We design and operate the four capabilities modern AI products actually need: autonomous agents that get work done, retrieval systems that keep answers grounded in your data, Claude Code integration that speeds up how your engineers ship, and the LLMOps backbone that keeps all of it reliable in production.
Most “AI initiatives” stall between the demo and production. TM Square closes that gap. We combine four disciplines that are usually sold separately — agent engineering, retrieval architecture, developer-tooling integration, and operations — into a single, coherent deliverypractice, so your AI systems don’t just work in a demo, they work every day, under load, with guardrails
We work across the entire stack — from agent reasoning and orchestration down to vector infrastructure and deployment pipelines — so you don't need four vendors for one system.
Every agent and assistant we build is anchored to your real data through retrieval architectures designed for accuracy, not just fluency.
We meet your developers where they work — inside the IDE, the terminal, and the CI/CD pipeline — with hands-on Claude Code integration, not just AI strategy slides.
Evaluation, observability, and cost controls are built in from day one, not bolted on after something breaks.
Autonomous systems get guardrails, permissioning, and human checkpoints wherever the stakes require them.
From discovery and architecture through deployment and ongoing optimization, one team owns the outcome.
We design and deploy autonomous agents that perceive context, plan multi-step actions, and execute — handling real workflows, not just
answering questions.
We build retrieval systems that ground your AI in your enterprise data — securely, accurately, and in real time — so answers are sourced, not guessed.
We bring Claude Code into your engineering workflow so your developers ship faster — with an agentic coding assistant that understands your codebase, your tools, and your process.
We build the operational layer that keeps agentic and RAG systems
reliable, observable, and cost-efficient once they’re live — because production is where most AI projects actually fail.
We identify the workflows, data, and engineering processes with the highest-value opportunity for agentic, retrieval, or coding-assistant capability.
Claude Code integration for faster shipping, plus agents that handle code review, testing, and internal tooling.
Compliance-aware RAG for policy and regulatory Q&A, agentic monitoring for reporting and reconciliation.
Agents that resolve multi-step issues autonomously, grounded in your product and policy documentation via RAG.
Retrieval systems grounded in clinical and research data, with strict permissioning and audit trails.
Autonomous agents for inventory, scheduling, and exception handling across complex, multi-system workflows.
RAG-powered research and drafting support, with LLMOps-grade evaluation to keep outputs auditable.
Most AI projects stall at the demo stage. We build the agents, retrieval systems, developer tooling, and operational discipline to get yours actually running — and keep running.
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