Routing Models by Task Complexity

GC AI is turning model choice into a margin lever, which is what makes flat price, unlimited usage viable in a category where inference costs can otherwise spike fast. The product does not send every request through the most expensive reasoning stack. It uses cheaper paths for routine summaries and heavier paths for dense research, while fallback chains across providers keep the workflow live when one model underperforms or fails.

• This matters because GC AI sells a self serve $500 per month plan and higher priced team seats, so gross margin depends on keeping a solo lawyer who asks for NDA summaries from costing far less to serve than a lawyer running cross border regulatory analysis all day.
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• The same architecture also supports product breadth. GC AI combines chat, Word redlining, playbooks, and a research agent that searches statutes, regulations, case law, and company documents, so different tasks naturally call for different models, retrieval steps, and reliability backstops.
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• In legal AI more broadly, the stack is shifting away from one proprietary model and toward workflow software sitting on top of frontier models. Legora built on frontier models rather than its own model, and buyers increasingly compare vendors on workflow fit, setup time, and price instead of raw model novelty alone.
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The next phase is deeper routing by matter type, risk level, and customer playbook. As legal AI vendors converge on the same base models, the advantage moves to who can cheapest deliver the right level of reasoning inside the lawyer's actual workflow, without forcing every task through premium inference.