Point a frontier model at a PDF and you get something plausible. Valara is purpose-built for collateral review: visually grounded in the actual file, benchmarked against human reviewers, and structured so your systems and your underwriters can trust the result.
A capable agent (Claude Code, Codex, or an in-house build on a frontier model) can read an appraisal and write plausible notes. Getting to something an underwriter can actually rely on is a different problem: findings that resolve to the page they came from, output your systems can consume unchanged, coverage of the rules that govern the file, and results consistent enough to build a process around. That is the work below.
Valara parses your appraisal PDF and reasons over the rendered pages, so a finding points at the exact region it came from. A general agent can read the file too, but its claims rarely resolve back to a spot you can open and check.
Each issue links to the exact page, table, and comparable it came from. Your reviewer clicks through to the source, and the citation goes straight into the loan file. Auditable, not just plausible.
Valara's output is measured against expert reviewer scores across graded files, so quality is something we test, not assert. Point a general model at the same file and you are trusting it with no domain benchmark behind it.
Reviews come back as typed JSON and XML (and PDF, Markdown, HTML) your systems can ingest and your underwriters can file. Every field, every time. Not whatever format a prompt happened to return.
The same file produces the same review (content-addressed), run through a multi-step workflow with retries. A one-off prompt varies from run to run, and from one reviewer to the next.
Your documents are processed in isolated environments and are not used to train AI models. Encrypted in transit and at rest.
Every review is validated against a published schema, so the same fields arrive every time: a risk level, a quality score, an escalation recommendation, and findings that carry their citations. Drop it into your LOS, your QC queue, or your data warehouse.
{
"review_type": "commercial",
"risk_level": "HIGH",
"appraisal_quality_score": 2.5,
"recommended_escalation": "ESCALATE",
"critical_issues": [
{
"title": "Unsupported cap rate",
"severity": "HIGH",
"approach": "INCOME",
"references": ["p.42 Income Approach", "Table 7"]
}
]
}{
"review_type": "commercial",
"risk_level": "HIGH",
"appraisal_quality_score": 2.5,
"recommended_escalation": "ESCALATE",
"critical_issues": [
{
"title": "Unsupported cap rate",
"severity": "HIGH",
"approach": "INCOME",
"references": ["p.42 Income Approach", "Table 7"]
}
]
}| A general AI agent | Valara | |
|---|---|---|
| Source of truth | The file you hand it, plus its training data | Your appraisal PDF, parsed and grounded to the page |
| Grounding | May cite, but references drift and rarely resolve | Every finding resolves to the exact page, table, and comparable |
| Domain rules | General knowledge of USPAP and the GSEs | Codified USPAP, Fannie, Freddie, FHA, and UAD checks |
| Output | Whatever format the prompt returns | Schema-validated JSON, XML, and a filable PDF, every time |
| Accuracy | No domain benchmark behind it | Measured against expert reviewer scores |
| Consistency | Varies prompt to prompt, run to run | Same file, same review |
| Who maintains it | You own the prompt, the eval, and the upkeep | A team tuning it to your files and standards |
| Your data | May be retained or trained on | Isolated; never used to train models |
Early customers get white-glove onboarding: we tune Valara to your files, your standards, and the checks your shop cares about, with direct access to the people building it. That is not something you get by pointing a general model at a folder of PDFs.
Run a sample review, or bring a file of your own.
$250 value, no credit card required.