Ground truth fromyour CRE documents.
Kernel transforms complex CRE documents into trusted, cited knowledge your team can act on with confidence.
Your operations run ondocuments your AI can't use.
CRE documents are dense, cross-referential, and full of the kind of complexity that breaks every general approach.
Teams can get a demo running in weeks. Getting it to a place you'd actually trust takes months, and most never do. Fix one thing and something else breaks. The pipeline is fragile — and the gap between "good enough to demo" and "reliable enough to trust" never seems to close.
Foundation models and generic extraction tools read a short, clean document fine — in isolation. They fall apart on operating agreements, regulatory filings, or leases with amendment chains, exhibits, side letters, and definitions that span 200 pages.
No provenance. No confidence score. No way to defend a number when someone asks where it came from. So either a human checks every output by hand, or the output doesn't get used at all. Both outcomes defeat the point.
Internal pipelines work for one document type. They don't generalize. Each new type means a new schema, another round of prompt engineering, another regression risk. The roadmap keeps stretching while the backlog fills.
Learn what's really slowing down your AI roadmap.
One trusted knowledge layer.
Three ways teams use it.
Kernel turns your documents into structured, verified, cited knowledge your people, systems, and agents can rely on at scale.
Enable AI Workflows
Most CRE firms have the models, the budget, and the mandate. What they're missing is a reliable way to get structured, verified data out of the documents those models need to reason about.
Frontier models weren't built for 300-page appraisals, leases with 47 defined terms, or servicing agreements with cross-referenced amendments. Feeding raw documents into a general-purpose model produces demo-grade output — plausible, inconsistent, and impossible to defend.
Kernel sits between your documents and your AI layer — extracting, normalizing, and attaching provenance to every output.
Copilots, agents, dashboards, workflows: yours. The document intelligence layer underneath them: ours.
Agent
Agent
Agent
Accelerate Document Review
Underwriting a deal means reading the appraisal, abstracting the leases, reviewing the servicing agreement — and reconciling numbers across documents that don't always agree. The information is there. The bottleneck isn't finding it. It's verifying it fast enough to trust it.
Kernel surfaces what matters with its source location. Clauses extracted and tagged. Discrepancies flagged before they become surprises.
Your reviewers spend their time on judgment, not on search.
Build Proprietary Datasets
Your firm receives hundreds of high-value documents every year. The data inside them reflects your markets, your collateral, your deal flow. No third party can sell it to you — because no third party has it.
What third-party providers know is public. What your archive knows is yours alone — what you underwrote, what the market believed at the time, how your collateral performed.
That's a proprietary data advantage waiting to be unlocked.
| field | value | type | conf |
|---|
Ready to see it on your documents?
An uncited answer is a
hallucination waiting to happen.
Kernel ties every response to the exact document, page, and passage it came from. Grounded inputs mean your agents reason accurately, take the right actions, and produce output you can stand behind.
However you work, Kernel works.
Start in the platform, or integrate via API. Either way, you're moving in days, not months.
For Business Users
Review and verify in a purpose-built interface.
Upload documents. Review outputs. Verify evidence. Export trusted knowledge. No engineering required — the entire workflow lives in one place.
- Upload any CRE document — lease, appraisal, loan file, or OM
- Extracted facts appear with citations linked to source text
- Reviewers verify, flag, or override outputs before export
- Export clean, structured data to downstream tools
555_Main_Street_Lease_2024.pdf
Extraction complete · 847 pages
| Fact | Extracted Value | Confidence |
|---|---|---|
| Base Rent | $42.50 / SF / yr | High |
| Lease Commencement | January 1, 2024 | High |
| Lease Expiration | December 31, 2034 | High |
| Tenant Name | Acme Holdings LLC | High |
| Free Rent Period | 6 months | Medium |
Built for the complexity CRE actually has.
Pipeline,
Not Prompt
Most extraction systems are model calls wrapped in software. Kernel is a document processing and knowledge extraction pipeline with deterministic, cached, replayable stages — LLM bounded to the last mile. Each fact is its own retrieval-scoped job: adding new fact types doesn't reprocess your corpus, and a change to one fact never regresses another.
Domain Knowledge
Encoded
The challenge is not reading documents. The challenge is understanding what matters. Kernel embeds CRE-specific knowledge directly into the extraction process: field definitions, document awareness, business context, edge cases, and relationships. The result is production-grade output rather than demo-grade output.
Provenance
Required
Every extracted fact includes supporting evidence: source references, page locations, bounding boxes, confidence scores, supporting references. Because knowledge you cannot verify is knowledge you cannot trust.
See what production-grade extraction looks like in your workflow.
See Kernel on your documents.
Pick a document type, share your schema, and we'll deliver production-quality, cited, accurate data from your documents. Your team sets the accuracy benchmark. You make the call at day 30. No system integration required.