Agentic AI Systems
We design and ship agentic workflows with LangChain and LangGraph, from a single tool-calling agent to multi-agent orchestration that can run in production.
- Dedicated Engineering Teams
- Cloud & DevOps
AI & MLOps Engineering
Agentic AI systems and production ML pipelines built by the same senior engineers who deploy and support them.
MLOps & Model Deployment
We deploy, monitor, and retrain models as part of your actual infrastructure, not a notebook that only runs on someone's laptop.
AI-Assisted Product Features
We integrate LLM-backed features, retrieval, agents, and evaluation into existing products without turning your codebase into a science project.
How We Build Production AI Systems
Scope the Use Case
We start with what decision or workflow actually needs automating, not which model to use first.
1
Prototype Fast
A working prototype against real data, evaluated against a baseline, before we commit to an architecture.
2
Productionize
Move from notebook to pipeline, deployment, monitoring, and retraining, the infrastructure that keeps a model running.
3
Operate & Iterate
We stay on to monitor drift, retrain, and adjust as your data and use case evolve.
4
Discuss an AI Use Case
Talk to the engineers who'd actually build it.
Discuss Your Project5
Why AI Should Not Be Bolted On
When a project calls for AI, it goes through the same dedicated team as everything else we build, not a separate department that hands you off after the demo.
We focus on agentic systems built with LangChain and LangGraph, and the MLOps work that keeps them running: deployment, monitoring, evaluation, and retraining. If your use case needs custom model training rather than integration and orchestration, we’ll tell you that upfront instead of force-fitting it into our process.
You get the agent workflow, evaluation loop, deployment path, and operating plan together, not as separate handoffs.
FAQs About AI & MLOps
Do you build custom models, or integrate existing ones?
Most projects integrate and orchestrate existing foundation models rather than train from scratch. We build the pipeline, evaluation, and guardrails around them.
What frameworks do you use for agentic systems?
Primarily LangChain and LangGraph for orchestration, alongside standard MLOps tooling for deployment and monitoring.
How do you keep AI features from becoming unmaintainable?
The same engineers who build it support it, and we design for evaluation and monitoring from day one instead of bolting it on after something breaks.
Can you add AI capabilities to a product we already have?
Yes, this is most of what we do, integrating retrieval, agents, or model-backed features into a product that already exists, rather than building something new from scratch.
Talk to the Team That Will Do the Work
Tell Us What You Need
Share the product, team, or technical
problem you want help with.No formal brief, deck, or RFP required.
1
Get a Practical Read
A senior person reviews the fit, constraints, risks, and likely path forward.
2
Meet the People Involved
You speak with the team that would scope and deliver the work, not a handoff chain.
3
Shape the Engagement
We align on priorities, team size, communication rhythm, and delivery expectations.
4
Start With Clarity
Work begins with the same context, people, and priorities discussed upfront.
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