AI Agents that go from demo to production and stay reliable.
An AI demo is easy to impress with. Making it reliable, useful, and ready for real business is harder. We turn promising AI agents into production-ready systems, built around your workflows and optimized for performance and cost.
Why promising AI projects struggle in the real world
Agents bolted onto old workflows
Adding AI to an old process won’t work. It can’t fix a broken workflow. We redesign the process around what the AI agent can actually do.
POC works, production doesn't
Yes, A demo can look perfect until the real users arrive. Without proper testing, monitoring, and guardrails, small failures can quickly become big problems.
Costs scale faster than value
More AI doesn't always mean more value. We design AI workflows to deliver the right results without letting unnecessary model usage drive up your costs.
Everything your AI needs to work in the real world.
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Agent engineering & workflow redesign
We redesign workflows around what AI can actually handle, then build the agents to do the work. From planning and tool use to human oversight when it matters.
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RAG & retrieval systems
We connect agents to your business data so they can find the right information, give grounded answers, and respect your permissions, even as your data changes.
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LLMOps: evals, monitoring & guardrails
Evaluation frameworks, regression suites, output monitoring, fallback mechanisms and audit logging — the reliability layer that separates production systems from prototypes. Available as an ongoing managed service.
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AI cost optimization
Multi-model routing, small-model substitution for focused tasks, caching and prompt engineering that cut inference bills 40–70% — a low-risk entry engagement for teams already running AI in production.
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AI governance, security & private deployment
Accountability structures, audit trails and escalation paths designed into agent architecture from day one — plus private and self-hosted LLM deployment for regulated industries that must keep data, models and infrastructure under their own control.
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AI-accelerated legacy modernization
AI coding agents applied to legacy codebases — .NET, PHP, Java — migrating and modernizing at a multiple of manual speed, backed by twenty years of legacy engineering experience most AI-native shops lack.
POC → Production
We build with production in mind from sprint one, bringing evaluation, monitoring, cost control, and governance into the process early. So your AI is ready when real users are.
Build AI capability that stays in-house
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AI enablement & training
What if your team could get more out of the AI you already have? We train your engineers and business teams to work confidently with agents, AI testing, and AI-assisted development.
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Fractional AI leadership
Need AI leadership without a full-time CTO? Get an AI architect or fractional CTO to shape your roadmap, guide key decisions, and keep your strategy on track.
Common questions about agentic AI
We already built an AI agent. Can you take it to production?
Yes. We audit what you have, keep what works, and add the testing, monitoring, security, and reliability needed for production. How do we know if an AI agent is actually ready for production? We test it against real-world scenarios, measure its performance, and put monitoring and guardrails in place before it reaches users.
Can AI agents work with our existing systems and business data?
Yes. We integrate agents with your existing systems and connect them to the data they need to perform their work accurately.
Can we keep our AI and business data on our own infrastructure?
Yes. We can deploy AI models in your cloud or on-premise environment, keeping your data and infrastructure under your control.
How do we control the cost of running AI agents at scale?
We optimize model usage, routing, caching, and workflows to reduce unnecessary AI costs while maintaining performance.
Bring your agents from demo to dependable
Start with a free 30-minute assessment — you leave with a roadmap either way.