MagicGo plugs agents into the workflows and SOPs you already run: they execute the work, monitor their own performance in real time, and are tuned to cut cost and lift margin. From text summarization to video generation to patent workflow automation, every result is backed by tools and retrieval — and every step leaves a trace you can inspect.
Most workflow software digitizes the process. MagicGo automates it: agents that plug into the SaaS and SOPs you already run, execute the work end to end, and monitor their own accuracy and cost in real time — then get tuned to lower cost and lift margin. Beyond agent deployment, we build custom data management and optimization pipelines for each client, raising output per employee across the business.
Our team built production systems at Apple and Google.
Every engineer holds a doctorate from a top North American research program.
In partnership with Stanford's data science faculty, we bring current statistics, mathematics, and machine learning research — including patented methods — directly into production.
Four layers, built the same way — each grounded in tools and retrieval, each traceable end to end.
Off-the-shelf models don't always fit the task or the data. We fine-tune and deploy specialized models built around a client's own data, then run them on infrastructure we control — private, VPC, or fully on-prem — so nothing a user uploads leaves the system.
We build retrieval pipelines over your own documents, extract and summarize what actually matters, and generate the output as a finished document or video — not just a chat reply.
Agents that carry a business process end to end — not a single prompt. A patent-filing agent, for example, chains prior-art search (RAG), claims comparison, and application drafting into one run. Add a new tool or data source without retraining the model.
Every agent run is a trace: which tool fired, what it returned, how confident the result was. Run summarization across hundreds of thousands of documents and you still catch drift or hallucination the moment it happens — not weeks later in a spot check.
From your data to a traced result — and back again. Every run feeds the monitoring layer, which tunes the models and workflows to keep cutting cost and lifting margin.
Fine-tuned & deployed on infra you control
RAG, extraction, document & video output
Agents chain the process end to end
Every step traced, cost & accuracy watched
Below is the structure of a typical run from the patent workflow automation agent (sample data, not from a real user) — showing exactly what a trace records.
You shouldn't have to hand over your data before you can validate results. The model, the tools, and trace export all come up together.
$ magicgo agent deploy --model video-gen-13b --mode local ✓ weights verified (offline) ✓ tool registry loaded (4 tools) ✓ trace export → otlp://localhost:4317 Agent is live. Nothing leaves this host.