Ogram provides AI agents that generate audit‑ready financial and legal analysis for M&A, capital markets, and private‑equity transactions. The platform anchors every claim to a verifiable source, maintains persistent context through checkpointed sessions, and outputs structured memos, models, or decks with full proof lineage for regulatory and legal scrutiny.
Funding
$3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Financial advisors and legal counsel in M&A, capital markets, and private equity need to produce analysis and documentation that can withstand legal and regulatory scrutiny, but existing AI tools are prone to hallucinations, loss of context, and lack auditability, making them unsuitable for evidentiary work.
Solution
Ogram offers decision‑grade AI agents designed specifically for regulated, evidentiary tasks. The platform runs long‑running, checkpointed sessions that anchor every claim to a verifiable source, creating a complete proof lineage. Its reliability architecture prevents hallucinations, memory loss, context drift, and other failure modes, ensuring that outputs remain consistent and reproducible even after interruptions. Users interact with the agent through a conversational interface that mimics expert questioning, while the system manages secure access to data rooms, Bloomberg, LSEG, and internal repositories. The final deliverable is a structured, audit‑ready memo, model, or deck that includes source citations, checkpoint logs, and traceable evidence, ready for committee review or legal defense.
Target Audience
Primary customers are M&A counsel, capital‑markets analysts, and private‑equity investment teams that require reliable, audit‑ready analysis for high‑stakes transactions.
Features
- Guardrailed, source‑anchored inference that ties every numeric or factual claim to an original document
- Persistent state management with checkpointing to preserve context across multi‑hour analytical sessions
- Structured output bundles that can be rendered directly into memos, financial models, or presentation decks
- Built‑in orchestration of specialized sub‑agents for parallel workstreams while maintaining a single decision set
- Full proof lineage metadata (source citations, passage extracts, checkpoint logs) for post‑hoc auditability
- Secure integration with data rooms, SharePoint, Bloomberg, LSEG, and internal data lakes via allow‑list controls