Computer Vision · DRISHTI

Vision that perceives — CV1 image+text (Drik) · CV2 image (Drishti) · CV3 video (Rasa). Industry-agnostic.

IN DESIGN

The need it answers

A camera captures pixels, not meaning. Every industry is drowning in images — shelves, vehicle damage, roads, documents, faces — yet turning a frame into a decision still needs a human to stop and look. Computer Vision makes any image instantly actionable: graded, detected, compared, explained — industry-agnostic, real-time, with a human making the final call.

What it is

A horizontal computer-vision platform that turns images into measurable value across six industry domains. Built on the 4M SAI agentic layer with real-time reasoning, a domain-agnostic architecture, and human-in-the-loop decisions. Three MVPs are prioritized: Retail Out-of-Stock, Vehicle Damage, and Pothole detection.

By the numbersHow much does computer vision change the picture?

Out-of-stocks cost retailers about $1.2 trillion a year. Computer vision reads a shelf at 95%+ accuracy versus 60–70% by hand, and flags gaps in minutes instead of a 24–72-hour audit cycle — one architecture across six domains.

$0T

out-of-stock loss / yr

IHL Group 2025

0%+

CV shelf accuracy

vs 60–70% manual

industry domains

one architecture

keepers, real event

our POC

Manual audit 65%

Computer vision 95%

Dimension	⊘ Manual	◉ With CV	Gain
Accuracyshelf / SKU read	60–70%	95%+	+30 pts
Time to find gapsdetection latency	24–72 hrs	minutes	~100×
Coveragehow often	point-in-time	continuous	always-on
Σ Reachone pipeline	rebuild per use-case	configuration	6 domains · 3 MVPs

Market baselines, validated 2026-06-10. CV figures are domain-level; the photo-culling POC is our own proof point.

Sources: IHL Group — inventory distortion Vision Group Retail — CV vs manual audits

The evolutionHow it was distilled — and what shaped it

🌱 Seed

CV-graded photo culling for events — surface the best frames, grade Gold / Silver / Bronze.

← shaped by the manual drudgery of sorting thousands of event shots by hand.

🛤 Path

Built the full pipeline — ViT-L/ConvNeXt features → CLOVE semantic grading → FLOW orchestration → AXIOM gate; 107 keepers surfaced from a real event.

← shaped by proving the architecture end-to-end on one real use-case before generalizing.

🔀 Pivot

From a photography product to an industry-agnostic CV platform — the photo workflow is the POC, not the product.

← shaped by the realization that the same pipeline grades a shelf, a dented bumper, a pothole — domain is configuration, not a rebuild.

💎 Crystal

One architecture, six domains, three prioritized MVPs — Retail out-of-stock, Vehicle damage, Pothole/road.

← shaped by market research — where the sharpest automatable pain and willingness-to-pay sit.

⭐ Principle

Any image becomes actionable intelligence in real time, with a human making the final call.

← shaped by the north star — converge the evidence, let the human decide.

Where we stand todayBuilt & working

✓Vision locked; architecture defined across 6 domains
✓Three MVPs prioritized (Retail OOS, Vehicle Damage, Pothole)
✓Open-source stack chosen: YOLO, EfficientNetV2, CLIP, SAM2
✓4M SAI agents mapped: CLOVE, CLEAN, RAGA, FLOW, AXIOM
✓Market validated — multi-billion-dollar CV opportunity

What's nextOn the path

→Build MVP #1 — Retail Out-of-Stock detection
→Establish the common architecture shared by all MVPs
→Deploy Phase 1 to a public demo space
→Finalize dataset sourcing + fine-tuning per domain

★ the moonshot

An industry-agnostic platform that perceives images in real time, narrows infinite possibility to the likely few, and augments human judgment — reducing error, stress, and decision friction in any domain.