Case Studies
AI copilot and ops suite pattern
A pattern for internal copilots and ops tools that sit on top of trading, risk and support data.
Context
A team with multiple dashboards and ticket systems wanted a single AI-assisted layer to help support, ops and risk teams answer questions faster. They needed an ai copilot for trading ops that respected existing permissions and tools.
Key challenges
- Data spread across dashboards, logs and tools.
- High context-switching for operators.
- Difficult to onboard new team members.
- No consistent way to ask questions across systems.
What we designed and built
We implemented a copilot layer that connected to core systems via APIs and document stores, with clear prompts and tools per role. Operators could ask questions about users, trades, incidents and docs in one place.
Key design decisions
- Keep the copilot inside existing tools (not a separate app).
- Role-based context and permissions per team.
- Allow the system to say “I don’t know” when data is missing.
- Log questions and actions for later review and improvement.
Outcomes
Ops and support teams could resolve issues faster, with less back-and-forth across tools. New team members had a clearer place to start when learning the system, and reliability stayed front and centre rather than hype.
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