Adaptive Security
Adaptive Security is OpenAI’s investment for AI cyber threats. The company was founded in 2024 by serial entrepreneurs Brian Long and Andrew Jones. Adaptive has raised $50M+ from investors like OpenAI, a16z and executives at Google Cloud, Fidelity, Plaid, Shopify, and other leading companies.
Adaptive protects customers from AI-powered cyber threats like deepfakes, vishing, smishing, and email spear phishing with its next-generation security awareness training and AI phishing simulation platform.
With Adaptive, security teams can prepare employees for advanced threats with incredible, highly customized training content that is personalized for employee role and access levels, features open-source intelligence about their company, and includes amazing deepfakes of their own executives.
Customers can measure the success of their training program over time with AI-powered phishing simulations. Hyper-realistic deepfake, voice, SMS, and email phishing tests assess risk levels across all threat vectors. Adaptive simulations are powered by an AI open-source intelligence engine that gives clients visibility into how their company's digital footprint can be leveraged by cybercriminals.
Today, Adaptive’s customers include leading global organizations like Figma, The Dallas Mavericks, BMC Software, and Stone Point Capital. The company has a world class NPS score of 94, among the highest in cybersecurity.
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StackAI
StackAI is an enterprise AI automation platform that allows organizations to build end-to-end internal tools and processes with AI agents. It ensures every workflow is secure, compliant, and governed, so teams can automate complex processes without heavy engineering.
With a visual workflow builder and multi-agent orchestration, StackAI enables full automation from knowledge retrieval to approvals and reporting. Enterprise data sources like SharePoint, Confluence, Notion, Google Drive, and internal databases can be connected with versioning, citations, and access controls to protect sensitive information.
AI agents can be deployed as chat assistants, advanced forms, or APIs integrated into Slack, Teams, Salesforce, HubSpot, ServiceNow, or custom apps.
Security is built in with SSO (Okta, Azure AD, Google), RBAC, audit logs, PII masking, and data residency. Analytics and cost governance let teams track performance, while evaluations and guardrails ensure reliability before production.
StackAI also offers model flexibility, routing tasks across OpenAI, Anthropic, Google, or local LLMs with fine-grained controls for accuracy.
A template library accelerates adoption with ready-to-use workflows like Contract Analyzer, Support Desk AI Assistant, RFP Response Builder, and Investment Memo Generator.
By consolidating fragmented processes into secure, AI-powered workflows, StackAI reduces manual work, speeds decision-making, and empowers teams to build trusted automation at scale.
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SuperDuperDB
Effortlessly create and oversee AI applications without transferring your data through intricate pipelines or specialized vector databases. You can seamlessly connect AI and vector search directly with your existing database, allowing for real-time inference and model training. With a single, scalable deployment of all your AI models and APIs, you will benefit from automatic updates as new data flows in without the hassle of managing an additional database or duplicating your data for vector search. SuperDuperDB facilitates vector search within your current database infrastructure. You can easily integrate and merge models from Sklearn, PyTorch, and HuggingFace alongside AI APIs like OpenAI, enabling the development of sophisticated AI applications and workflows. Moreover, all your AI models can be deployed to compute outputs (inference) directly in your datastore using straightforward Python commands, streamlining the entire process. This approach not only enhances efficiency but also reduces the complexity usually involved in managing multiple data sources.
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Instructor
Instructor serves as a powerful tool for developers who wish to derive structured data from natural language input by utilizing Large Language Models (LLMs). By integrating seamlessly with Python's Pydantic library, it enables users to specify the desired output structures through type hints, which not only streamlines schema validation but also enhances compatibility with various integrated development environments (IDEs). The platform is compatible with multiple LLM providers such as OpenAI, Anthropic, Litellm, and Cohere, thus offering a wide range of implementation options. Its customizable features allow users to define specific validators and tailor error messages, significantly improving the data validation workflow. Trusted by engineers from notable platforms like Langflow, Instructor demonstrates a high level of reliability and effectiveness in managing structured outputs driven by LLMs. Additionally, the reliance on Pydantic and type hints simplifies the process of schema validation and prompting, requiring less effort and code from developers while ensuring smooth integration with their IDEs. This adaptability makes Instructor an invaluable asset for developers looking to enhance their data extraction and validation processes.
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