Your company's
knowledge,
finally at work.
Vedaris is a private AI operating system built for Indian mid-market enterprises. We connect to your existing systems, make your documents searchable, and automate the workflows your team repeats every day — deployed entirely within your own infrastructure.
We're working with a small number of early partners to shape the product around real workflows — not selling a finished platform we haven't tested with you.
Architectural commitment, not a promise — deployment happens inside your own cloud account, not ours.
Claude, DeepSeek, and Qwen — each used for what it's best at, so you're never locked into one vendor's pricing or roadmap.
From shared SaaS to fully air-gapped on-premise — you choose based on your compliance needs, not ours.
Most companies don't have a knowledge problem. They have a fragmentation problem.
Documents live in SharePoint.
Contracts live in email.
Project updates live in WhatsApp.
Approvals live in spreadsheets.
Employees spend hours finding information that already exists.
Vedaris connects these systems into a single intelligence layer that understands how your company actually operates.
Not another chatbot.
Vedaris sits on top of the tools you already use and makes them searchable, actionable, and AI-ready.
What's actually under the hood.
Four systems work together. This is the moat — not any single model, but how knowledge, models, workflows, and deployment fit together.
Enterprise Memory Graph
A continuously updated map of your organization's knowledge, not a static document index.
Multi-Model Routing
Claude, DeepSeek, and Qwen, each used for what it's best at.
Private Deployment
Runs inside your cloud account. We never hold your data.
Workflow Engine
Turns retrieval into action: approvals, onboarding, compliance checks.
Four layers. One intelligent enterprise.
This is the architecture we're building toward — some of it running today, the rest in active development with our first partners.
Knowledge layer
Connects to your existing systems. Documents are processed through OCR, chunking, and vector indexing into a continuously updated knowledge graph.
Intelligence layer
Multi-model routing selects the right AI model per query — reasoning tasks to Claude, technical analysis to DeepSeek, high-volume FAQs to Qwen.
Action layer
AI doesn't just answer questions — it executes defined workflows like vendor onboarding or contract approval, always with a human checkpoint.
Learning layer
Frequently searched knowledge gets prioritized over time, and user feedback improves retrieval quality — the system gets sharper with use.
What we're actually betting on.
We're not claiming to be the biggest or the first. Here's the specific reasoning behind what we're building.
Most AI tools centralize your data. We don't.
Generic AI assistants require sending your documents to a third party's servers. For legal, financial, and construction firms handling client-sensitive material, that's often a non-starter. Vedaris deploys inside your own cloud account — your data never has to leave.
One model isn't the right answer for every task.
A contract review question and a high-volume FAQ have different cost and quality needs. Our router picks the right model per request instead of forcing every query through one expensive model — or locking you into one vendor's roadmap and pricing.
We're building with operators, not for them.
We're not designing in isolation. Early agent workflows are shaped directly with the firms piloting Vedaris, so what ships is what your team actually needs — not a generic feature list.
Permissions are inherited, not bolted on.
AI access follows your existing role-based permissions from day one. If someone can't see a document today, the AI assistant can't surface it either — this is built into the retrieval layer, not added later.
Who we're building for.
Six enterprise verticals plus education — each with a distinct fragmentation problem and example queries. Click any card to expand.
One category, three distinct buyers.
Universities, coaching institutes, and schools each have different document chaos, different urgency, and different decision-makers.
Where this goes from here.
We'd rather show you the honest plan than oversell what exists today.
- Knowledge search
- Multi-tenant platform
- Document intelligence
- Workflow automation
- Approval agents
- Contract intelligence
- Enterprise memory graph
- Full AI operating system
- Autonomous agents
Built for the buyers who scroll straight here.
Enterprise prospects evaluate security before anything else. This is what's architected in, not bolted on.
Your data never leaves your environment.
Four deployment models — you choose based on your security and compliance requirements.
SaaS Edition
Hosted by Vedaris on shared infrastructure. Fastest path to evaluating the product.
- → Shared infrastructure
- → No DevOps required from you
Dedicated Edition
Dedicated database and vector store. Isolated from other tenants.
- → Dedicated DB + vector store
- → Full tenant isolation
Private Cloud
Deployed inside your own AWS, Azure, or GCP account. We never hold your data.
- → Your cloud account
- → Zero data egress
On-Premise
Entirely within your infrastructure. Air-gapped deployment supported.
- → Air-gapped support
- → Custom model support
Who's building this.
A five-person founding team covering the full stack this product requires — not outsourced, not a single generalist.
Let's build this with you,
not just for you.
We're looking for one or two firms to pilot Vedaris on real documents and real workflows. No long contracts — just a working session to see if this solves your actual problem.
Talk to the founders →