85% of developers use AI daily — ship faster, secure AI-generated code, and automate DevOps with purpose-built copilots
Domain-trained AI assistants that handle the most common technology questions, paperwork, and decisions. Free to try, with everything you need in one place.
Architecture review, AI-generated code evaluation, technical decision-making, and engineering leadership for the AI-augmented team
Open copilotAI code vulnerability assessment, slopsquatting defense, dependency verification, compliance frameworks, and incident response
Open copilotCloud cost optimization, multi-provider architecture design, rightsizing, and infrastructure planning
Open copilotCI/CD pipelines, Kubernetes, platform engineering, infrastructure as code, and deployment strategies
Open copilotThe everyday problems we hear from technology folks, and how an AI copilot resolves each one in minutes instead of weeks.
62% of AI-generated code contains design flaws, including injection vulnerabilities, insecure deserialization, and hardcoded credentials. Slopsquatting attacks exploit AI-hallucinated package names to inject malware into dependency trees. Traditional security audits cost $15,000-$75,000 per engagement and were not designed to handle AI-generated vulnerability patterns.
Solve with Cybersecurity CopilotOrganizations waste an average of 32% of their cloud budget on oversized instances, idle resources, and suboptimal architecture decisions. For a company spending $100,000/month on cloud, that is $384,000 per year in waste that requires specialized expertise to identify and eliminate.
Solve with Cloud Architecture CopilotDevelopers spend 40% less time reviewing AI-generated code than human-authored code due to automation bias, even though AI output is more likely to contain subtle logical errors, performance bottlenecks, and security vulnerabilities. Without AI-specific code review processes, defect rates increase as AI adoption scales.
At senior engineer compensation of $180,000-$350,000, technical debt effectively costs organizations $54,000-$175,000 per engineer annually in lost feature development capacity. AI-generated code introduces a new category of uniformly distributed technical debt that compounds faster than human-authored debt.
Solve with Engineering CopilotDevOps engineers command $140,000-$220,000 in total compensation, and organizations without strong DevOps practices experience 440x longer lead times from commit to deploy. Platform engineering has emerged as the scaling solution, but building internal developer platforms requires expertise most teams lack.
Solve with DevOps CopilotCompanies spend $250,000-$500,000 on failed data science initiatives due to poor problem framing, inadequate data engineering, and misalignment between model capabilities and business needs. AI/ML roles grew 163% YoY but demand still outstrips supply significantly.
A glimpse of how a real conversation flows when you bring a tricky question to one of our copilots.
AI-generated code introduces 10,000+ new security findings per month — a 10x spike
Cybersecurity Copilot: 62% of AI-generated code contains design flaws, including injection vulnerabilities, insecure deserialization, and hardcoded credentials. Slopsquatting attacks exploit AI-hallucinated package names to inject malware into dependency trees. Traditional security audits cost $15,000-$75,000 per engagement and were not designed to handle AI-generated vulnerability patterns.
30-35% of cloud spending is wasted on overprovisioned resources
Cloud Architecture Copilot: Organizations waste an average of 32% of their cloud budget on oversized instances, idle resources, and suboptimal architecture decisions. For a company spending $100,000/month on cloud, that is $384,000 per year in waste that requires specialized expertise to identify and eliminate.
41% of production code is now AI-generated but lacks adequate review
Developers spend 40% less time reviewing AI-generated code than human-authored code due to automation bias, even though AI output is more likely to contain subtle logical errors, performance bottlenecks, and security vulnerabilities. Without AI-specific code review processes, defect rates increase as AI adoption scales.
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