No ML degree required. Engineers need modern web development skills (JavaScript, TypeScript, APIs). We teach practical AI skills: prompt engineering, agent design, evaluation methods, and integration patterns through hands-on building.

We prioritize developer productivity and team preferences. Our toolkit focuses on CLIs like Anthropic's market leading Claude Code and OpenAI's Codex. We're technology-agnostic and adapt to your existing stack.

Success means shipping measurable AI workflows to real users with tangible business value. Your team gains hands-on knowledge to maintain and extend solutions independently. We ensure knowledge transfer, not dependency.

Completely vendor-agnostic. We select models based on your requirements: quality, latency, privacy, and cost. Implementations include runbooks explaining architectural decisions so you understand trade-offs and can adapt.

Bencium designs systems with auditability and compliance at their core: source attribution, detailed logging, role-based access controls, and fallback procedures. All implementations work within your existing compliance frameworks.

Most engagements run 4-12 weeks depending on complexity. Workshops are 1-2 weeks, proof-of-concept builds take 4-6 weeks, and full implementation projects span 8-12 weeks. We deliver incrementally so you see value throughout the process, not just at the end.

You receive working code, comprehensive documentation, evaluation frameworks, tested processes, and deployment runbooks. More importantly, your team gains hands-on knowledge to maintain, extend, and improve the solution independently. We ensure knowledge transfer, not dependency.

Success means shipping measurable AI workflows to real users that deliver tangible business value. We establish evaluation frameworks to monitor performance, track key metrics like task completion rates and user satisfaction, and ensure quality remains consistent throughout deployment.

Engineers should be comfortable with modern web development (JavaScript, TypeScript, APIs). We teach practical AI skills: prompt engineering, agent design, evaluation methods, and integration patterns. No ML degree required—we focus on building solutions, not academic theory.

Plan for 10-15 hours per week during active phases. Workshops require full participation for 1-2 weeks. Build-with-you engagements need core team availability for reviews, decisions, and learning sessions. We design schedules around your operational constraints.

We work with both. Startups get rapid prototyping and validation of AI-powered MVPs. Enterprises receive strategic guidance on adoption, compliance, and scaling. Our approach adapts to your stage: speed for startups, governance for enterprises, always practical and implementation-focused.

We're platform-agnostic and select models based on your requirements: quality, latency, privacy, and cost. We work with OpenAI, Anthropic, open-source models, and hybrid approaches. Our implementations include runbooks explaining architectural decisions so you understand trade-offs and can adapt.

We use your existing infrastructure or preferred cloud provider. Data stays in your environment unless explicitly moved. For regulated industries, we deploy within your VPC or on-premises. All implementations follow your governance policies, with comprehensive logging and role-based access controls.

You own everything: code, documentation, and knowledge. Your team can maintain and extend the solution independently. We offer optional ongoing advisory for quarterly reviews, optimization guidance, and adaptation to new AI capabilities, but you're never locked into dependency.