INDIA'S SOVEREIGN AI PLATFORM
WHAT WE ARE
From-scratch coding, reasoning and vertical models, trained here — and a sovereign trust layer to run them, any open model, or your own, entirely inside your borders. Every call is designed to be masked and screened, with a Trust Receipt for each masking call and a recorded trail you can audit.
SOC 2 & ISO 27001 certified · independently assessed for HIPAA & GDPR · designed to support India’s RBI FREE-AI framework
THE PROBLEM
In regulated industries, AI you can’t govern is AI you can’t use.
A hallucination becomes a compliance event. A prompt that leaves the country breaks the law.
THE PLATFORM
ONE SOVEREIGN SYSTEM
Watch one request make the trip: masked, screened, routed by the hub to the right model for the job — coding, reasoning, or a vertical — and recorded on the way out. Different intent, different model; same governance, same perimeter, every time.
Model Hub
A governance-aware catalog. Every model carries provenance, evaluation history and the policies that apply to it, so the choice of a model can be explained from the record.
Sovereign
The perimeter itself. Deploy the whole platform on your cloud, in your jurisdiction — region-pinned or air-gapped. Nothing in this picture leaves your boundary.
Our models + the Trust Platform
Coding, reasoning, and vertical models built in India — calls to them designed to be masked by Aavaran and screened by Kavach, with every step recorded.
INDIA’S SOVEREIGN AI PORTFOLIO
Taksha, our sovereign AI engineer, is in developers’ hands now — with an independent referee that checks its work and says so when a claim doesn’t verify. Behind it, seven more families are being measured. We publish a number only where a model has been measured, and we say which model it belongs to.
The wedge: every model serves behind a trust layer that is live product, not roadmap
Kavach
Screens each request in front of the model
The sovereignty layer has its own home: AgentAnywhere Swaraj (स्वराज — self-rule) — agentic AI designed to stay inside your borders, with Trust Receipts for masking, a recorded trail for every run, and region-pinned deployment.
Works in VS Code (our extension, with the independent referee), Zed, Aider, and any OpenAI-compatible editor — two-minute setup guide.
Measured, not claimed
Not a leaderboard screenshot. Our own battery, run on our own infrastructure, graded by executing the output rather than trusting it. Two candidates failed our gates and were rejected.
Agentic tasks completed
Taksha Pro and Taksha Max each finished every task in the graded battery.
5 task classes × 9 runs per model, graded by executing the result — not by asking the model. Run separately for Pro and for Max.
Phantom completions
Zero runs claimed success without doing the work.
A phantom is a run that finishes fast, calls no tools, and reports success. We count them because they are the dangerous failure.
Mutation-kill rate
Taksha Pariksha's test suites catch the defects we seed on purpose.
We break the code on purpose and require the suite to go red. 57 of 64 mutants killed, zero invalid suites.
Two candidate models failed these gates and were rejected rather than shipped — a coding fine-tune that regressed against the model it was meant to replace, and a first UI specialist that did not beat its own baseline. We publish that for the same reason we publish the rest: a number you cannot fail is not a measurement.
SOC 2 and ISO 27001 are held certifications. HIPAA and GDPR readiness is independently assessed. The platform is designed to support India’s DPDP Act and the RBI FREE-AI framework — each claim stated exactly as strong as it is.
SOVEREIGN CODING PLATFORM
Our own sovereign model behind a policy gateway, with every completion metered. Run it hosted and region-pinned, or drop the whole control plane inside your VPC and air-gap it. Your source never trains anyone else’s model.
Start free in any OpenAI-compatible editor — Continue, Cline, Zed, Cursor — metered in credits at a fraction of the closest hosted rival.
PRICING AT A GLANCE
Free
500K credits · by invite
$0
Dev
15M credits · Fast + Max
$9/mo
≈ ₹899/mo
Team
Pooled credits · Pro model
$19/seat
≈ ₹1,849/seat
Enterprise
Flat, unlimited, air-gapped
In-VPC
Credits = tokens × model weight (Fast 1× · Pro 5× · Max 20×, reflecting its dedicated node). Pay in USD or INR · buy top-up packs any time.
TEN CAPABILITIES · ONE PLATFORM
You build on the surface. Every call routes through one gateway — to any model, with every step of the run recorded — all inside your perimeter.
Every call routes through here
The intelligence, inside your walls
DOGFOOD
Our marketing team's lead generation runs on agents we built in Flow Studio. Our sales team responds to enterprise RFPs with our RFP Agent. Our legal team reviews contracts with our Legal Agent. Our cloud spend is managed by Cloud Optimizer. Our content engine is staffed by agents we built ourselves, deployed in our own AgentAnywhere instance.
Every claim we make about the platform's production readiness, we make as a customer first.
WHO RUNS WHAT
Marketing
Lead generation in Flow Studio
Sales
RFP Agent
Legal
Legal Agent
Infrastructure
Cloud Optimizer
Content
Bespoke agents in our own instance
From the blog
A complete guide to DPDP compliance for AI agents in India: what the DPDP Act 2023 and DPDP Rules 2025 require of a data fiduciary, duty by duty, the evidence AgentAnywhere records for each, and what stays your job before May 2027.
AgentAnywhere Research18 min read
An AI agent gateway is the front door every agent call passes through. Five questions to ask of one, and how the AgentAnywhere Agent Universal Gateway answers each.
AgentAnywhere Research8 min read
AgentAnywhere Model Hub is a governance-aware model registry: every model carries provenance, evaluation history, the policies that apply to it and an audit trail, so a bank, a defence programme or a contact centre can answer which model produced an output, what its limits were and who approved it.
AgentAnywhere Research9 min read
Regulators are asking which AI systems you run, who built them and what changed. How AgentAnywhere Registry and Model Hub keep agents, flows and models on record, with agent approvals and saved flow versions.
AgentAnywhere Research7 min read
How a drone wind turbine inspection reads each blade for erosion, lightning damage and defects, and how a finding becomes a work order an engineer approves.
AgentAnywhere Research13 min read
THE CLIMB
India built its own space programme one launch at a time. We are building its intelligence the same way — not a press release, a ladder. We publish a number only where a model has been measured, and we say which model it belongs to.
Where we are — measured
Where we’re going — our aspiration
A sovereign frontier model for India, at the scale the frontier demands — on the order of a trillion parameters, trained here, owned here.
That is the destination, and we are honest about the distance: it is gated on data and compute, not ambition, and every rung between here and there will be measured and published the same way — efficiency levers first (MLA proven, mixture-of-experts next), so the climb is capability per rupee, not scale for its own sake. We would rather show you the meter than sell you the mountain.
OPEN SOURCE
Four Apache-2.0 projects at the control points: Shuddhi decides what a model may learn from, the Universal Gateway decides what an agent may call, Model Hub records which model is serving, and the Annotator produces the labels underneath. A governance claim you cannot read is a claim you cannot check.
For pilot requests, partnership inquiries, and architecture conversations.
