Some of the most important things about a person, a structure, or a system are never directly measured.
They are inferred — badly, or not at all. Cognifluenz builds the physics and the mathematics that make them measurable: real quantities, recovered from indirect and imperfect signals, delivered with a confidence bound you can defend.
What we do
Where labels are scarce, ground truth is unobservable, and a plausible guess is not good enough, generic AI fails. We work from the physics up — and when no existing sensor can see the signal, we design one that can.
Forward modeling & synthetic data
We model the measurement chain from first principles — geometry, propagation, absorption, motion, noise formation — and manufacture physics-accurate training data when reality will not supply it.
Sensor–algorithm co-design
The forward model tells you what the instrument should have been. We use it to specify the sensor — bands, optics, illumination, geometry — so the signal is acquired, not merely fought for downstream.
Model-based, interpretable AI
Inversion architectures where the network's structure is the physics — algorithm unrolling, learned regularizers, hybrid classical–learned pipelines. No black boxes where a number carries consequence.
Uncertainty & field robustness
Every estimate ships with a calibrated confidence bound — and is hardened against motion, drift, lighting, vibration, aging, and the long tail that separates a demo from a product.
Focus
We work on measurements with consequence — where a number triggers a payment, a certification, an intervention, or a safety decision, and interpretability and uncertainty are requirements rather than preferences.
Human state, made measurable
A body continuously modulates the light, heat, radio and sound around it. Those modulations carry real physiological information — cardiac and respiratory dynamics, autonomic activation, micro-motion — that today is either invisible or reachable only by attaching electrodes to a person.
We build the physics that recovers it without contact, and the instruments that acquire it cleanly. Our work is directed at situations where the answer matters: fatigue and impairment in drivers and operators, patient monitoring where a wire is impractical or unsafe, and the autonomic signals that precede a person's own awareness of their state.
These are physiological quantities, with physical models behind them and error bars attached — not guesses about what someone is feeling. That distinction is the whole discipline (see our principles).
Also active
Spectroscopic identification
Deciding what a detector actually saw — overlapping spectral signatures on a drifting background at low signal-to-noise — with the calibrated confidence that a safety or security decision demands.
Industrial imaging & NDT
Metrology-grade reconstruction from sparse, fast, limited-angle scans — recovering the accuracy that shrinking acquisition times take away.
Infrastructure measurement
Inferring load and hidden condition from indirect response — where the measurement carries legal and safety force.
Track record
Our founder's algorithms run inside shipped commercial products at four global instrument makers, across four different sensing physics. Each was published by the client under their own brand.
- A leading X-ray instrumentation OEMAutomatic classification and counting of crystal dislocations in silicon-carbide wafers — shipped in the client's X-ray topography analysis software.
- A global diagnostic ultrasound OEMReal-time 4D speckle reduction on GPU — shipped in the client's flagship ultrasound line.
- An industrial CT manufacturerOblique-angle iterative reconstruction for flat objects, derived from geometric first principles where no closed-form inversion exists — shipped for electronics inspection.
- A top-tier automotive supplierGNSS constellation and multipath signal simulator with receiver test bench — for automotive positioning validation.
Why physics wins here
Every AI company is running out of data. We generate it from first principles — for sensors, in physics, at scale — and invert it into a number someone will act on.
In measurement that carries consequence, the winning move is not more data and more compute. It is a knowable forward model, mathematics that can be explained to an auditor, an instrument designed for the signal rather than adapted to it, and engineering that survives the field. That is what we build — and what we own.
The overlap does not go away. It is the error you will make. The honest thing is to measure it, state it, and decide anyway. That is the job.
Principles
Working on human physiological signals demands that we say plainly what we will not do. These are commitments, not disclaimers.
What we will not build
- We do not infer emotions. The claim that a face or a voice reliably reveals anger, fear or deception is not supported by the science. We measure physiological states — arousal, fatigue, cardiac and respiratory dynamics — which have physical models behind them.
- We do not build workplace or classroom surveillance. Inferring emotional state in these settings is prohibited in the EU, and we consider it wrong regardless of jurisdiction.
- We do not ship a number without its uncertainty. A measurement that cannot state how sure it is has no business informing a decision about a person.
- We do not use black boxes where consequence attaches. If a clinician, an engineer or a regulator must act on our output, they must be able to see why.
Company & careers
Cognifluenz Deeptech Pvt Ltd is a DPIIT-recognised deeptech company in Trivandrum, India, founded by a signal-processing and inverse-problems specialist whose algorithms ship inside commercial products across ten sensing modalities.
Our conviction is that the hardest problems in sensing are solved by small groups of exceptionally capable people going deep together — not by scale. We are building that group.
If you are a physicist, mathematician or engineer who wants to derive a forward model on Monday, design the instrument around it on Wednesday, and see the result inside a real product — write to rajeev@cognifluenz.com. Tell us about a problem you could not stop thinking about.
Contact
General enquiries and partnerships: ceo@cognifluenz.com
Technical and research: rajeev@cognifluenz.com
Swatantra, ICFOSS, Thiruvananthapuram, Kerala 695581, India