Sapiex solves, what matters to investors the most.

Multi-model, multi-agent workflows that read CIMs, financial models, and transaction documents the way a great associate would. We tear down the strategic headers and the footnotes. Map every figure to its source. Test internal consistency. Surface what a mediocre read would miss.

The agents are specialised. Each owns a stage of the deal. Together they produce associate-grade output across the lifecycle, not surface summaries.

Today's investing workflow is a context-switch nightmare. Five to seven fragmented tools switched daily, none remembering the last deal. PitchBook, CapIQ, Excel, data rooms, email, public AI. The team becomes the bottleneck.

Sapiex ends the fragmentation. One platform with persistent memory across deals. Open a deal once; every subsequent agent inherits full context, so your associates spend their day on judgement, not on collating.

LLMs are big black boxes. They produce outputs without citations and ask you to trust the result. In investing, trust without sources is not a position you can defend at IC.

Every Sapiex output is cited. Every figure traceable to its document, page, and extract. Every flag auditable. If the source moves, the citation moves with it.

Confidential deal data does not belong on a general-purpose AI. We give you isolated tenant infrastructure by default, or a ready deployment on your own VPC. Either way, your data does not train any public model, never leaves the perimeter you specify, and is yours to delete whenever you want.

Rohith is the founder and CEO. He was a private equity investment professional working directly with ex-partners from TPG and Carlyle on cross-border transactions across healthcare, technology, and consumer.

Started as a consultant at Bain & Company. He is an AI forward developer and an entrepreneur who has lived the problem and is building the solution.