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<!doctype html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width" />
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<title>Dhi Labs: honest edge vision AI</title>
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<link rel="stylesheet" href="style.css" />
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</head>
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<body>
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<div align="center">
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<h1>Dhi Labs</h1>
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<p><strong>Edge native video analytics engineering, built to say "I don't know" instead of guessing.</strong></p>
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</div>
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<h2>Who we are</h2>
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<p>Dhi Technologies builds video analytics software that runs directly on Jetson class edge
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hardware, close to the camera, rather than shipping frames to a cloud model. The engineering
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identity is honesty as a feature: every component here quantifies its own uncertainty and is
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built to refuse an answer it cannot support instead of guessing.</p>
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<hr />
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<h2>Honesty as a feature</h2>
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<table>
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<tr>
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<td width="50%" valign="top">
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<h3>Refusal gates</h3>
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<p>Components decline to answer when the input falls outside their calibrated support,
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instead of emitting a confident guess.</p>
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</td>
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<td width="50%" valign="top">
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<h3>Calibrated confidence</h3>
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<p>Where a product reports an interval or a confidence score, the coverage of that
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interval against ground truth is itself measured and reported, including when it is
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imperfect.</p>
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</td>
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</tr>
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<tr>
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<td width="50%" valign="top">
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<h3>Falsification ledgers</h3>
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<p>Predictive components log what they predicted, what actually happened, and whether the
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prediction was fulfilled or falsified, rather than only surfacing hits.</p>
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</td>
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<td width="50%" valign="top">
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<h3>Stated limitations</h3>
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<p>Every dataset and demo states plainly whether its numbers are synthetic or real
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world, and what has not been checked yet. No claims of state of the art or foundational
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status, only what was actually measured.</p>
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</td>
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</tr>
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</table>
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<p>Two more things follow from the same policy: no SOTA claims, no customer names, and no
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deployment claims (this org is a public research and engineering surface, not a sales page),
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and every benchmark below is synthetic first and disclosed as such, so every claim can be
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checked exactly while real world validation is still in progress.</p>
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<hr />
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<h2>Products and evidence</h2>
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<p>Six products, each with a benchmark dataset built from procedurally generated ground truth
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and a live static demo Space you can exercise in the browser.</p>
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<table>
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<thead>
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<tr>
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<th align="left">Product</th>
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<th align="left">What it does</th>
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<th align="left">Demo</th>
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<th align="left">Benchmark dataset</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td><strong>Amodal Counting</strong> (A4)</td>
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<td>Visibility corrected counting through crowds and occlusion: a calibrated interval
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instead of a bare point count.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/amodal-counting-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/amodal-counting-benchmark">Dataset</a></td>
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</tr>
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<tr>
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<td><strong>Multicam Reasoning Memory</strong> (E1)</td>
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<td>Cross camera identity linking with transit time priors and weeks scale bounded
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memory; every answer carries provenance, and it refuses to link when unsure.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/multicam-reasoning-memory-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/multicam-reasoning-memory-benchmark">Dataset</a></td>
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</tr>
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<tr>
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<td><strong>Causal Predictive Alerting</strong> (E4)</td>
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<td>Predicts an incident seconds before it happens from kinematic trajectories, and
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proves why via counterfactual replay and a fulfilled or falsified ledger.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/causal-predictive-alerting-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/causal-predictive-alerting-benchmark">Dataset</a></td>
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</tr>
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<tr>
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<td><strong>Fixed Camera 3D</strong> (A3)</td>
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<td>Turns an ordinary fixed camera into a metric 3D sensor by self calibrating from
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people walking through the scene: no GPU, no model weights.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/fixed-camera-3d-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/fixed-camera-3d-benchmark">Dataset</a></td>
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</tr>
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<tr>
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<td><strong>Thermal Perception</strong> (A5)</td>
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<td>A radiometric data engine and self supervised pretraining harness for thermal
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native perception, with CPU verifiable math ahead of any GPU pretraining run.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/thermal-perception-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/thermal-perception-benchmark">Dataset</a></td>
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</tr>
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<tr>
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<td><strong>Prompt2Model</strong> (B1)</td>
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<td>A language guided vision model factory: prompt to dataset to trained model to a
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calibrated, quantized, ONNX exported artifact, with an accuracy floor refusal gate.</td>
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<td><a href="https://huggingface.co/spaces/Dhi-Technologies/prompt2model-demo">Space</a></td>
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<td><a href="https://huggingface.co/datasets/Dhi-Technologies/prompt2model-examples">Dataset</a></td>
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</tr>
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</tbody>
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</table>
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<p>Everything above, plus the blog dataset, is indexed in one place: the
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<a href="https://huggingface.co/collections/Dhi-Technologies/dhi-labs-honest-edge-vision-ai-6a4eb297cbd60f5f673cc2d7">Dhi Labs collection</a>.
