per day, for every team member
built by Product and Business
by the teams themselves
using those internal tools
between a question and its answer
Loadshare
Loadshare is a third-party logistics company in Bengaluru that innovates on last-mile delivery and is building a third-party logistics marketplace. Its Product, Tech and Business teams are fully forward-deployed: they work inside the operations they serve and move at the speed of the business.
The challenge
Every individual at Loadshare was AI-native. The team was not. What one person worked out with their assistant stayed in that session, and the next person started from zero. Coordination still ran on meetings and status threads.
The connectors were not the problem. Engineering had wired AI assistants to the company's data through MCP, and that part worked. The problem was the expertise behind them: the definitions, cut-offs and methods that turn data into an answer had been written by hand by engineers, once. Every release moved the business a version ahead of that text, and nobody owned keeping it current. So nobody trusted the results. Every number was re-checked by hand, analysis queued behind a Product Analyst layer, and every decision waited on it.
After Catalyst
The future
The next step at Loadshare takes Product past the spec. Product managers already write the PRDs and share the expertise behind them. The aim now is for them to raise pull requests on the code of the core enterprise product lines themselves, reviewed by Engineering rather than queued for it. Deciding what to build and building it become the same motion, and the queue between them disappears.
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