Nazmi
Nazmi is an AI engineering company that builds production-grade AI agents and integrates them into clients' existing systems, data, and infrastructure. The firm works forward-deployed inside client teams, owning the technical path from architecture to running system, including deployment, evaluation, guardrails, and handover. Its services cover agent and multi-agent architecture, tool use, retrieval pipelines, and MLOps for enterprise environments.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Many companies struggle to move AI projects from prototype to production, facing challenges with integration into existing systems, lack of evaluation and guardrails, and difficulty maintaining and operating AI applications after deployment. This often results in stalled demos that never deliver real business value.
Solution
Nazmi provides forward-deployed AI engineering, working inside client teams to own the technical path from first architecture to running system. The company builds AI applications for client workflows and connects them to the systems teams already use, including agentic systems that pursue goals using existing tools and data. Nazmi handles the full production engineering lifecycle: integration with data sources and APIs, deployment on client cloud or private infrastructure, evaluation and monitoring for quality and cost, and documentation and handover so client teams can maintain the systems. The approach emphasizes deciding where an LLM earns its place versus simpler automation, and building the guardrails, human oversight, and failure controls needed for production reliability.
Target Audience
Primary customers are enterprises and product teams that need production-grade AI systems integrated into their existing workflows, data, and infrastructure, particularly those with judgment-heavy processes that can benefit from AI agents and automation.
Features
- Agent and multi-agent architecture for multi-step, judgment-heavy work, including LLM workflow design and orchestration across tools and steps
- Tool use, function calling, and integration into existing systems, with retrieval, memory, and persistent state over client data
- Guardrails, human oversight, failure controls, evaluation, observability, and cost control to keep agent behavior measurable and production-safe
- Integration with data sources, APIs, and existing applications, including retrieval pipelines connecting applications to relevant company information
- Deployment on client cloud or private infrastructure with monitoring for quality, latency, and cost, plus failure recovery and operational support
- Documentation and handover so client teams can operate and maintain the system independently