DataVedam technology

Intelligence is only
the beginning.

What surrounds the model matters.
The runtime. The permissions. The path to the real world.

A precision sculpture of titanium and amber-glass layers.

Two different
engineering questions.

How should an agent operate inside a product? And how can a model be shaped for a particular task and environment? Sarva and Bodhi address these questions at different layers.

Sarva focuses on product-agent hosting, authority and execution. Bodhi focuses on the model-development path from learning material to an evaluated student artifact. They are distinct technologies with their own integration requirements, not an assertion that every DataVedam product runs on one identical stack.

Agent framework

Sarva

An agent needs more than a model.

A governed framework for product agents—with tool access, approvals, persistence and traceability built into the runtime.

Useful agents operate inside a product, with users, permissions, memory and consequences. Sarva provides a reusable host and governance layer so product teams can define domain behaviour without treating every action as an unrestricted model instruction.

Explore Sarva

Model distillation

BodhiVedam

The right model fits the work.

A knowledge-distillation platform for turning teacher-model capabilities into smaller models you can evaluate, package and deploy.

The largest model is not always the right deployment choice. Bodhi brings the distillation lifecycle together—from preparing task data and learning from a teacher to evaluating a student and selecting a deployment format for its intended environment.

Explore BodhiVedam

Find your
technical starting point.

The right evaluation begins with the boundary your team needs to understand.

“Our agent needs to take actions inside a product.”: Start with the product’s identity, permissions and tool contracts. Sarva’s architecture separates domain behavior from the host and governed action boundary. Explore approvals, persistence and the evidence left by execution.Inside a governed product agent
“We need to assess a smaller model for a specific task.”: Start with representative inputs, quality criteria and deployment constraints. Bodhi connects data preparation, teacher/student training, evaluation and packaging. The decision depends on the measured task and the target environment.Inside the distillation workflow
“We need help connecting these ideas to our environment.”: A services engagement can assess the architecture, build the required application path and evaluate the result. Framework or model selection follows the task, data boundary and operating constraints.Private AI & deployment AI & agent engineering

What a useful
evaluation should show.

The boundary

Define the responsibility of the technology and the responsibilities retained by the integrating product. Identify authentication, data access, provider dependencies, model-use terms and operating ownership.

The complete path

Follow a representative task through the intended integration. For an agent, inspect the action and its result. For a student model, inspect the artifact and its behavior on the agreed evaluation in the target stack.

The important trade-offs

Review quality, control, operating effort and deployment fit together. Separate a local implementation check from a provider-verified action or a production rollout. The evidence should support the decision your team is actually making.

The next implementation step

Agree the missing integration work and how it will be verified. Private distribution, supported configurations and technical support are discussed with the team for the specific scope.

Your next chapter

What could we
make possible?

Bring us the work that matters.
We’ll find the right place to begin.

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Intelligence is onlythe beginning.

Two differentengineering questions.