Altimate AI

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About
Altimate AI acts as an intelligent layer for data ecosystems, providing "AI teammates" designed to streamline analytics and platform engineering workflows. The platform integrates deeply with tools like dbt, Snowflake, and Databricks, assisting users in writing SQL, converting queries into dbt models, and generating comprehensive documentation automatically. By maintaining full context across a company's data stack, these AI agents can remember past work, adapt to specific team standards, and provide intelligent suggestions directly within common coding environments like VSCode, Cursor, and Windsurf. The system operates on a local-first architecture, which is a significant differentiator for security-conscious organizations. Credentials and actual customer data are stored and processed locally on the user's machine, ensuring that sensitive information never leaves the work environment. The platform includes a PII Guardrails feature that automatically sanitizes or blocks sensitive data before it reaches third-party LLMs or agentic interfaces. This allows teams to leverage the power of generative AI for data tasks while maintaining SOC 2 Type II compliance and strict data privacy standards. For analytics engineers, Altimate AI simplifies the complexities of dbt development by offering real-time column lineage, automated testing, and smart-complete capabilities. Platform engineers can use the tool to automate infrastructure management, monitor warehouse costs, and plan migrations between major platforms like Snowflake and BigQuery. The inclusion of a Knowledge Hub and Memory Hub ensures that institutional knowledge is preserved and easily accessible, reducing the time spent on redundant tasks and context switching across the data lifecycle. What sets Altimate AI apart is its focus on enterprise-grade guardrails combined with deep technical integration into the modern data stack. Unlike generic AI coding assistants, it understands the specific nuances of data lineage, transformation, and warehouse optimization. It supports a variety of deployment options, including a Python package and CLI-based tools. This flexibility, coupled with a transparent metadata-only collection policy, makes it a specialized solution for data-heavy enterprises looking to scale their engineering output without compromising on data sovereignty.
Pros & Cons
Maintains SOC 2 Type II compliance and enterprise-grade security controls.
Local-first architecture ensures raw data and credentials never leave the user's environment.
Provides deep, out-of-the-box integration with the full data stack including Snowflake and dbt.
Automates repetitive manual tasks like writing dbt documentation and generating tests.
Supports modern AI-native coding environments like Cursor, Windsurf, and Claude Code.
The Community and Pro tiers are governed by a monthly credit system that limits usage.
Advanced features like custom AI teammates and private knowledge hubs are restricted to Enterprise.
Certain cost-saving modules for Tableau and warehouses require a custom Enterprise contract.
Use Cases
Analytics engineers can automate the conversion of raw SQL into dbt models while generating real-time lineage and documentation.
Platform engineers can utilize AI agents to monitor Snowflake infrastructure costs and automate cross-platform migrations.
Data security teams can deploy automated PII guardrails to ensure sensitive data is sanitized before developers use generative AI tools.
Platform
Task
Features
• infrastructure migration assistance
• vscode & cursor integration
• automated data quality testing
• snowflake cost monitoring
• dbt documentation generation
• automated pii guardrails
• real-time column lineage
• sql to dbt model conversion
FAQs
Does Altimate AI use my data to train its models?
No, the platform only utilizes metadata for its operations and does not use actual customer data for model training or improvements. Your data is never shared with other customers or third parties.
How are my warehouse credentials stored and managed?
All credentials are encrypted and stored locally on your machine using your IDE's secure storage API. Connections are established directly from your environment, ensuring credentials never leave your machine.
What kind of data does the platform collect?
Altimate AI collects minimal metadata necessary for functionality, such as usage statistics and configuration settings. Users can optionally enable a Memory Hub to store high-level task summaries locally for better AI context.
How does the PII Guardrails feature protect sensitive data?
The feature automatically detects, sanitizes, or blocks personally identifiable information based on your policies. This prevents sensitive data from being exposed to agentic interfaces like GitHub Copilot or Cursor.
Is Altimate AI compliant with industry security standards?
