Ontora

Reach for Ontora when the question is 'where do we start with AI, and how do we justify it?' Someone has to talk to twenty teams, stitch the answers into a process picture, and hand leadership a business case. That's the job Ontora automates, and doing it in parallel is the part that changes the economics — a two-week scope at Pilot Group beats the usual quarter of interviews and slide-building. Where it earns its keep is mid-sized, people-heavy operations: services firms, support orgs, engineering teams where handoffs and exceptions quietly eat capacity. Private equity teams get an obvious second use — run the same playbook across portfolio companies and compare where the friction sits. Where it bites is the human dependency. Ontora only knows what your employees tell it, so if teams are sceptical of recorded agent interviews, or process owners won't spare the time, discovery stalls. It's also periodic by design, not a live monitoring layer — if you need real-time operational telemetry, this isn't that. The closest alternatives split into two camps. Qualtrics and SurveyMonkey give you distribution and analytics without the conversation; Lucidchart and Miro give you diagramming after someone has already done the thinking. Ontora's bet is that the thinking itself — the follow-up question, the exception, the handover — is the expensive part, and that's a defensible position. On the product side, the recent chat workspace rework matters more than it sounds. Conversations now sit alongside campaign knowledge, research progress is visible, and generated charts and documents open as artifacts. In practice that turns discovery output into something you can hand around rather than a transcript dump. Shared chats push the same direction — scope a chat to specific campaigns