Grapevine

Product Introduction

1. Grapevine is an AI-powered platform that integrates with enterprise data sources such as Slack, Google Drive, code repositories, Linear, and others to enable AI agents to perform unified searches across all connected systems. It eliminates manual data retrieval by providing a single interface for querying company-specific documentation, communications, and codebases. The first product built on this infrastructure is a specialized company GPT optimized for organizational context.
2. The core value of Grapevine lies in its ability to reduce time spent on repetitive information retrieval tasks while delivering accurate, context-aware answers. It streamlines workflows by enabling AI agents to operate with full awareness of a company’s historical data, internal processes, and real-time updates across multiple platforms.

Main Features

1. Grapevine connects seamlessly to 15+ enterprise data sources, including Slack, Google Drive, GitHub, and Linear, using OAuth2 and API-based integrations with AES-256 encryption for data at rest. It indexes both structured (code, documents) and unstructured (Slack threads, meeting notes) data into a unified vector database.
2. The platform achieves operational readiness in under 30 minutes through automated data ingestion pipelines and preconfigured AI agent templates. Users can deploy a Slack bot within 10 minutes and begin querying historical company data with full context retention spanning up to two years of historical records.
3. Grapevine’s machine learning models continuously improve answer accuracy through reinforcement learning, analyzing user feedback on response quality. The system maintains an 85%+ accuracy rate for technical and operational queries, as validated by beta testing across 200+ enterprise users.

Problems Solved

1. Grapevine addresses inefficient cross-platform data discovery, where employees waste 3–5 hours weekly searching through disconnected systems like Slack archives, Jira tickets, and internal wikis. It eliminates redundant queries for information that already exists within company records.
2. The product targets engineering, DevOps, and operations teams at tech companies with 50–500 employees, particularly those managing complex infrastructure or distributed systems requiring frequent context switching.
3. Typical use cases include resolving cross-team dependencies (e.g., cloud infrastructure setup), onboarding new hires through automated documentation retrieval, and troubleshooting production incidents using historical post-mortem data.

Unique Advantages

1. Unlike generic ChatGPT implementations, Grapevine enforces strict data isolation with per-customer vector databases and SOC II Type 2 compliance, while competitors like Glean or Rewind use shared indexing architectures. It delivers 40% faster query resolution compared to alternatives by prioritizing organizational context over general knowledge.
2. The platform introduces dynamic context windows that automatically expand based on query complexity, accessing up to 50+ relevant documents per request without manual filtering. Its hybrid search combines semantic matching with exact keyword retrieval for technical terminology.
3. Competitive differentiation comes from transparent pricing (free tier for 100 monthly queries) and verifiable accuracy metrics, whereas competitors charge per user or require minimum contracts. Grapevine’s AI explicitly avoids training on customer data, unlike some LLM-based solutions that retain query logs.

Frequently Asked Questions (FAQ)

1. How does Grapevine ensure data security? All data is encrypted using AES-256 at rest and TLS 1.3 in transit, with isolated databases per customer and quarterly third-party penetration testing. Access controls follow zero-trust principles, requiring explicit permission grants for each data source.
2. What’s the setup process timeline? Most teams complete initial integration in 28 minutes on average, involving three steps: OAuth2 authorization for data sources (10 minutes), automated data synchronization (15 minutes), and Slack bot deployment (3 minutes). Full historical context becomes available within 48 hours as indexing completes.
3. Does Grapevine train AI models on our data? No customer data is used for model training—all machine learning improvements derive from anonymized query patterns and aggregated accuracy metrics. Data processing occurs in dedicated AWS instances that are purged upon contract termination.