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Browse <a href="https://huggingface.co/Dhi-Technologies?type=dataset">all datasets</a> or
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<a href="https://huggingface.co/Dhi-Technologies?type=space">all Spaces</a> directly.</p>
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<hr />
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<h2>Writing</h2>
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<p>Four technical posts, each grounded in the committed repo numbers. Hugging Face has no
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public Posts or Articles API, so these live as versioned markdown in the
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<a href="https://huggingface.co/datasets/Dhi-Technologies/blog">blog dataset</a>.</p>
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<ol>
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<li><a href="https://huggingface.co/datasets/Dhi-Technologies/blog/blob/main/01_ai_that_refuses_to_guess.md">AI that refuses to guess</a>: the thesis, calibration, refusal gates, provenance, and
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falsification ledgers as product features.</li>
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<li><a href="https://huggingface.co/datasets/Dhi-Technologies/blog/blob/main/02_e1_precision_first_linking.md">Precision first cross camera linking (E1)</a>: how a uniqueness guard lifted
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synthetic site precision from 0.918 to 1.0, and how weeks scale memory stays under 2 MB.</li>
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<li><a href="https://huggingface.co/datasets/Dhi-Technologies/blog/blob/main/03_a4_calibration_over_accuracy.md">When the error bar is the product (A4)</a>: calibration honesty, the analytic
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interval undercovered (0.50 to 0.63 versus a claimed 0.90); conformal calibration widened it
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6.7x to 14.5x.</li>
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<li><a href="https://huggingface.co/datasets/Dhi-Technologies/blog/blob/main/04_portfolio_overview.md">Six products, one honesty thesis</a>: a portfolio overview tying all six products to the
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honest by construction thesis.</li>
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</ol>
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<hr />
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<h2>Research status</h2>
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<p>Research papers describing these methods are in preparation and have not yet been published
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on arXiv or any other venue. The two whitepapers on the labs page are internal architecture
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write ups, not peer reviewed papers, and are labeled as such.</p>
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<h2>Code and evidence</h2>
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<p>The code itself is proprietary and closed source permanently. These datasets, benchmarks,
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and demo Spaces are the published evidence; closed source code, open evidence. Prompt2Model is
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a deliberate exception with one public, MIT licensed release:
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<a href="https://github.com/DHI-Technologies-Inc/Prompt2Model-Language-Guided-Vision-Model-Factory">Prompt2Model, Language Guided Vision Model Factory</a>.
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The GitHub org below is an identity link, not a browsable destination for the other five
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products.</p>
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<hr />
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<h2>Links</h2>
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<table>
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<tr>
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<td align="center"><a href="https://dhi-tech.com">dhi-tech.com</a></td>
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<td align="center"><a href="https://dhi-tech.com/labs">dhi-tech.com/labs</a></td>
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<td align="center"><a href="https://github.com/DHI-Technologies-Inc">github.com/DHI-Technologies-Inc</a></td>
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<td align="center"><a href="https://github.com/DHI-Technologies-Inc/Prompt2Model-Language-Guided-Vision-Model-Factory">Prompt2Model repo</a></td>
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</tr>
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</table>
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<p align="center"><em>Partnership or access inquiries: <a href="https://dhi-tech.com">dhi-tech.com</a></em></p>
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</body>
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