// Kalyan Autonomy
Systems That Run Themselves
We build autonomous systems that operate and assist the efficient running of industry and society. Identifying defects is the first step toward that.
How We Start
PHASE 01: PATTERNWherever we notice a pattern, and see enough volume, velocity and variety of data, we deploy intelligent systems to learn that pattern — or to verify whether the pattern exists at all. Then we build the digital or physical systems that use it to optimise or innovate.
You Have Big Data
YOU JUST DON'T KNOW ITIt does not need to be in a data lake to count. Whether it sits in a spreadsheet, behind a sophisticated software interface, or in the heads of a team with years of hard-won experience — we link it together using AI.
High Volume
Years of inspection photographs, maintenance logs and QC sheets that nobody has the hours to read.
A decade of archived defect images.
High Velocity
Lines and sensor feeds producing readings faster than any person could review them, continuously.
An FMCG line at hundreds of units per minute.
High Variety
Camera frames, spreadsheets, handwritten logs, sensor traces and expert judgement — none of it in the same format.
Thermal imagery, shift notes and vibration data on one asset.
Detect → Act
THE PROGRESSIONDetect
Vision models identify defects and anomalies as they occur, at speeds and scales manual inspection cannot reach.
Diagnose
We trace a defect back to its cause, correlating it against process data rather than only flagging the symptom.
Propose
The system recommends the corrective action — what to change, and when to intervene.
Act
Systems that close the loop and act on their own findings — running and maintaining themselves without a person in the path.
Beyond Defects
SYS_FEED: KINETIC_MANIFOLD_3DDefect detection is where we start, not where vision stops. The same perception stack that finds a micro-crack on a tool surface can follow how materials and people move across a factory floor, or show where a process quietly loses time. Anywhere there is a pattern and enough data to learn it, the approach holds.
The Team
KALYAN LABSVishwajeet Shukla
ID:001Aditya Joshi
ID:002Think you might have a pattern worth finding?
Bring us the data you already have — however messy it is — and we will tell you whether there is a system worth building on top of it.