You cannot prompt your way out of fragmented data.
The reason the modules can be trusted: one resolved, current, thesis-aware view of every company, person, and fund your firm touches.
One knowledge graph: companies, people, and funds resolved into a single connected record.
Six reasons raw data breaks AI
Foundation models solved reasoning. They did not solve the data. Six structural problems sit underneath almost every wrong answer.
One company shows up as five scattered versions across your tools.
Sources disagree, and AI cannot tell which one should govern.
AI fills gaps by generalizing from public patterns, and passes guesses off as facts.
AI repeats an old number as if it were current, with no sense of history.
Analysis needs a taxonomy and a peer set that is never actually defined.
AI applies a generic lens, not your firm’s thesis and judgment.
From fragmented inputs to a record you can trust
Built bottom-up, the way the product does: from unstructured inputs to a resolved, standardized record with your judgment on top.
One company, not five scattered versions
Every role in the firm captures data somewhere different. Kruncher ingests all of it, from CRM, documents, transcripts, email, messaging, LinkedIn and your address book, plus 20-plus premium sources, in any language, and resolves it into one clean record linked in a knowledge graph of companies, people, and funds.
Raw data and documents in, IC-ready reports out
Kruncher ingests everything, standardizes it, and keeps a living profile of every company, continuously. The same pipeline runs behind screening, diligence, and monitoring, so every module reads from the same current record.
- ✓ Ingest all three layers: external data, your data rooms, your team’s decisions
- ✓ Standardize: unstructured to structured, reconciled and normalized
- ✓ A living profile: a time-series model, not a one-off snapshot
The 14-step ingestion process
Every document and data feed passes through the same pipeline before it becomes a data point you can trust.
Reads the world in its own language
Kruncher ingests 20-plus premium databases and public registries across the EU and beyond, plus documents in any language. Everything is normalized into structured English, with a link back to the original-language source, so your coverage never stops at the border.
- ✓ Multilingual ingestion, normalized to English
- ✓ EU and global public-registry coverage
- ✓ The original-language source is always linked
Unified Company Record
Ingest everything and resolve it into one clean record, linked in a knowledge graph of companies, people, and funds.
Source Precedence
When sources disagree, the latest and most authoritative one governs. Contradictions are resolved, not averaged.
Layered Record & Gap Detection
Labels each value by type (core data, premium, Kruncher estimate, your notes) and flags missing data instead of filling it. You always know a fact from an estimate.
Living Company Profile
A time-series model and the Time Machine: a full change history where every change carries its source and date. No stale number repeated as current.
Standardization
Unstructured to structured: roughly 1,000 normalized data points per company, with consistent peer sets so companies are actually comparable.
Your Lens (Adaptive Learning)
Thesis, deal score, and preference learning sit on top. A thumbs-up teaches the system what your firm values, so later analysis reflects how you decide, never from shared-model training.
30+ specialized agents, working in parallel
Kruncher is not a single prompt. Dozens of specialized agents each read one slice, decks, transcripts, filings, competitor sites, and extract traction, unit economics, team and category signals, then hand off to the next. Purpose-built for private markets, not a generic LLM wrapper.
Public data at the base, your judgment at the peak
Every analysis is built in layers. Public data forms the foundation, premium sources and your internal research sit above it, confidential data from the target company adds depth, your thesis focuses it, and your judgment sits on top. Each value is labelled by which layer it came from, so nothing is a guess dressed as a fact.
Every company, fully analyzed
One standardized report, built the same way every time so companies are comparable.
Scored the way your firm decides
Kruncher applies your thesis and up to 500 configurable signals, plus any you add, then returns a clear fit.
Track how a company changes over time
Every data point is stored as a time series, not overwritten. You see how a company evolves quarter over quarter, when each number changed, and what it was before, so you understand trajectory, not just today’s snapshot.
Five layers of data on one record
Kruncher holds far more than public facts. Each layer is labelled, so you always know where a value came from.
Not just data points. Relationships.
Kruncher turns every document into a knowledge graph that tracks companies, people, and investors, and the relationships between them. Those connections are what let it build accurate answers, not guesses assembled from scattered text.
A report specific to your firm, not the same one everyone gets
Kruncher learns from your past choices. Every IC decision, portfolio update, and piece of feedback sharpens your scoring, red-flag rules, and deal criteria, so the analysis reflects how your firm actually invests and gets sharper the longer you use it.
Everybody rents the same model
The model is a commodity. Your edge is the knowledge layer beneath it: unstructured data turned structured, entities resolved, everything standardized, and the relationships and intelligence that only your firm accumulates. That layer is what Kruncher builds and keeps for you.
The longer you run on Kruncher, the sharper it gets
Every override, saved view, and diligence note sharpens the proprietary intelligence inside your isolated tenant. Your firm’s pattern recognition, what makes a great founder, what predicts breakout traction, what kills a deal at IC, gets encoded over time. No shared learning across customers; your edge stays yours.
Proprietary interaction data accumulates only for you, never used to train shared models.
No black box
Follow any number in a report back to the document it came from. Here is how one data point gets built, step by step.
The documents highlight trends such as automation, modular APIs, embedded finance, and bank-fintech partnerships.
Click any number to open its source.
Every data point carries a timestamp and the document, filing, or URL behind it. Every claim opens back to its source.
Trust & Control
Isolated Azure tenant, on-prem option, and full JSON export. Never used to train shared models.
A CRM, built in
Kruncher is also the CRM where you track investors, people, and companies, and the deal flow between them, so your relationships live on the same record as the intelligence.
The same layer, available as an API
Everything here is accessible programmatically. Call one primitive, analyze a company or document against a thesis, and get structured, sourced, timestamped output. Connect Claude, ChatGPT, or your own agents through the MCP server, or query the knowledge graph directly.
Explore Build on Kruncher →What the engine runs on
Every output is traceable, sourced, and audit-ready
Kruncher outputs are evidence-linked and governed, so your team can trust them and use them consistently. This is the layer every module reads from.