A comprehensive study of platforms turning an organization's hidden relationship capital — buried in emails, calendars, conversations, and directories — into its most powerful competitive asset.
When a partner at KPMG wants to land a new client, the most valuable asset isn't a pitch deck or a credentials document — it's knowing that a colleague had lunch with the CFO last month. That intelligence exists. It's in someone's sent folder. But no one could find it until Introhive came along.
This is the fundamental insight powering an entire category of enterprise software: organizations generate enormous quantities of relationship data, conversation data, and collaboration data every single day — and almost none of it gets captured, organized, or made accessible to the people who need it most.
Where the Knowledge Hides
The "dark data" of enterprise organizations — interaction intelligence that exists but is never surfaced.Who knows whom, how recently they've spoken, relationship strength, warm introductions — all locked in individual inboxes.
What customers are actually saying, objections, buying signals, champion identification — vaporized the moment a meeting ends.
Institutional knowledge, past work, playbooks, and expertise scattered across Slack, Drive, Notion, Confluence, and email.
Who your people know externally, who's changed jobs, which customers are champions — unmapped and invisible to BD teams.
The companies profiled in this study have each staked out a piece of this territory. Some focus on external relationship graphs (who knows who outside the firm), others on internal collaboration networks (how knowledge moves within teams), and the newest entrants are building unified knowledge graphs that connect all of the above. This is the most interesting and contested space in enterprise software today.
Introhive connects to the email and calendar infrastructure of an entire organization and quietly maps every touchpoint — every email sent, every meeting attended — across the firm. The result is a continuously updated graph that shows who knows whom, how strong each relationship is, and when the last meaningful contact occurred. When a partner at KPMG wants to reach a target client, they search the person in Introhive and immediately see which of their 30,000 colleagues already has a strong relationship with them — and can request a warm introduction.
Introhive's core innovation is the automatic, passive capture of relationship data. Unlike traditional CRMs that require salespeople to manually log interactions (a task that almost never gets done thoroughly), Introhive works silently in the background. No behavior change required from the firm's professionals. The data just accumulates.
It also introduced the concept of relationship strength scoring — not just "this person knows that person" but a quantified measure of how strong the connection is, based on recency, frequency, and reciprocity of communications.
Affinity automatically ingests email and calendar data from an investment team and builds a live, enriched map of all relationships. When a VC wants to evaluate whether to invest in a startup, they can instantly see which of their colleagues has the strongest relationship with the founder, who on their portfolio team has met them, and what the recent interaction history looks like. It also enriches company profiles automatically — pulling funding data, headcount, and growth signals without anyone entering a single line into a CRM.
The platform's "relationship scores" and automatic "deal flow" management have made it the dominant tool inside private equity and venture capital firms, where the traditional CRM was never designed for the long, winding, multi-decade relationship journeys of deal-making.
Introhive is built for large, staffed professional services firms (1,000+ person organizations). Affinity targets smaller, deal-intensive teams — a 15-person VC fund or a 40-person M&A team. It also places a heavier emphasis on company intelligence (funding rounds, hiring trends, growth signals) layered on top of the relationship graph, making it a deal sourcing tool as much as a relationship one.
Gong records and transcribes every customer-facing interaction — calls, video meetings, emails — and then applies AI to extract signal from the noise. Which deals are actually progressing vs. just looking healthy? Which reps have the best talk-to-listen ratio? Where are objections clustering? It turns the dark matter of sales conversations into structured, searchable, analyzable data.
By 2026 Gong has expanded into full revenue intelligence — deal forecasting, AI-powered next-step recommendations, automated CRM data entry from call recordings, and "Gong Agents" that work autonomously across the revenue cycle. It holds approximately 45% market share in conversation intelligence for enterprise sales.
Before Gong, the knowledge in a sales call was ephemeral — it existed for the duration of the call and then vanished (or lived as a rough note in someone's head). Gong makes every sales conversation a permanent, searchable, institutional asset. A new sales rep can listen to a hundred calls with a target account before their first meeting. A manager can spot exactly when a deal started going sideways. A product team can mine 10,000 calls for feature requests.
Glean connects to over 100 enterprise applications — Slack, Google Drive, Salesforce, Jira, Confluence, GitHub, email — and builds a personalized, permissions-aware knowledge graph for each organization. When an employee asks Glean a question ("What's our policy on X?" or "Find the last proposal we sent to Company Y"), it searches across all connected systems simultaneously and returns a synthesized, cited answer.
More recently, Glean has pushed into AI agents — autonomous workflows that can pull from this knowledge graph to complete tasks. It reached $100M ARR in under three years from commercial launch, which CNBC named it the #1 Most Innovative Company in Applied AI for 2025.
