microsoft Teams, WhatsApp, SMS

AI providers: OpenAI, Anthropic, Google, Cohere

Custom APIs: Connect any RESTful service; Managed Custom Processes (MCP) nodes allow advanced integrations

Integration setup: OAuth authentication, reusable across workflows

Multi-Channel Interfaces

Web chatbots: Embeddable on websites or internal portals

Forms: Structured data collection with AI-powered feedback

Batch processing: Analyze documents in bulk

Slack/Teams bots: Bring AI assistants into collaboration tools

API endpoints: Embed workflows into custom applications

Custom UI branding: Tailor chatbots and forms with company branding

Granular access control: Manage who can use or modify each interface

Advanced AI Capabilities

Retrieval Augmented Generation (RAG): Connect LLMs to proprietary data for context-aware answers with citations

Data extraction/classification: OCR for scanned documents, structured data extraction

Prompt engineering: Tune LLM instructions, collect feedback, iterate on agent behavior

Custom LLM selection: Assign specific models (GPT-4, Claude 3) to different workflow steps

Knowledge base search: Vector database support for semantic retrieval

Security, Compliance, and Access Control

Compliance: SOC2, HIPAA, GDPR certified—suitable for regulated industries

Deployment: Cloud, on-premise, or VPC for dedicated infrastructure

SSO: Integrates with enterprise identity providers

Role-based permissions: Fine-grained access, audit trails for compliance

Use Cases and Examples

Financial Document Review

Challenge: High-volume loan or credit application processing

Workflow: Ingest documents → OCR and LLM extraction of key terms → Cross-reference databases → Generate reports → Notify on Slack

Outcome: Review time reduced from hours to minutes, fewer manual errors

Healthcare Patient Co-Pilot

Challenge: Reduce administrative time for physicians

Workflow: Integrate with EHR → Summarize history, generate SOAP notes → Deploy secure chatbot in Teams

Outcome: Improved documentation, more time for patient care

AI Knowledge Assistant

Challenge: Employees need instant, accurate answers from internal docs

Workflow: Load knowledge base → RAG workflow retrieves relevant info → LLM generates cited answers → Deploy as web/Slack bot

Outcome: Boosts productivity, reduces helpdesk tickets

Automated RFP Management

Challenge: Tedious, repetitive RFP/questionnaire responses

Workflow: Ingest RFPs → Draft responses with LLM/templates → Route for review → Submit and track progress

Outcome: Faster, more consistent RFP turnaround

Marketing Content QA

Challenge: Review volume and quality of marketing copy

Workflow: Pull drafts → LLM checks for grammar, compliance → Summarize feedback → Notify managers

Outcome: Faster, higher-quality reviews, lower risk

Technical Architecture

Stack AI’s technical foundation is modular and cloud-native:

Node-based workflow engine: Each step is a modular node (data, logic, AI, output)

Connector ecosystem: 100+ connectors for SaaS, databases, APIs

Model orchestration: Assign/manage LLMs per workflow step; supports prompt/context injection

Deployment: SaaS, VPC, or on-premise

Monitoring/logging: Detailed run history, error tracking, analytics

Vector databases: Power semantic search and RAG

Machine learning/NLP: Core for extraction, classification, summarization

Access/security modules: Enforce compliance and protection

Stack AI vs Other Platforms

Stack AI Differentiators:

• Rapid LLM support (new models adopted quickly)
• RAG and vector database integration
• Enterprise compliance (SOC2/HIPAA/GDPR)
• Flexible deployment (SaaS, on-prem, VPC)
• UI for both technical and business users

Limitations and Considerations

Pricing: Free tier for prototyping (500 runs/month); commercial deployments require custom enterprise plans

Learning curve: Advanced workflows require understanding of LLMs, data pipelines, and integration logic

Onboarding: Some users note limited step-by-step onboarding for complex cases

Integration coverage: Not every SaaS/analytics tool supported out-of-the-box (e.g., Google Analytics missing as of 2025)

Support: Community for free users, dedicated engineers for enterprise

Real-World Feedback

Testimonials:

• “The amount of adapters and connectors seems endless.”
• “Impressed with how Stack AI adds support for new LLM models the same day they’re released.”
• “A breath of fresh air when it comes to design compared to n8n or similar tools.”

Areas for Improvement:

• More onboarding content for complex workflows needed
• Pricing transparency is a common enterprise platform challenge

Key Terminology

Artificial Intelligence (AI): Simulation of human intelligence by machines

Workflow Automation: Automating business processes using software

Large Language Models (LLMs): AI models trained on large text datasets, such as GPT-4, Claude 3

Retrieval Augmented Generation (RAG): Combines retrieval from knowledge bases with generative AI for context-aware responses

Vector Databases: Databases designed for storing/searching high-dimensional vectors (embeddings), key to semantic search

Unstructured Data: Non-tabular data formats (text, PDFs, emails, images)

Access Control: Security mechanism for managing user permissions

Drag-and-Drop Interface: Visual UI for workflow/process building

Technical Expertise: Skill level required to use/build on a platform

Summary Table

Frequently Asked Questions

Can I use Stack AI if I’m not a developer?
Yes. Stack AI’s no-code interface and templates make it accessible to business users.

What kinds of AI models does Stack AI support?
OpenAI (GPT-4, GPT-3.5), Anthropic (Claude 3), Google Gemini, Cohere, and more.

How does Stack AI handle data privacy?
SOC2, HIPAA, GDPR compliant; supports on-prem/VPC deployment and role-based access.

Is Stack AI suitable for small businesses?
Primarily aimed at enterprises. Free tier is available for prototypes; SMBs may prefer alternative platforms.

What is a typical Stack AI workflow?
Input (file, email, API) → Data extraction (OCR, LLM) → Analysis → Output (CRM, Slack, report).

References

• Stack AI Official Website
• Stack AI Documentation
• Stack AI Security Page
• Stack AI Integrations Documentation
• Stack AI Pricing FAQ
• Stack AI Finance Solutions
• Stack AI Insurance Solutions
• Stack AI Healthcare Solutions
• Stack AI Education Solutions
• Stack AI Customer Stories
• Stack AI Use Cases
• Marketer Milk Stack AI Review
• Voiceflow Stack AI Comparison
• G2 Stack AI Reviews
• G2 Stack AI Review Example
• MongoDB AI Stack
• PromptLoop Stack AI Directory
• IBM AI Stack Overview
• NWAI AI Stack Overview
• Marketer Milk: Best AI Agent Platforms
• IBM Cloud AI Glossary
• Zapier Workflow Automation Guide
• OpenAI GPT-4
• Pinecone Vector Database