Calibrated intervals instead of point guesses. Provenance on every answer. Systems that grade their own predictions and refuse to ship what they can't verify. Six open products, built and measured this way.
Runs on Jetson-class hardware in the ₹40,000 class, no data-center GPU required, and the platform underneath is engineered and hardened for national-scale transit and public-infrastructure operators, validated to production-grade edge requirements.
Every edge vision system eventually meets a scene it can't resolve: an occluded crowd, an ambiguous re-identification, a prediction that may or may not come true. Most systems paper over that with a confident-looking number. Dhi's systems are built to say "I don't know" in a specific, checkable way: a refusal gate that won't ship a degraded model, a calibrated interval instead of a bare estimate, a provenance trail on every answer, a ledger that grades a system's own alerts against what actually happened.
That's the differentiator we're building toward: not a bigger model, but a class of mechanisms (refusal, calibration, provenance, falsification) that make the system's confidence auditable instead of asserted. Below is what's shipped so far, with the honesty mechanism and one measured number per product, and a link to the code.
Each repo is open, tested, and benchmarked on synthetic ground truth today (see the footer note). One verified number per card; read the README for the full table.
Corrects a raw detection count for what the camera can't see, instead of reporting the visible-only number as if it were the true one.
Calibrated intervalsGround-plane position, height, and separation from a single fixed camera, self-calibrated from the person boxes already passing through frame, no LiDAR, no stereo rig.
Measured, not assertedA self-supervised recipe for thermal perception that is explicit about what's proven today versus what a GPU training run would still need to confirm.
No fabricated benchmarkdocs/COMPUTE_BUDGET.md instead of assumed · 33 tests.Causal, predictive alerting for early warning, paired with a ledger that checks every lead-time promise against what actually happened, after the fact.
Falsification ledgerLinks the same person or vehicle across cameras and answers where/when/who-was-there questions with a provenance trail on every answer, and refuses to link when it isn't confident.
Precision-first, provenance-carryingA compiler for vision models: describe the detector you want in plain English, get a deployed, benchmarked TensorRT engine, or a documented refusal, not a silent degradation.
Refusal gateTurns detector output into queryable relations plus a symbolic rule engine, with plain-language explanations, and a zero-dependency core so it runs on Jetson-class devices as-is.
View repo →Add a new alert class by text prompt or image crops, on the box, forward-only, no cloud retrain, no GPU training loop, no forgetting of prior classes.
View repo →Long-term identity across cameras from appearance, gait, and body shape and learned transit priors, deliberately with no face biometrics.
View repo →The products above plug into a single edge-analytics platform, not six separate stacks.
The platform also carries a base-kitchen food-safety build for regulated food-service operators: on-floor object detection, contamination-zone dwell alerts, camera-health monitoring, and P1/P2/P3 alert triage, shipped as config-driven handlers per the operator's food-safety spec. Pest and PPE detection in that build are model-gated: the code path is real, the detector weights are not yet installed; we say so rather than claim a capability that isn't running.
A portfolio of applied-systems papers in preparation, built from the same code and numbers on this page, not a separate marketing narrative.
Open code and reproducible benchmarks are the norm for every paper we ship, not an appendix.
Shorter technical write-ups on the same research track, published as versioned markdown on Hugging Face.