What are the limitations of Inven?

No published pricing, no self-serve signup and no plan comparison, so budget fit requires a sales conversation. Demo-gated onboarding makes it hard to evaluate data quality on your own before committing time. Positioned around M&A and consulting use cases, so general market research or non-financial teams will find it overbuilt.

Inven

Name: Inven
Author: Inven

AI market research and deal sourcing for M&A and consulting teams

Intermediate

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Screenshot of Inven, AI market research and deal sourcing for M&A and consulting teams

What is Inven?

Inven is an AI research and deal sourcing platform for M&A, private equity and consulting teams. It maps markets, builds target lists including off-market companies, and drafts client-ready materials, with every number tied to a verifiable source across 65M+ companies and 160+ markets.

Inven is an AI platform built for M&A and consulting work: mapping markets, sourcing companies, and producing client-ready deliverables from data that carries its source. The founders came out of McKinsey and BCG, where they spent their days hunting companies, untangling ownership and financial records, and rebuilding the same spreadsheets. Inven packages that repetitive layer into a queryable data foundation spanning 65M+ companies, 160+ markets and 3M+ transactions, and lets teams run research against it at scale. The product is organized around a handful of workflows rather than a single blank chat box. Market and competitor analysis maps a competitive landscape, sizes segments and flags adjacencies. Deal sourcing builds target lists at volume, including off-market companies that do not surface in traditional databases. Company materials preparation turns raw findings into market maps, strip profiles, decks and sheets. Agentic workflow automation lets a user set a brief and have repeatable research tasks run without supervision. Data enrichment fills gaps left by incumbent providers and outputs structured company data ready for models. The audience is specific: investment banking, private equity, consulting and corporate development teams, plus business brokers, search funds and venture capital. The pitch rests on defensibility. Every figure shows its source, private European financials link back to the filing they came from, and the AI is positioned as retrieving rather than inferring, so outputs can be defended in front of an investment committee or a client. Firms cited on the site include Deloitte, PwC, J.P. Morgan, Moelis and Oaklins, with more than 1,000 firms using the platform. Against general research assistants, Inven's advantage is domain structure and source traceability; against legacy data providers, it is coverage of private and off-market companies plus the deliverable layer on top. Pricing is not published, and the site routes interested teams to a demo rather than a self-serve signup.

How do you use Inven?

1Book a demo and scope your use case
Inven onboards through a demo rather than self-signup. Come with a live mandate or client question so the team can show the relevant workflow instead of a generic tour.
2Pick the solution that matches your seat
Separate pages cover investment banking, consulting, private equity and corporate development. Read the one matching your role to see which workflows and outputs are prioritized.
3Map the market and competitors first
Start with a market and competitor analysis to establish structure, segments and adjacencies. This universe then feeds later sourcing and materials work.
4Build the target list
Run the deal sourcing workflow to assemble a list at scale, including off-market companies. Check the source behind each entry before the list goes to a client.
5Automate repeatable research
Once a brief is proven, convert it into an agentic workflow so routine pulls and updates run without manual rework.
6Produce the client deliverable
Use company materials preparation to turn findings into market maps, strip profiles, decks and sheets in your firm's format, with data traceable to source.

Pros and cons

Pros

Workflows mirror real deal work: market mapping, target lists, company materials and enrichment are separate, purpose-built tasksAI
Source shown on every number, with private European financials linked to the underlying filingAI
Coverage extends to off-market companies that do not appear in traditional databases, a repeated point in customer quotesAI
Credible practitioner base (Deloitte, PwC, J.P. Morgan, Moelis, Oaklins) and case studies with named firmsAI
Founders come from McKinsey and BCG, so the product reflects analyst workflow rather than generic promptingAI

Cons

No published pricing, no self-serve signup and no plan comparison, so budget fit requires a sales conversationAI
Demo-gated onboarding makes it hard to evaluate data quality on your own before committing timeAI
Positioned around M&A and consulting use cases, so general market research or non-financial teams will find it overbuiltAI
Claims about 10x research speed come from vendor and customer testimonials rather than independent benchmarksAI
Financial depth is emphasized for private European filings; coverage outside that geography is not detailed on the siteAI

How much does Inven cost?

Features

Core workflows are market and competitor analysis, deal sourcing (including off-market companies), company materials preparation for decks, sheets and market maps, agentic workflow automation for repeatable research, and data enrichment. The underlying layer covers 65M+ companies, 160+ markets and 3M+ transactions, with a source shown on every number and private European financials linked to their filings.

Learn more

Frequently asked questions about Inven

How do you use Inven?
The walkthrough on this page covers 6 steps: 1. Book a demo and scope your use case 2. Pick the solution that matches your seat 3. Map the market and competitors first 4. Build the target list 5. Automate repeatable research 6. Produce the client deliverable.
What platforms does Inven support?
Inven is available on Web App.
What are the limitations of Inven?
No published pricing, no self-serve signup and no plan comparison, so budget fit requires a sales conversation. Demo-gated onboarding makes it hard to evaluate data quality on your own before committing time. Positioned around M&A and consulting use cases, so general market research or non-financial teams will find it overbuilt.