More time for
the right deals
Deal sourcing, matching and data room analysis for M&A advisors and venture capital funds. So experience goes where it counts instead of burning up in pre-screening.
References in this industry
What we build for this industry
Deal sourcing & screening
Automated coverage of the sources that matter, scored against your investment thesis, delivered as a curated digest rather than a database nobody searches.
- Source monitoring & scraping
- Scoring against your thesis
- Weekly digest
- CRM integration
Matching & advisor dashboard
Buyer and seller profiles captured in structure, scored against each other, with enquiries assigned to a mandate on the record.
- Structured profiles
- AI-assisted matching
- Enquiry queue
- Mandate assignment
Data room & document analysis
Structured filing with automated processing, plus questions in natural language answered with a citation down to the passage.
- OCR & document pipeline
- Questions in natural language
- Answers with citations
- SharePoint integration
Confidentiality & architecture
Tenant separation, least-privilege access and logging that holds up under scrutiny.
- Strict tenant separation
- Least-privilege access
- Complete audit logging
- Data classes before model choice
What actually gets in the way
The problems we hear in this field, before anyone mentions a technology.
Hundreds of candidates, one closing
Most drop out within minutes. Those minutes tie up exactly the people whose judgement decides it.
The knowledge sits in people
Which buyer fits depends on individual advisors. When someone leaves, market access goes with them.
The data room outgrows the overview
Thousands of pages per transaction, spread across folders and inboxes.
No evidence, no statement
What nobody can check against the original is no basis for anything you tell a client.
Discretion is not a setting
A deal that surfaces too early is damaged. That includes where data goes for processing.
Deadlines ignore your workload
Whatever is coordinated by hand breaks when several mandates peak at once.
What we have built here
Automated Deal Sourcing for Venture Capital
AI-powered sourcing pipeline and chief-of-staff system for a Food & AgTech VC - 40 accelerators evaluated automatically every week.
AI-Powered M&A Advisory Platform
AI-enhanced platform transforming mergers & acquisitions advisory workflows. 15h time saved per deal, 120% advisor efficiency gain.
M&A Process Digitalization
Analysis and digitalization of internal M&A processes for an advisory firm with actionable roadmap and digital workflow design.
AI-Powered M&A Matching Platform
B2B2B matching platform for company acquisitions with AI-driven buyer/seller profiling and automated deal workflows.
Have a project in mind?
Tobias
Let's Talk →Writing on this industry
AI in Due Diligence: Citations Matter More Than the Model
Document analysis speeds up due diligence considerably, but only when every answer stays traceable to its source. What that means for the architecture.
Deal Sourcing: The Reasoning Matters More Than the Score
Automated sourcing finds candidates faster than a team can review them. What decides the value is not the number of hits but whether the shortlist can be justified.
AI in Transactions: The Data Question Comes Before the Model Question
A model that sends a confidential memorandum to the wrong provider is unusable regardless of its quality. How that shapes the architecture.
Frequently Asked Questions
No. Judging whether a company fits stays with people who have experience and market access. What automation delivers is a narrower funnel: instead of reviewing a hundred candidates superficially, your team reviews ten thoroughly that survived a reasoned pre-screen.
We settle the data classes before choosing a model, not after. Depending on sensitivity, processing runs through a provider with a data processing agreement and European endpoints, or through a model on your own infrastructure. Alongside that come strict tenant separation, least-privilege access and logging that shows who saw which document and when.
Only if they are evidenced, and that is how we build them. Every answer points at the passage it came from, with a link to the document. If the system finds nothing, it says so rather than composing something. In due diligence an uncheckable statement is not a relief but a risk.
Yes, and then the professional privilege layer is added. We have built platforms where the separation between operating company and mandate-holding firm reaches into the architecture - separate tenants, separate access paths, and a settled answer to whose name a generated document carries when it goes out.
A sourcing run or a document analysis typically reaches a productively usable state in four to six weeks. We deliberately start with a slice - one source, one mandate type - and extend from there, rather than building for a year and discovering at the end that the scoring logic does not match your thesis.
They stay, as a rule. We connect to what is there - CRM, SharePoint, data room - instead of replacing it. For one fund the output went to Affinity, BigQuery and Slack; for an advisory firm it ran through SharePoint and Power Automate. A tool that creates a second data silo makes the problem worse.