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.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
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.
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
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.
We read news articles about stuff like this, but actually seeing it with our data, live, is a lot different.
Led by Peter Enestrom.
RAG development services questions, answered straight.
What partners, COOs, and IT leads ask before the pilot.
One place to ask a question and get an answer drawn from everything the firm knows — inboxes, documents, call transcripts, the CRM — with the sources shown. The catch in this category is where the index lives: most enterprise AI search products hold your data in their SaaS index. We build the same capability inside your own tenant, so the answer to “whose cloud does this live in” is yours.
Retrieval-augmented generation, minus the jargon: instead of asking an AI model to answer from memory, the system first retrieves the relevant passages from your own documents, notes, and transcripts, then writes the answer from those. That is how enterprise AI search stays accurate and cites its sources — and the retrieval layer is the part that has to be built around your permissions. It is the honest version of “talk to your data.”
The layer that links the things your firm knows about — people, companies, deals, experts — so questions like “does anyone here know someone at Apple” or “have we looked at this sector before” get real answers. Search finds documents; the knowledge graph finds the relationships between them. For a deal firm, it is the difference between a filing cabinet and a partner’s memory.
Buy if your data can live in a vendor’s SaaS index and your permissions are simple. Build when they are not: regulated data, LP confidentiality, and a compliance gatekeeper turn most product evaluations into a dead end. RAG development services means the ingestion, retrieval, and permission logic are built for your sources and your rules — inside your tenant — instead of configured within a product’s limits. For custom agents on top of that retrieval layer, see our AI agent development work.
The buyer-coined name for what this page describes: one intelligent system that stores the firm’s data and acts as a true enterprise-wide brain — an associate that never forgets anything you’ve ever told it. It is not a product category yet; it is what you get when enterprise AI search, a knowledge graph, and an assistant are built on your own data, in your own tenant.
In your tenant, under your existing permissions. If a person can’t open the SharePoint folder or the inbox, the assistant can’t answer from it — and walled-off material like LP-sensitive data and compensation stays walled off by design. For an SEC-registered RIA, that means the system fits the Reg S-P vendor-vetting and due-diligence questionnaire process instead of fighting it.
Probably lower it. The surprise bills come from sending every question — including shallow ones — to the most expensive frontier model. The ingestion layer is built so deep, analytical questions get the strong model and simple lookups get cheaper ones, with an initial token budget agreed up front. One buyer’s goal says it plainly: reduce the token costs and shift them to lower cost.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.