Data Ingenious Global Limited / Sovereign Enterprise AI
Own your intelligence.
100% Data Ownership. Unlimited AI for Your Entire Enterprise.
ZenithAI turns institutional documents into cited, permission-aware answers on infrastructure you own — on-premises, in your private cloud, or in fully isolated networks.
Sec. 00 Workspaces in operation
One platform. Three altitudes.
The same governed console serves a single officer, a whole department, and the entire organisation — each inside its own isolated workspace. All examples use fictional demonstration data.
Sec. 01 The problem
Your institutional knowledge is everywhere.
Your answers are nowhere.
Policies, contracts, registers, reports and correspondence hold decades of institutional knowledge — scattered across file shares, inboxes and archives. Finding an authoritative answer means finding the right person, on the right day.
Public AI services can read documents — but only if you send your documents to them. For a government department, a bank, or any custodian of regulated data, that trade is often unacceptable. And per-user, per-token billing makes it unpredictable at exactly the moment adoption succeeds.
Sec. 02 The platform
One platform. Deployed where your data lives.
ZenithAI is a complete enterprise AI system — assistant, knowledge layer, document intelligence, governance — installed inside your security boundary and operated under your rules.
Documents in. Governed intelligence out. The core platform keeps your data inside the boundary you define.
Sec. 03 Capability registry
What the platform does. On record.
Every entry below is a running capability of the platform — not a roadmap slide. Demonstrated live, on request.
Full capability detail, including governance and administration → Platform
Sec. 04 Intelligence tiers
Four tiers of intelligence. All of them yours.
Every task gets the right engine — quick lookups never burn heavyweight compute; deep analysis is never short-changed. The platform routes automatically, or your users choose.
Instant
Immediate responses for lookups, utilities and speech transcription.
Example class · compact models, 2–5BFast
The everyday workhorse: general questions, summaries, routine drafting.
Example class · efficient models, 4–9BSmart
Analysis, document reasoning with citations, coding, formal drafting.
Example class · capable models, 12–30BGenius
The deepest reasoning tier for complex, multi-step analytical work.
Example class · large open-weight models, sized to your infrastructureModels are examples, not commitments — tiers run leading open-weight families selected for your deployment and exchanged as the field advances. Models & infrastructure →
Sec. 05 A different operating model
Public cloud AI and owned AI are different architectures.
Public AI services are excellent products, built on an operating model where the provider runs the infrastructure and your data travels to it. ZenithAI is the other architecture: the platform comes to your data.
| Dimension | ChatGPT Enterprise | Claude Enterprise | ZenithAI |
|---|---|---|---|
| Data location | Cloud | Cloud | Your infrastructure |
| Internet required | Yes | Yes | Optional† |
| Uses your documents | Partial | Partial | Complete |
| Model choice | Provider's models only | Provider's models only | Model-flexible, open-weight |
| Unlimited users | No | Limited by licence | Yes |
| API charges | Yes | Yes | No |
| Token billing | Yes | Yes | No |
| Commercial model | Per-user / per-token subscription | Per-user / per-token subscription | One-time licence + AMC |
| Air-gapped | No | No | Core platform, yes† |
| Data ownership | Shared responsibility | Shared responsibility | 100% yours |
† Core platform runs without internet access; fully isolated configurations including offline authentication are scoped and validated per engagement. Comparison reflects each provider's publicly documented enterprise offering as of this writing and describes structural differences between deployment models, not overall product quality.
Sec. 06 Ownership economics
Predictable economics, not a meter that runs.
With per-user and per-token pricing, cloud AI grows more expensive with every employee who adopts it — success is penalised. ZenithAI inverts that: capacity is a deployment you own, so organisation-wide adoption doesn't multiply a monthly bill.
Usage on your own deployment is unmetered. Hardware, licence, implementation and maintenance are quantified transparently during scoping — we prepare a side-by-side projection against your current or forecast public-cloud AI spend. Deployment & commercials →
Sec. 07 Security & governance
Governed by design.
The decisive control is structural — the platform lives inside your boundary. The remaining controls are engineered into the product and configured to your policy.
Keeping regulated data inside your own infrastructure directly supports data-minimisation and localisation objectives, including obligations under India's Digital Personal Data Protection Act, 2023.
- 01Authenticated access — TOTP sign-in, bounded sessions, hashed tokens
- 02Workspace isolation — private to creator; cross-user access denied without acknowledging existence
- 03Fail-closed privileged audit — if the audit write fails, administrative access is refused
- 04Egress logging — outbound email and web activity recorded, with redaction
- 05Hardened execution — sandboxed computation: no network, no filesystem, resource-limited
- 06Human-consent gates — emails and calls require explicit confirmation; the assistant proposes, a person decides
Sec. 08 Who builds it
Built by Data Ingenious Global Limited.
An Indian enterprise technology company with a long record of building and operating secure, mission-critical communication and e-governance platforms — engineered and supported in India, operated daily on its own work. The product vision is guided by the author of ZENITH — Mastering AI for Everyday Life and Work.
Track record of Data Ingenious Global Limited across its enterprise platforms. About the company →
Sec. 09 Questions
Frequently asked questions.
Where does ZenithAI run?
Does our data leave our infrastructure?
Which AI models does ZenithAI use?
Can ZenithAI operate in a restricted or offline network?
How is ZenithAI priced?
What does implementation involve?
The next step
See your documents answer back.
Request a demonstration and we'll show ZenithAI working on realistic material for your sector — deployed the way it would run in your environment.