Formerly
Now
Raven
Raven
Frontline Visual Intelligence
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Raven · RV-001
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2026.04
From pixels to intelligence.
Raven is Graylark’s frontline visual intelligence platform. It reads pixels, identifies signals, and returns actionable leads in seconds — no metadata required.
Scroll to learn more about RavenRaven
No metadata required — a single image is enough.
§Product demonstration
From one photo to a pinpointed address.
A walkthrough of the Raven workflow — narrowing a single image from a region to an exact street. No metadata, no landmarks.
§01The Raven platform
Every pixel carries a clue.
Raven is Graylark's visual intelligence platform. We train AI to understand the world — so it can read every detail in an image and tell you what it means.
What would take hours of manual review returns as high-confidence intelligence in seconds.
Raven · Live geoestimation
Graylark · Raven core
INPUT · LOW-CONTEXT IMAGE
0.81foliage species
0.74sky gradient
0.69distant signage
0.58curb · concrete
0.52light angle
0.48pavement texture
INFERRED LOCATION
COORDINATE
30.2672° N
-97.7431° W
REGION
Austin, TX
RADIUS
1.2 km
NEIGHBORHOODS
- South Lamar 94%
- Zilker 87%
- Bouldin Creek 81%
- Barton Hills 76%
No metadata · no landmarks · no text
Every pixel carries a clue.
Fragments in. Intelligence out.
§ Capabilities · Today
One platform. Many answers.
Raven reads a single image and returns intelligence an investigator can act on — geolocation, street-level targeting, vehicle ID, and image authenticity — with more always in deployment.
NO METADATA · NO LANDMARKS · NO TEXT
A
B
C
Where
Geoestimation
South Lamar, Austin · 94%
§ 01 →
Which
Street Targeting
1204 Westmoreland Blvd · ±3 m
§ 02 →
What
CAR-ID
2024 Ford F450 · 90% match
§ 03 →
§01Find Region·Geoestimation
01
No landmarks. No metadata. Just a region.
Geoestimation reads environment, structure, and context from a single image, returning ranked, high-confidence regions in seconds.
One frame in. A city out.
Input
No metadata
Single frame · no landmarks · no EXIF required
Raven · Geoestimation
Vision transformer decomposes the frame into vegetation, soil, architecture, sky — inferring a region directly from those signals.
Model view
Direct Inference
$ downsampled · luminance · edges
Returned in seconds
Live result
§02Find Street·Street Targeting
02
Built for low-context images.
Street Targeting delivers meter-level precision even when clear landmarks are absent, narrowing broad regions to exact locations in seconds.
Input
Phone photo
Parked vehicle · residential street · no address in frame
Raven · Street Targeting
Refines a low-context image into a precise, real-world location using visual signal alignment.
Model view
Direct Inference
$ downsampled · street features
Top prediction confirmed
Top prediction · confirmed
§03Identify Car·CarID v2.0
03
Make. Model. Year. From a single image.
Analyzes partial visual signals across interior and exterior (body lines, materials, trim, controls, lighting) and compares them against a learned vehicle representation to identify make, model, and year with ranked candidates.
It returns ranked candidates scored by visual similarity, enabling fast and reliable identification.
Input
Partial · blurred
Rear bumper corner · blurred · no full-body view
Raven · CarID v2
Compares visual features to surface the closest vehicle matches — make, model, and year.
Model view
Vehicle ID Model
$ input → vehicle signature
Ranked by visual similarity
§04Verify Image·AI & Deepfake Detection
04
Real, manipulated, or AI-generated?
Distinguishes genuine photos from AI-generated images, and catches deepfake or face-swap edits on real frames. When the image is synthetic, Raven names the most likely generator and ranks the rest by per-tool confidence.
Beta preview · accuracy is not guaranteed
Beta preview
Verify · matches
Uploaded image
110625-A-YG824-012.jpeg
Likely AI-generated
Signals consistent with a generative model. Closest match: Other.
Face manipulationFace-swap / deepfake signal
Deepfake (face swap)
2%
AI generatorsPer-tool confidence
Other
92%
Wan
2%
Qwen
1%
Imagen
1%
Kling
1%
DALL-E
1%
Grok
0%
Higgsfield
0%
Z-Image
0%
GAN
0%
FLUX
0%
Midjourney
0%
Adobe Firefly
0%
Beta preview · informational only · verify results independently
§05Case management
05
Case management, built for visual investigations.
Every search, pin, and source lives inside a shared case. Geolocations, vehicle IDs, and operator annotations pile onto one map.
Raven measures the distance between them, clusters the confident ones, and surfaces the leads that matter — automatically.
Auto-clustered. No manual correlation.
Sources · 6 added
Live case
Miami, FL — Find Region
94%
2019 Honda Civic Si
87%
Home — S. Biscayne Dr
fixed
Little Havana block
89%
Target address
fixed
Anonymous · street observation
med
$ fragmented · mixed sensors · ungrouped
Raven · Case Intelligence
Measures distance between sources, scores proximity and confidence, and surfaces the tightest clusters — as the case grows.
Leads · 3 surfaced
Auto-clustered
1
1423 SW 12th Ave · Little Havana
2 sources · 8 m apart · visual + vehicle
High
2
Biscayne corridor
3 sources · 142 m cluster · mixed
Medium
3
Target address · standalone
1 operator pin · unsupported
Low
$ ranked · distance × confidence × recency
One map. Every lead.
A new name
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Effective 2026.04
A safer world has no blind spots.
Raven is available to verified agencies and investigative teams. Book a demo to see it run on your own imagery.
