Company
We are building the Mortgage Intelligence Platform.
Mortgage is one of the most information-dense financial systems in the economy. A single mortgage can involve hundreds of pages, multiple institutions, changing policy, dozens of calculations, and years of downstream obligations. Yet most technology still treats those inputs independently. We are building an intelligence layer designed to understand them together.
- Source
- Fact/Context
- Policy
- Calculation
- Decision
- Evidence
Why mortgage
Every decision in this industry is a judgment about a stack of documents
Whether to lend. Whether to buy the loan. What to service, and on what terms. What sits inside a security. Whether a file survives an examination in three years. Each of those is a question about what a few hundred pages actually say, and each is answered today by a person reading a fraction of them against a policy that keeps moving.
That is the layer the industry has never had: something that reads mortgage documents, understands what they assert, knows the policy that applies, and can prove the answer to whoever asks later. It serves the originator, the aggregator, the investor, the servicer and the auditor, because they are all asking questions of the same documents.
The problem with document-only intelligence
Reading a value and understanding a mortgage are different problems
Document reading is now a capability anyone can buy, and it improves across the whole industry every quarter. A system can be excellent at the questions on the left and structurally incapable of the ones on the right, and from a demonstration the difference is invisible. It shows up later, in what escapes.
Document intelligence
Questions about a page.
- What document is this?
- Where is the value?
- What text is on the page?
- What field was extracted?
Mortgage Intelligence
Questions about the mortgage.
- Which value actually governs?
- Which policy applies on the relevant date?
- Should two sources agree?
- What is missing?
- Is the difference legitimate or a defect?
- Which calculation should run?
- What action follows?
- What evidence supports the conclusion?
Why QC first
The first review by someone with no interest in the deal closing
A defect in a loan file is usually described from one end of the chain: a repurchase demand, an audit finding, a cost. The same defect at the other end is a borrower whose income was assessed incorrectly, a disclosure that arrived late, or a loan that should not have closed on those terms. The borrower has the most at stake in the file being right and the least ability to check any of it.
Post-closing quality control is one of the few reviews performed by someone with no direct interest in the transaction having closed. It is also where the checking already happens, where the answer is already supposed to be evidenced, and where the work is measurable. It is the first application, not the ambition.
The platform thesis
One intelligence layer, and the applications that earn their place on it
The same mortgage is examined at every stage by different teams for different reasons. The application changes. The need for coherent, evidence-backed understanding of the file does not, so the platform is built once and each application is built on it.
- Origination
- Underwriting
- Closing
- Quality controlFirst application
- Delivery
- Servicing
One shared intelligence substrate
- Documents
- Mortgage data
- Policy
- Rules
- Calculations
- Evidence
- History
- Exceptions
Where we are
Current stage, stated with the same status vocabulary as the products
- TodayAvailable for evaluation
Quality control, on real files
Whole-file QC with versioned rules and evidence-linked findings, available for evaluation on sample, synthetic or de-identified files.
- In developmentIn development
Deployment inside your own AWS account
Files and the processing that reads them stay inside your boundary and inherit the controls you already operate. Not available yet.
- Being built
Deeper mortgage reasoning across the file
Knowing which value a program actually wants, telling a legitimate difference from a defect, recognizing what should be in the file and is not, and explaining an exception well enough to act on.
- DirectionPlanned
Underwriting, Closing, Delivery; Servicing in research
Each application earns its place by demonstrated capability on real files, not by appearing on a roadmap.
We turn complex loan files into explainable mortgage intelligence, helping teams identify issues earlier, review consistently, and retain the evidence behind every finding. The platform combines AI-assisted document understanding with governed mortgage intelligence to identify issues, explain findings and support consistent human review. Every finding is designed to remain traceable to the underlying evidence and the applicable mortgage requirements.
How we build responsibly
Six principles, each one checkable on the site
Current and roadmap are always distinguishable
Every application carries one of five statuses, set by product rather than marketing. A roadmap item is never described in the present tense.
Customer documents are not training data
They are not used to train any model, ours or any provider’s. Any use of customer data for model training would require a separate written agreement signed knowingly.
No performance figure without the method
An accuracy percentage is publishable only alongside a stated benchmark, a stated document set and a comparison anyone can reproduce.
A named person makes the decision
The system produces findings and evidence. A reviewer reaches the disposition, and that is recorded. This is a design position, not a gap.
No implied endorsement
Encoding a published requirement is not endorsement by, affiliation with, or certification from Fannie Mae, Freddie Mac, HUD, the CFPB or any other agency.
Evidence as a by-product
The record that answers an examiner eighteen months later is produced by doing the review, not written afterwards to satisfy them.
Leadership
Who is building it
Shailesh Bhujbal
Co-FounderNearly three decades in fintech, building document and data platforms at scale across mortgage finance, cloud infrastructure, international development and industrial engineering. The problem has been the same throughout: turning documents into systems whose output can be relied on, in environments where the record has to survive examination years later. This is the continuation of that.
Talk to us about your files.
A working session on a synthetic file, or de-identified files of your own. No obligation and no procurement process required.