Vikram Sreekanti
CEO
RunLLM
RunLLM provides an AI Site Reliability Engineer that integrates directly with existing observability and tooling stacks to automate incident investigation. This platform correlates evidence across logs, telemetry, and code to deliver rapid root cause analysis and actionable remediation steps. The service aims to significantly reduce Mean Time to Resolution (MTTR) and alert fatigue for on-call engineering teams.
- Artificial Intelligence
- Developer Tools
- Software Only
Updated 7 months ago
Funding
Funding not disclosed
Funding rounds are not available yet.
Founders
Product
Problem
Developers often spend significant time troubleshooting technical issues and searching for precise answers within complex documentation, leading to decreased productivity and delayed project timelines. Existing support solutions may lack the specific context needed to provide accurate and efficient assistance.
Solution
RunLLM offers AI-powered developer assistants tailored to a company's specific tech stack, providing context-aware technical support to accelerate troubleshooting and improve user adoption. By fine-tuning language models on proprietary documentation and codebases, RunLLM delivers precise answers and actionable insights directly within existing workflows. The platform integrates with tools like Slack and Zendesk, enabling developers to quickly access relevant information, reduce time spent on repetitive tasks, and focus on higher-value activities. RunLLM's assistants provide citations to documentation, multimodal answers, and detailed reasoning for each response, ensuring transparency and promoting self-learning.
Target Audience
RunLLM's primary customers are software development teams, open-source projects, and technology companies seeking to improve developer productivity, reduce support costs, and accelerate user adoption of their products.
Features
- Fine-tuned language models trained on specific technical documentation and codebases
- Integration with existing developer tools like Slack, Zendesk, and documentation sites
- Context-aware responses that understand the specific technical environment
- Multimodal answers that include relevant images and code snippets
- Detailed reasoning and citations for each answer, promoting transparency and trust
- Instant learning capability to correct errors and improve future responses
- Automated insights and suggestions for improving documentation and product
- Analytics dashboard to track user questions, feedback, and areas for improvement