Investment banking teams use Kaylo to run proposals faster, more consistently, and with full traceability — a structured workflow from data ingestion to client-ready export, not a tool you prompt.
CRM, email, research, filings, market data
Tagged by deal, client, sector, team, and source
Single source of truth per deal
Centrally governed prompt logic, context selection, output handling
Source attribution, versioning, approval states, access controls
Precedent-grounded suggestions, continuous quality improvement
PPT and PDF in bank-standard formats
The average deal team already runs GPT, Gemini, Copilot, Bloomberg, and CRM in parallel. Each tool generates insight. Each keeps it. None feed back into firm memory. None are governed. Building the layer that fixes this would take an internal team years — and it would still start from zero.
Banks use a mass of AI tooling. The issue is that none of it feeds back into a shared memory layer — insights stay inside the tool that generated them, and the IP compounds elsewhere. Kaylo sits above all of it and captures what they produce.
Role-based access, data lineage, approval states, and MNPI barriers have to be designed into the first ingestion — not retrofitted onto an existing pipeline. Internal IT builds features. Kaylo builds the architecture, purpose-built for regulated deal environments.
When analysts take AI output into external tools to revise it, that learning is lost. Kaylo captures every accepted change back into institutional memory, linked to the original source and the reasoning behind it. The next mandate starts with what the last one learned.
A structured deal workspace that tags every piece of incoming data — CRM contacts, email threads, transcripts, filings — by deal, client, sector, team, and source. Proposal sections are generated using centrally governed prompt logic, not open-ended user prompts: the system selects the relevant context, routes to the right model, and validates output before surfacing it. Every edit made inside Kaylo is written back into institutional memory.
Every piece of content in Kaylo carries a complete lineage chain — original source, ingestion metadata, access control state, model routing decision, generation parameters, and approval state. Governance isn't a layer added on top: it's enforced at every step from the first byte ingested. The result is output a compliance team can interrogate, not just an MD can trust.
Atlas runs semantic search across a metadata-tagged corpus of deal files, filings, transcripts, and research — routing each query to the most appropriate model based on task type, cost, and latency. Answers are cited at the source-chunk level, not just the document. Insights saved by analysts are written back into the deal's memory layer, not stored in a chat session that disappears.
Structured deal workflows powered by AI, not a chatbot. The value is in the process, not the prompt.
Every output must meet the standard of a Managing Director reviewing it cold.
Every claim, number, and narrative traceable to a source. Institutional trust is built on lineage.
The product gets better with every deal. Institutional memory, approved precedents, and feedback loops are the moat.
Permissions, audit trails, compliance, and data isolation are not afterthoughts — they're foundational.
Request a walkthrough with our team and see how Kaylo transforms deal execution.
Seek Access