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Production AI Stack

The AI stack we build, ship, and keep running

A working demo is easy. A production AI system needs orchestration, retrieval, evals, monitoring, interfaces, cloud controls, and a team that owns the thing after launch.

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Stack Map

From workflow to operated AI

01 Agent Orchestration LangGraph / LangChain / CrewAI 02 Retrieval & Knowledge Vector Databases / RAG Systems / Document AI 03 Evaluation & Monitoring MLOps & Monitoring / LangSmith & Langfuse / Custom Evals 04 Application Layer Python / Node.js / React 05 Cloud & Infrastructure AI Cloud Infrastructure / Security Controls / Cost Controls

stack layers we design around

24/7

monitoring for production systems

30+

common integrations across agents

owned path from build to run

Core Layers

The stack is selected around the workload

We do not force a default toolchain. We choose the smallest reliable stack for your use case, then add controls where production risk demands them.

Agent Orchestration

Stateful graphs, tool-calling, multi-agent coordination, and human checkpoints for workflows that need to make decisions across systems.

LangGraph → Stateful agents, checkpoints, HITL flows LangChain → RAG, tool-calling, LLM orchestration CrewAI → Role-based multi-agent workflows MCP Servers → Standardized tool access for agents AI Agent Development → Custom agents wired to your tools and data OpenClaw → Autonomous agent platform, productionized safely

Retrieval & Knowledge

The layer that keeps answers grounded in your documents, product data, policies, records, and internal systems.

Vector Databases → Pinecone, Weaviate, Qdrant, Chroma, pgvector RAG Systems → Grounded answers with citations and source trails Document AI → Extraction and understanding over files

Evaluation & Monitoring

Production AI needs tests, traces, quality gates, and cost visibility so releases can be measured instead of guessed.

MLOps & Monitoring → Deployment, observability, retraining loops

LangSmith & Langfuse Tracing, regression checks, eval dashboards

Custom Evals Scenario tests based on your real workflows

Application Layer

The APIs, services, review queues, copilots, and interfaces that make AI useful to real users inside real operations.

Python → AI backends, agent services, RAG pipelines Node.js → Workflow APIs, tool integrations, real-time systems React → Copilots, review queues, internal tools Next.js → AI product interfaces and workflow apps

Cloud & Infrastructure

The deployment foundation for secure, scalable, cost-controlled AI systems across the cloud environment you already use.

AI Cloud Infrastructure → AWS, Azure, GCP, Docker, CI/CD

Security Controls Access boundaries, audit logs, secrets, data handling

Cost Controls Budgets, limits, cache strategy, usage dashboards

How we decide what belongs in your AI stack

A practical comparison of the stack choices we make when moving from a prototype to production AI.

Need	Likely Stack	Why It Fits
Agentic workflows	LangGraph, LangChain, CrewAI, MCP	When the system must plan, call tools, remember state, and hand off to humans.
Grounded answers	Vector databases, RAG pipelines, document AI	When the answer must cite internal documents, customer data, or operational records.
Product copilots	React, Next.js, Node.js, Python	When AI needs to live inside a product, dashboard, review queue, or workflow app.
Reliable operations	MLOps, tracing, evals, cloud infrastructure	When the AI system needs uptime, cost control, measurable quality, and release discipline.

Production Controls

What production adds on top of the tools

The model is not the product. The controls around it are what make the system reliable enough for customers, operators, and leadership.

Evaluation

Golden datasets, regression checks, and quality gates before model, prompt, or retrieval changes ship.

Observability

Tracing for agent runs, tool calls, failures, latency, and cost so the team can diagnose behavior quickly.

Security & Governance

Least-privilege access, audit trails, human approval paths, and data handling aligned to your risk level.

Ownership After Launch

Monitoring, iteration, prompt and retrieval tuning, incident response, and expansion to the next workflow.

Common questions about the production AI stack

What do you mean by a production AI stack?

It is the full set of tools needed to build AI that runs reliably: orchestration, retrieval, evaluation, monitoring, application engineering, and cloud infrastructure. A demo needs a model and a prompt. Production needs the full system around it.

Do I have to use every layer?

No. Most projects use a few layers, not all of them. We start from the workflow and recommend the smallest stack that can safely do the job.

How do you choose which tools go in our stack?

We choose against your constraints: data sensitivity, latency, scale, existing cloud commitments, integrations, and your team's skills.

Do you run the stack after you build it?

Yes. We stay on for evals, monitoring, cost control, and iteration. Production AI drifts if no one owns it.

Have a production AI system to build or rescue?

Tell us what you are building. We will map the stack, the first release, the risks, and what it takes to run it after launch.