Yes, the platform is SOC 2 Type II certified and maintains enterprise-grade security controls. It utilizes a local-first architecture and hosts its SaaS components within isolated AWS private VPCs.
Pricing Plans
Pro
USD29.00 / per month• Starts at 200 credits per month
• Limits at 500 credits per month
• Everything in Community
• VSCode integration
• Python package
• Community Slack channel
Enterprise
Unknown Price• Custom number of credits
• Custom AI Teammates (Python/OpenAPI)
• Knowledge Hub (Private Links)
• Warehouse & Tableau cost savings
• RBAC, SSO, and Configurable security
• Priority Support with SLAs
• Training Services
Community
Free Plan• 200 initial credits
• 100 monthly credits thereafter
• Knowledge Hub (public links)
• dbt models/docs/tests generation
• Real-time column lineage
• VSCode & Cursor support
• Python package access
• Claude Code & Windsurf support
Job Opportunities
Forward Deployed Software Engineer (Data)
Accelerate dbt development and optimize data workflows with AI teammates that provide real-time lineage, automated documentation, and enterprise-grade PII protection.
Education Requirements:
Bachelor's or Master's degree in Computer Science or a related technical field
Experience Requirements:
4+ years of professional software engineering or data engineering experience
Expert proficiency in Python
Deep, hands-on experience with the modern data stack
Other Requirements:
Experience in a Forward Deployed, Solutions Architect, or Customer-Focused Engineering role for a B2B SaaS product
Familiarity with Data Governance, Data Quality, and Data Observability principles
Strong communication skills
Responsibilities:
Lead the technical implementation, integration, and optimization of Altimate AI's platform
Serve as the primary technical point of contact for key accounts
Develop custom scripts, integrations, and tools using Python
Troubleshoot, diagnose, and resolve complex technical issues
Contribute to the core product by identifying common integration patterns
Show more details
Forward Deployed Software Engineer-Data
Accelerate dbt development and optimize data workflows with AI teammates that provide real-time lineage, automated documentation, and enterprise-grade PII protection.
Education Requirements:
Bachelor's or Master's degree in Computer Science or a related technical field
Experience Requirements:
4+ years of professional software engineering or data engineering experience
Expert proficiency in Python
Deep, hands-on experience with the modern data stack
Other Requirements:
Experience in a Forward Deployed, Solutions Architect, or Customer-Focused Engineering role for a B2B SaaS product
Familiarity with Data Governance, Data Quality, and Data Observability principles
Strong communication skills
Responsibilities:
Lead the technical implementation, integration, and optimization of Altimate AI's platform
Serve as the primary technical point of contact for key accounts
Develop custom scripts, integrations, and tools using Python
Troubleshoot, diagnose, and resolve complex technical issues
Contribute to the core product by identifying common integration patterns
Show more details
Founding Generative AI Engineer - India
Accelerate dbt development and optimize data workflows with AI teammates that provide real-time lineage, automated documentation, and enterprise-grade PII protection.
Benefits:
Competitive salary and equity
Access to cutting-edge AI infrastructure and resources
Team offsite (US)
Learning budget for AI courses, books, and resources
Dynamic and intellectually stimulating work environment
Experience Requirements:
5+ years of hands-on ML/AI experience
At least 1 year of hands-on experience in GenAI/LLM projects
Track record of successfully deploying ML/AI systems in production
Experience working with enterprise customers
Other Requirements:
Expertise in semantic search and embedding models
Experience in LLM frameworks (LangChain, LlamaIndex) and agentic AI systems
Advanced model fine-tuning techniques
Responsibilities:
Design and implement sophisticated multi-agent systems
Build and optimize retrieval-augmented generation pipelines
Implement advanced techniques (LoRA, Prompt Tuning) to adapt off-the-shelf models
Develop and optimize cloud-native architectures (AWS, Kubernetes)
Implement robust security measures for LLM applications
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