Yantra runs every AI model on your hardware or ours. Medha gives everyone an assistant grounded in your documents. Nirman is the desktop workspace for research, writing and code. Prebuilt connectors bring in WhatsApp, email, chat and files.
Yantra, Medha and Nirman in one platform. Screens show sample data.
Yantra runs the models. Medha and Nirman are what people use every day on top of it, with the same models, permissions and audit trail everywhere.
One console to run AI for the whole organisation: every model, every request, every cost.
Policies, manuals and project files: Medha answers from what your organisation has written, on models you control.
1234Give it a task, a question or a snippet. It remembers the session, cites its sources and stops to ask when it is not sure.
1234Paying a frontier vendor per call means your data leaves and you keep nothing. With Yantra, that spend becomes an asset: your usage trains models you own.
Your data stays insideModels run on hardware you control. Nothing leaves the network unless you opt in.
Usage trains models you ownReliance on frontier models steadily gives way to your own: the graduation curve.
Automatic model orchestration SoonEach request goes to the least expensive model that can handle it. Nobody picks a model again.
Seven families of open research models, each reachable with the same token. No separate vendors, no data leaving the building.
Reasoning, writing, structured output, agentic tool use.
Natural voice from text in under a second.
Transcripts with word timings and subtitles.
From rapid drafts to photoreal finals.
Short cinematic clips from a prompt.
Photo and audio become a lip-synced presenter.
Click-to-mask segmentation and detection.
A built-in assistant grounded on your documents.
Yantra sits between the tools your teams build and the compute you own. It routes every request, enforces who can do what, and keeps a complete record.
from openai import OpenAI client = OpenAI( base_url="https://yantra.your-org/v1", api_key=YANTRA_KEY, ) client.chat.completions.create( model="qwen3.6", messages=[{"role": "user", "content": "…"}], )
import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://yantra.your-org/v1", apiKey: process.env.YANTRA_KEY, }); await client.chat.completions.create({ model: "qwen3.6", messages: [{ role: "user", content: "…" }], });
curl https://yantra.your-org/v1/chat/completions \ -H "Authorization: Bearer $YANTRA_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"qwen3.6", "messages":[{"role":"user","content":"…"}]}'
The only change is the base URL. Anthropic and Ollama clients work the same way.
All workers healthy. Requests are spread across the fleet.
A sample of the 44-model catalog. Click a model to copy its id.
Who asked, which model answered, where it ran and how long it took: every call is on the record.
One workspace for research, writing and code. Every session is kept, so work builds on itself instead of starting over.
Every conversation is kept, searchable and pinnable, so work picks up where it left off. Blurred here.
Prebuilt connectors come with the platform. Anything else is built on demand.
ERP, HR, finance, CRM, a government portal or an internal database: we build the connector by API or MCP. Once it’s in, it works like the prebuilt ones, with the same sign-in, permissions and audit trail.
Connect the channels where work already happens. Yantra reads them with your own models, governs every call, and a connected app turns it into action. Below is one we run every day for staffing and delivery.
WhatsApp chats, Gmail senders, Slack channels and Drive folders, each linked to a project through the prebuilt connectors.
Project health, risks, answers and staffing suggestions come from models served by Yantra on local GPUs, through its OpenAI-compatible API.
Google SSO, roles and rate limits, with each request logged and costed. The app did not have to build any of it.
Staffing, project health, actions from WhatsApp with YES and undo, and a full activity log.
Comes with YantraBuilt on top for your workflow
Link a WhatsApp chat, a Gmail sender, a Slack channel or a Drive folder to a project once. From then on every message lands in one picture per project. Read-only, and only the chats you choose.
Yantra’s models read what clients and teams actually say, flag risk early, and turn it into one move: who to add, who frees up, which skill is missing. The reading happens on your own GPUs.
A question like “who can cover QA next sprint?” gets an answer from live data, with the reason and a suggested booking. Nothing changes until a person says yes, and only people with the right role can say it.
Managers ask and act from WhatsApp: who is free, book someone, move a project, approve a step. Several instructions in one message become one list and one yes. Every change can be undone and is reported to the right people.
Who signed in and what changed, from the app, from WhatsApp or by the assistant itself, with before and after. Search by person, date or activity, and export it.
Allocation, utilisation and the bench in one view, kept current by the channels, the models and the record above. Planning starts from what is true today.
Built for your workflow. Connected apps sit on the prebuilt connectors and Yantra, and are scoped with you as their own engagement.
Discuss a connected appThe same platform, delivered three ways, from fully managed to fully sovereign.
Intelligaia hosts and runs Yantra. Prepaid credits, spent per model.
The entire platform on your hardware, under your brand.
Bring your own GPUs into Yantra as a private, pinned fleet.
Compute for a sovereign setup. A standard server runs the platform and applications. The AI needs GPU compute: NVIDIA DGX Spark units, two at minimum and four to five for a full team.
Yantra, Medha, Nirman and the prebuilt connectors, deployed the way you choose.
Your processes turned into apps and agents on Yantra and the connectors, like the staffing app shown above. Scoped with you.
Our engineers work inside your team to map processes, build and hand over, and train your people to run it.
Not on a sovereign or hybrid deployment. Models run on your GPUs, and frontier cloud models are only used when you switch them on. Every request is logged, so you can show exactly where data went.
No. Yantra speaks the OpenAI, Anthropic and Ollama APIs. Point the client at your Yantra base URL and the application keeps working, now with access control, logging and cost accounting.
Yes, as an opt-in burst. Organisations already on Google, Microsoft or Amazon can keep frontier models where they want them and run the same applications on top.
Yes. Custom connectors are built on demand, by API or MCP, for systems such as ERP, HR, finance, CRM, government portals or internal databases. Once built, a custom connector works like the prebuilt ones: same sign-in, permissions and audit trail.
For a sovereign setup, a standard server for the platform and applications, plus GPU compute for the models: two NVIDIA DGX Spark units at minimum, four to five to support a full team. The managed plan needs no hardware.
The managed plan is live in minutes. A sovereign rollout starts with one team and one application, connected to the channels you already use, then grows from there.
They are built on top of it. A connected app uses the prebuilt connectors, Yantra’s models and its control plane for one workflow, like the staffing and delivery app shown above. Each one is scoped with you as its own engagement.
Our engineers. They map your processes, build the applications and agents on Yantra, connect them to your systems, and train your team to run them.
Tell us how you want to deploy, managed, on-premise or on your own fleet, and we'll tailor a rollout.
