ZenteiQ
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Scientific intelligence infrastructure for industry

Frontier AI for the physical world.

Not just another LLM. We build scientific foundation models from scratch, grounded in physics and engineered for real-world industry.

Request a brief Meet Dhīr
Born at IISc Bangalore Supported by the IndiaAI Mission
∂u/∂t = α∇²uLive solver in your browser. Hover to probe, click to add a source.

Dhīr: built from scratch, fine-tuned for your enterprise

Dhīr base models are pre-trained from scratch rather than adapted from existing foundation-model weights. Enterprises can fine-tune them on their own data and deploy them in configurations suited to their requirements.

  1. 1Dhīr base model pre-trained from scratch
  2. 2Fine-tuned on your enterprise data
  3. 3Your enterprise model, deployed where you run
Dhīr model sizes
Attribute Dhīr Small Dhīr Medium Dhīr Large
DeploymentEnterprise edgeEnterprise self-hostedCloud-hosted
Built forEdge applicationsR&DComplex engineering

  • Dhīr Small

    Deployment
    Enterprise edge
    Built for
    Edge applications

  • Dhīr Medium

    Deployment
    Enterprise self-hosted
    Built for
    R&D

  • Dhīr Large

    Deployment
    Cloud-hosted
    Built for
    Complex engineering
Each size can be fine-tuned on enterprise data.

AI that computes physics, not one that describes it.

Most AI fine-tunes language models on text about engineering. The result can discuss Navier–Stokes fluently, but cannot solve it. Engineers need answers that hold up, so we build models that reason about the physics itself.

DescribesTypical language model answer

“The outcome depends on several factors, including the materials, the geometry and the operating conditions. Exact values would require further analysis.”

Plausible, but nothing to measure
ComputesComputed field
u = …Illustration computed by a reference solver in your browser

Physics-native by design

Built to reason about physical systems, not only to describe them.

Physics-aware validation

Scientific model results are evaluated using reference numerical solutions and relevant physical constraints.

Sovereignty and governance

Independent model development, traceable data pipelines and deployment choices designed for enterprise requirements.

Research and progress

Dhīr training milestones and scientific AI work in industrial engineering. Workload-specific technical and validation results can be reviewed with qualified partners under appropriate confidentiality arrangements.

2

Dhīr base models trained

Training complete, including a mixture-of-experts model.

Upcoming

Public release

For the first two Dhīr models.

Underway

Next scientific foundation model

In development. Public release subject to evaluation.

Engineering use case

Industrial engineering · partner undisclosed

Challenge

High-fidelity engineering simulations are computationally intensive when engineers need to explore many design variations and operating conditions.

Approach

ZenteiQ developed a Scientific Machine Learning surrogate from high-fidelity simulation data and compared its predictions with reference numerical-solver results.

Outcome

The surrogate supports rapid assessment of selected design scenarios, with high-fidelity solver results remaining the validation reference. Workload-specific quantitative findings are shared only through approved technical disclosures.

Technical depth for partners

Grounded in physics and numerical methods. Architecture and benchmark materials are available under review.

Scientific AIPhysics-native foundation models for engineering systems.
ValidationIndustrial validation for accuracy and reliability.
Technical briefsArchitecture and benchmarks, under NDA.

One model family at the centre of everything we build

BrahmX prepares scientific and engineering data, Dhīr provides a family of foundation models, and ZenteiQ products bring the technology into practical workflows. Select any part to see what it does.

Scientific depth built into the foundation

ZenteiQ develops physics-native foundation models from scratch, combining scientific research, traceable data pipelines, model training and enterprise deployment capabilities.

Our differentiation combines first-principles research, scientific data, model training, and engineering workflows into a connected technology stack.

AI meets science

Physics-native intelligence purpose-built to power Industry 5.0.

Sovereign by design

Independent foundation-model development with an emphasis on data governance, deployment flexibility and privacy-conscious engineering.

Backing and affiliations

IndiaAI Mission (MeitY)Selected and funded to build India's sovereign scientific foundation models.
ARTPARKCollaborating in this DST-backed ecosystem to scale data pipelines and engineering platforms.
FSID, IIScIncubated for deep-science co-innovation and national R&D projects.
The AI AllianceMember of the global open-science consortium.

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