Stack AI vs. Parlant

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.

Parlant is an open-source framework meticulously crafted for production, aimed at developing AI chat agents that comply with industry standards while adeptly handling rising complexity. It equips developers with the ability to build conversational agents that are not only adaptive but also iterative and transparent, leveraging natural-language behavior modeling techniques that incorporate a variety of components such as guidelines, journeys, canned responses, retrievers, glossaries, and tools, all of which can be managed through version control via Git. The framework's guidelines enable developers to finely tune agent behavior according to contextual factors, while the journey feature delineates multi-step interaction paths; canned responses ensure consistency during critical exchanges, and explainability tools provide clarity regarding the agents' decision-making processes. Furthermore, the tools are designed to align with established guidelines during operation, fostering a clear separation between business logic and conversational dynamics, thereby enhancing cooperation between developers and business stakeholders. In addition, built-in functionalities such as session persistence, tracking of tool outcomes across different sessions, and a user-friendly React chat widget further streamline the integration process, simplifying implementation for developers. This holistic approach not only ensures that users can craft highly efficient and compliant conversational agents but also allows them to tailor these agents specifically to meet unique requirements and objectives. Ultimately, the Parlant framework represents a significant advancement in the realm of conversational AI development.