Most AI agent projects stall after the proof of concept. The demo works. Production doesn't. I lead teams that build agents for real business processes and manage them after deployment so they stay accurate, reliable, and cost-effective.
AI agents are software systems that take action on your behalf. They don't just answer questions. They read documents, query databases, make decisions, trigger workflows, and interact with customers and employees. They operate continuously, handle volume that would require large teams, and improve over time.
Agents that execute multi-step business processes: intake and routing, document processing, approval workflows, data extraction and entry, report generation. They handle the repetitive operational work that consumes your team's time.
Voice and text agents that handle customer inquiries, schedule appointments, process requests, and escalate to humans when needed. These aren't chatbots reading a script. They understand context, access your systems, and resolve issues.
Agents that query across your databases, documents, and APIs to answer complex questions, generate insights, and surface information that would take a human analyst hours to compile.
Agents that evaluate options, score risks, recommend actions, and prepare analysis for human decision-makers. They augment your team's judgment with consistent, data-grounded analysis at speed.
The demo-to-production gap kills more AI agent projects than bad technology. The common pattern: a team builds a compelling proof of concept, leadership approves funding, and then reality hits.
The agent hallucinated on an edge case. It costs 4x more to run than the model suggested. The data pipeline feeding it breaks every Tuesday. Nobody defined what "good enough" accuracy means. The vendor who built it disappeared after delivery.
Production AI agents need monitoring, retraining, cost management, error handling, and someone who understands both the technology and the business process it serves. That's what I provide.
I don't design and hand off. I design and run. Every agent engagement follows the same disciplined lifecycle, and my team stays with the system after launch.
Map the business process. Identify where agents add value vs. where they add risk.
Architecture, model selection, data strategy, success metrics, cost projections.
Iterative development with continuous testing against real scenarios and edge cases.
Production rollout with monitoring, guardrails, human escalation paths, and fallbacks.
Continuous performance tracking: accuracy, latency, cost per interaction, drift detection.
Ongoing retraining, prompt refinement, model updates, and cost optimization.
AI systems are not traditional software. They degrade. Models drift as real-world conditions change. Foundation model providers update their APIs and pricing without warning. Business rules evolve. Your agents need active management, not just maintenance.
We track every interaction: accuracy rates, response quality, latency, cost per query. You get dashboards and regular reports. When performance drops below thresholds, we act before it becomes a business problem.
Agent accuracy degrades as the world changes. We monitor for drift, maintain evaluation datasets, and retrain or update agents proactively. Your agents stay accurate without requiring your attention.
AI compute costs compound. We optimize prompts, evaluate model alternatives, implement caching strategies, and right-size infrastructure. The goal: maintain or improve quality while reducing cost per interaction.
Defined SLAs with clear escalation paths. When an agent behaves unexpectedly or a dependency breaks, we respond. You get transparent post-incident reporting and permanent fixes, not temporary patches.
I'll tell you whether it's a good candidate, what it would take, and what it would cost. No commitment required.
Schedule a conversation →