3.95%
best MAPE on held-out data
Property valuation, validated on a temporal holdout split.
The prediction platform
where trust meets insight.
Finally — anyone can get forecasts grounded in the facts of their business. Property prices, freight quotes, crop yields, solar output and more, each with a verified confidence interval — through one REST API.
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17 prediction APIs
9 domains
0.9 calibrated coverage
7ms avg response
dryapi.app — Prediction Studio API online
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POST /predict — request
{
"model": "property/valuation/v1",
"features": {
"sqft": 1850,
"beds": 3,
"baths": 2,
"zip": 90210
}
} Sale price forecast — 90% confidence band
{
"value": 615800,
"interval": [560400, 671200],
"confidence_interval": {
"lower": 560400, "upper": 671200, "coverage": 0.9
},
"latency_ms": 4.5,
"top_factors": [
{ "feature": "sqft", "impact": "+73.1%" }
{ "feature": "beds", "impact": "+21.4%" }
{ "feature": "location", "impact": "+5.5%" }
]
}
Trusted by sectors that run on forecasts
17
Prediction APIs
9
Industries
0.9
Calibrated coverage
<10ms
Median latency
Connect your data, then predict on it
Stripe Shopify HubSpot Salesforce Google Sheets GitHub Notion Slack Airtable Pipedrive
Why calibrated
Every prediction ships with a confidence band.
A point forecast is a guess with extra steps. DryAPI returns a verified interval — a 90% confidence band measured on held-out data, not an approximation.
Every forecast is calibrated on held-out data before it ships, and re-checked as new data arrives — the number you get is the number you can act on.
response — property/valuation/v1
{
"model": "property/valuation/v1",
"value": 512400,
"interval": [468900, 559200],
"confidence_interval": {
"lower": 468900,
"upper": 559200,
"coverage": 0.9
},
"confidence": { "level": 0.9, "score": 0.93 },
"latency_ms": 7,
"top_factors": [
{ "feature": "sqft", "impact": 0.42 },
{ "feature": "zip", "impact": 0.31 },
{ "feature": "beds", "impact": 0.18 }
]
}Self-serve
Predictions in the tools your team already uses.
Drop a question into Slack or Discord, get a forecast with the confidence band attached — no dashboard tour required.
D dryapi · predictions
J
Julia 4:02 PM@DryAPI can you forecast next quarter's freight costs for the west-coast
corridor?
D
DryAPI 4:02 PMSure — a 90% interval for LA → Seattle, based on 18,400 historical shipments.
freight/quote/v1 — quarterly forecast
$5,180
point forecast
$4,900 – $5,460
90% interval
0.91
calibration
Q6_west_coast_forecast.json
D #forecasts
M
maya Today at 4:02 PM@DryAPI what's the LA → Seattle freight forecast for Q6?
D
DryAPI APP Today at 4:02 PM90% interval on 18,400 shipments — $4,900 to $5,460.
freight/quote/v1 — quarterly forecast
$5,180
point forecast
$4,900 – $5,460
90% interval
0.91
calibration
Q6_west_coast_forecast.json
Benchmarks
Numbers we can prove on held-out data — not marketing slides.
Calibration is a contract. Every published metric is measured on data the models never saw during training.
3.95%
Best MAPE
Property Valuation
0.988
Best R²
Property Valuation
7ms
Avg response
Across all APIs
90%
Interval coverage
Verified out-of-sample
Calibration curve
out-of-sample
Always fresh
Models retrain daily. Drift never gets a vote.
New data flows in every night, every model in the catalog refreshes each morning, and drift is re-measured against the latest holdout — so the interval you act on stays calibrated.
- 00:00 Ingest 48 feeds listings · permits · sales
- 06:00 Retrain 17 models refit + recalibrate
- 06:12 Hot-swap registry zero-downtime serve
Production-ready
The guarantees your forecasts run on.
Uptime, latency, and support — the numbers behind the number. Your forecasts ship on an API that's always warm and always monitored.
99.9%
Uptime
Monitored around the clock
7ms
Avg response
No queues, no warm-ups
24/7
Support
Email + priority SLA
0 ops
For you
We run the models
The proof
Numbers we can stand behind.
Every model is measured on data it has never seen, recalibrated daily, and shipped with the confidence band attached — no marketing slides.
Property Freight Solar AgTech Commerce Logistics
90%
interval coverage out-of-sample
Every prediction ships with a calibrated confidence band — you see the uncertainty, not just a number.
Daily
retrain cadence across all 17 models
Models are re-fit on fresh data every day and hot-swapped into the registry to keep drift in check.
The catalog
17 endpoints. One contract.
Every endpoint below returns the same shape — a point forecast, a calibrated interval and the top factors behind it — through one REST API and one API key.
Property
4property/valuation/v1Estimate a home's market value from its characteristics and location.property/rental/v1Predict fair monthly rent for a property in any market.property/risk/v1Assess flood, fire, and disaster risk for a property.property/investment-score/v1Score a property's attractiveness as an investment.
Construction
3Agriculture
3Energy
2Vehicle
1Freight
1Delivery
1Shipping
1Marketing
1Integration
Ship in your language.
First-class SDKs and a single REST contract across the entire catalog. Copy the call, paste your features, get a calibrated answer.
predict.py
from dryapi import DryAPI
api = DryAPI("pk_live_...")
res = api.predict(
model="property/valuation/v1",
features={"sqft": 1850, "beds": 3, "baths": 2, "zip": 90210},
)
print(res.value) # 512400
print(res.interval) # [468900, 559200]
print(res.confidence_interval) # {lower: 468900, upper: 559200, coverage: 0.9}
print(res.top_factors) # [sqft +42%, zip +31%, beds +18%]Getting started is easy.
One account, one credit balance, one POST. Pick an API, pay as you go, and get a calibrated answer in milliseconds.
Tabular forecasting across nine domains — property values and rents, vehicle prices, freight quotes, delivery ETAs, solar output, crop yields and more. Every endpoint returns a point forecast, a calibrated confidence interval and the top contributing factors.