Glean addresses the broadest version of the problem: the totality of an organization's documented knowledge, scattered across every tool everyone uses, is invisible to most employees. The average knowledge worker reportedly spends 20% of their week searching for information or tracking down colleagues to answer questions. Glean's bet is that if you index everything and put AI on top, that 20% largely disappears — and with it comes an enormous productivity dividend.
Where Gong focuses on the content of conversations (what was said), People.ai focuses on the activity layer — quantifying who is engaging with which accounts, how often, at what depth, and whether that level of engagement is consistent with winning patterns. It has processed 350 million sales activities, 40 million contacts, and data from $100 billion in closed deals, building a model of what "healthy" engagement looks like across industries.
It also serves a CRM accuracy function: because most Salesforce instances have severely degraded data (outdated contacts, missing activity logs), People.ai effectively auto-populates and maintains the CRM, making the entire downstream stack more reliable.
While Introhive, Affinity, and Gong focus primarily on external relationships (clients, prospects, deals), a parallel industry has emerged around internal organizational intelligence — using the same email and calendar data to understand how knowledge flows within an organization.
ONA reveals the informal organization — the actual network of influence, communication, and knowledge-sharing that is completely invisible in the org chart. In a 50,000-person firm, there are typically a handful of "super-connectors" who bridge otherwise siloed departments. Identifying and retaining them is enormously valuable. ONA platforms find them using math (network centrality measures), not intuition.
Each platform occupies a different position in the "untapped knowledge" spectrum. The key dimensions are: what data source they tap, what question they answer, and who buys them.
| Company | Data Source | Core Question Answered | Primary Buyer | Best For |
|---|---|---|---|---|
| Introhive | Email + Calendar metadata | "Who inside our firm knows this external contact — and how well?" | CMO / BD Leader | Large professional services firms (law, consulting, accounting) |
| Affinity | Email + Calendar + public co. data | "Who on our team has the best path to this deal or founder?" | Managing Partner / Deal Team | Smaller deal-intensive teams: VC, PE, IB, corp dev |
| Gong | Call recordings + email transcripts | "What are customers actually telling us, and which deals are at risk?" | VP Sales / RevOps | Enterprise B2B sales organizations |
| People.ai | Email + Calendar + CRM activity | "What activities are our reps actually doing, and are they correlated with wins?" | CRO / RevOps | Large Salesforce-centric revenue teams |
| Glean | All enterprise apps (100+ connectors) | "Where is the answer to my question, anywhere in our entire organization?" | CIO / CHRO | Any large enterprise with fragmented knowledge tools |
| TrustSphere / Viva | Email + Calendar metadata | "How does knowledge actually flow internally, and who are our hidden influencers?" | CHRO / People Analytics | HR-driven transformation and change management programs |
Every company in this landscape is monetizing data that organizations already possess but couldn't use. None of them ask organizations to change what they do — they observe what already happens.
This "zero behavior change" architecture is the key design principle of the entire category. The value is unlocked by removing the human as the data entry point.
The category is maturing in some areas (professional services relationship intelligence is well-served) but wide open in others. Here is where the frontier sits in 2026:
Market Size Context
The macro market numbers behind the opportunity.30.4% CAGR. The underlying infrastructure that powers relationship and knowledge graphs is growing explosively as AI makes graph-based reasoning tractable.
15.7% CAGR through 2033. Network analytics as a sub-category, covering both ONA and external relationship mapping tools.
The broader sales intelligence and revenue intelligence category, which Gong and People.ai sit within, has been one of the fastest-growing software categories of the past decade.
The broader enterprise knowledge management market is massive — and AI has dramatically expanded what's possible, pulling significant investment into this previously stagnant category.
The companies in this study collectively represent a fundamental shift in how large organizations think about knowledge. For most of corporate history, institutional knowledge walked out the door when people left, sat dormant in inboxes, or simply never got captured at all. The insight these companies share is that the data to change that has always existed — it just needed to be observed, organized, and surfaced intelligently.
Introhive proved this in professional services. Gong proved it in sales. Glean is proving it in enterprise-wide knowledge access. The category is real, it's growing fast, and — most interestingly — it remains fragmented by vertical and use case. A platform that unifies relationship intelligence, conversation intelligence, expertise location, and organizational network analysis into a single, industry-specific product for a market that today's players haven't prioritized could be genuinely differentiated.
The "untapped knowledge" problem is not solved. It has barely been started.
Every idea below follows one non-negotiable constraint: zero additional friction for employees. No uploading, no tagging, no data entry. The intelligence is captured passively from systems that already exist.