Enterprise AI Search
Enterprise AI search that runs inside your tenant — not a SaaS index.
Your firm’s knowledge is spread across 15–20 inboxes, SharePoint project folders, a legacy deal CRM, and note-taker apps. Enterprise AI search would fix it — if your data could live in someone else’s index. It can’t. We build AI-powered enterprise search inside your own tenant: permission-aware retrieval, a knowledge graph of your people and deals, and an assistant in the channels your team already uses.
Built with the leading AI platforms
NVIDIA
Anthropic
The scattered firm
Enterprise AI search is easy to want and hard to buy.
From five working sessions with a Dallas-based lower-middle-market PE sponsor — a partner wearing two hats across ~9 portfolio companies and the management company itself, with the COO as compliance gatekeeper. What the firm wants, in their words: “kind of like an enterprise firmwide brain concept… an intelligent system that stores a lot of our firm data and can act as a true enterprise wide brain.” What used to be called enterprise knowledge management never solved this.
Knowledge lives in all sorts of different systems
The firm’s answers are scattered across inboxes, project folders, the deal CRM, and note-taker apps — “it’s very hard to talk to all those systems at once and access the knowledge and get the types of answers that they’re looking for.”
15 to 20 inboxes of relationship context
“Does anyone here know someone at Apple?” is a real sourcing question, and the answer sits in 15–20 personal inboxes. Who the partners know — and who they should be spending time with — is searchable by no one.
The CRM is only as good as what people type into it
“There’s a big gap often to what’s in the CRM versus what’s in the inbox.” The deal-sourcing CRM is arguably a legacy, analog system of its own — and nobody has time to keep it current by hand.
Call transcripts stranded on 5–7 seats
The firm pays for AI note-taker apps on a handful of seats. Every transcript — risks, takeaways, action items — is searchable by one person, inside one app, and invisible to everyone else.
Token bills nobody budgeted
“Token usage really, really scales up — especially with the more analytical and deeper questions.” One person somehow hit $400 in a month on a frontier-model subscription, and nobody can say what a sane budget looks like.
Compliance is way behind the models
As an SEC-registered RIA, every new vendor goes through Reg S-P vetting and a due-diligence questionnaire. “Our compliance partners are way behind where the models are” — so the default answer to a SaaS index full of firm knowledge is no.
What scattered knowledge costs
Without an enterprise knowledge graph, every question starts from zero.
What it looks like when firm knowledge has no connective tissue — the people, deals, and experts exist, but nothing links them. These are one firm’s numbers, from its own calls; the first thing we document on yours is where the knowledge lives and what the scavenger hunts cost.
From an anonymized engagement — a Dallas-based lower-middle-market PE sponsor, ~15–20 people, an SEC-registered RIA on a Microsoft 365 stack
How it works
The enterprise RAG build: ingestion, retrieval, and your permissions.
We are an AI operating partner — we advise, build, and run the machine. Three steps, fixed order, and your compliance gatekeeper reads the design before anything is indexed.
- 01
Map the sources, encode the permissions
We inventory where firm knowledge actually lives — the inboxes, the SharePoint project folders, the deal CRM, the note-taker transcripts, the data rooms — and encode who is allowed to see what before anything is indexed. LP-sensitive data, compensation, and anything the COO walls off stays walled off.
- 02
Build the retrieval layer and the knowledge graph
Documents, notes, and transcripts are indexed once, inside your tenant. A knowledge graph links people, companies, deals, and experts, so “who knows someone at Apple” becomes a question the firm can actually answer. And deep questions route to the right model instead of token-maxing a frontier model on every lookup.
- 03
Put the assistant where the firm already works
The assistant shows up in Teams, email, and text — the channels the team already lives in. Ask it what was said on a call three months ago, or whether the firm has history that validates a new banker outreach: an associate that never forgets anything you’ve ever told it.
An enterprise AI assistant, not another platform to adopt.
Search this category and you hit a wall of enterprise search software — polished products built on one assumption: your data moves into their index, a SaaS service outside your tenant. For a firm whose knowledge includes LP information, live deal flow, and partner inboxes, that assumption fails the Reg S-P vendor review before the demo ends.
The alternative is build, not buy. The retrieval layer, the knowledge graph, and the assistant run inside your own tenant, under your existing permissions, with the adoption work done alongside the build — so the partnership group sees it answer questions on their data, live, instead of reading about it. What one buyer called an enterprise brain is not a product you license; it is a system you own, and it compounds every day the firm uses it.
Led by Peter Enestrom.
Questions
RAG development services questions, answered straight.
What partners, COOs, and IT leads ask before the pilot.
What’s holding your business back?
A workflow ready for automation. An AI product you want to build. A problem that has sat on the roadmap for years. Let’s talk about what it would take to solve it.
Talk to Zaigo