We build intelligence platforms for high-trust environments spanning governance, public institutions, financial institutions and regulated industries.
Quantum-ready by architecture. Quantum-proven only on evidence.
Built to sit inside the country's existing digital rails rather than beside them.
DPDP Aligned
Data protection by design
Explainable AI
Transparent & interpretable
Audit Ready
Logs, traceability, compliance
Privacy Design
Consent-driven architecture
Human Oversight
In-the-loop controls
Partner Ecosystem
Regulated execution
The common thread is not the sector. It is the obligation: these are institutions that must be able to explain a decision afterwards, to someone with the standing to ask.
Central, state and district administration, line departments and the offices that allocate public resources.
State-owned operators running physical networks at scale, where a scheduling decision has a cost measured in crores or in outage hours.
Public-sector banks, cooperative banks, development finance and insurers: institutions whose every model decision is examinable by a supervisor.
The institutions on the other side of that examination, who need the same evidence trail to supervise with, and who cannot accept a black box from anyone.
Fin 100X AI builds in four stages, and the order is the argument: each one is only available once the stage before it holds. Skip any of them and what comes out is something no institution can be accountable for.
Models, agents and orchestration. Real engineering, and no longer a differentiator on its own.
The same capability under controls an institution can be held to, so someone can answer for what it produced.
A decision modelled against its alternatives before it is taken, not explained after.
An architecture that can absorb future hybrid compute where it is justified, without being rebuilt to do it.
We are not building another chatbot company.
The proposition is an infrastructure stack that turns institutional data and knowledge into governed, secure, explainable and actionable intelligence.
The difference is not features. It is where the data lives, who carries the regulatory risk, and whether an auditor arriving a year later can reconstruct exactly what was decided, on what data, under which policy version, and who approved it.
Conventional / offshore AI stack
Processed and stored outside Indian jurisdiction
The Fin100X sovereign approach
Built on Indian soil, deployed in sovereign cloud regions
Conventional / offshore AI stack
Compliance retrofitted once the product already exists
The Fin100X sovereign approach
DPDP-aligned and consent-driven by design
Conventional / offshore AI stack
Black-box scores with no decision trail
The Fin100X sovereign approach
Transparent models with end-to-end logs and audit-ready systems
Conventional / offshore AI stack
Fully automated decisions with no review step
The Fin100X sovereign approach
Human-in-the-loop on every critical decision
Conventional / offshore AI stack
Proprietary formats that sit beside national systems
The Fin100X sovereign approach
DPI-compatible, NIC-aligned and API-first
Conventional / offshore AI stack
The platform quietly carries the regulated risk
The Fin100X sovereign approach
Licensed partners execute every regulated transaction
Conventional / offshore AI stack
Priced and sized for large institutions
The Fin100X sovereign approach
Built for districts, cooperatives and the last mile
We do not force quantum onto problems classical methods solve better: the backbone evaluates and orchestrates the right computational paradigm for each workload. What comes out the other side is decisions, not dashboards.
Layer 1 of 4
Produces scenarios, forecasts and ranked options, each carrying the evidence that produced it.
Every layer is shared. InQAI and Fin AI differ in what they reason about, not in what they run on.
Outcomes
What the backbone will not do
Fin 100X AI builds the core; InQAI and Fin AI are what it is pointed at. One foundation of intelligence, decision, trust, evidence, security and deep-tech capability, inherited by both platforms rather than rebuilt in each.
Turning institutional data and knowledge into usable intelligence.
Moving from analysis to modelled, comparable courses of action.
The controls that decide what a model is allowed to do.
What survives the question asked a year later.
High-trust infrastructure assumptions, applied to AI systems.
Where the next decade of compute is prepared for, not promised.
Four separate programmes, kept separate on purpose. None of them is a production quantum deployment, and this page does not imply otherwise.
Research and application of quantum and quantum-inspired approaches to the optimization problems where they are actually the right tool.
Resource allocation · routing · scheduling · infrastructure planning
Systems designed today so future hybrid classical–quantum compute can be integrated where it is technically justified, rather than retrofitted.
Workload routing · solver abstraction · interface stability
Crypto-agility and migration preparation, so high-trust infrastructure can move to post-quantum cryptography without being rebuilt.
Crypto-agility · key management · migration planning
Experimentation, benchmarking and research partnerships. Any claimed advantage is measured against a strong classical baseline, or it is not claimed.
Benchmarking · academic partnership · publication
Quantum-ready by architecture. Quantum-proven only on evidence.Any claimed advantage is benchmarked against a strong classical baseline, or it is not claimed.
Each control below names what it produces: an artifact, a gate or a log. A governance claim that cannot be inspected is a preference, not a control.
Six answers, each resting on a commitment stated elsewhere in this document. Nothing here is asserted only here.
Built on Indian soil and deployed inside sovereign cloud regions, with architecture principles aligned to NIC standards. Data residency is a deployment property, not a policy promise.
The authorized officer, always. The platform produces a recommendation; a human approves, modifies or rejects it. The platform holds no authority of its own.
Yes. An auditor can reconstruct exactly what was decided, on what data, under which policy version, and who approved it, from the retained log rather than from anyone's recollection.
An independent service verifies every mandatory constraint before a recommendation can reach an approval screen. A solver does not mark its own homework.
DPI-compatible, NIC-aligned and API-first. It is built to sit inside existing national and state systems rather than beside them or in place of them.
DPDP-aligned and consent-driven by design, ISO 27001 certified and SOC 2 Type II, aligned to the IndiaAI mission and CERT-In practice.
The boundary
A platform recommendation is not a government decision.
The recommendation carries its evidence. The decision, and the accountability for it, stays with the authorized officer.
One governed backbone, stated in numbers. Structural counts, not projections.
Our infrastructure is designed for interoperability with India's Digital Public Infrastructure and government systems.
Contributing to the UN Sustainable Development Goals through intelligence built for public good.
What that means in practice
Everything you need to know, all in one place
Fin AI (financial intelligence) and InQAI (the sovereign intelligence infrastructure covering governance, welfare, financial, compliance, and public-sector intelligence).
Fin100X.AI builds AI intelligence infrastructure rather than selling human-hours. Its products Fin AI and InQAI provide AI-native intelligence layers for financial services, regulatory compliance, and public-sector governance, with a compliance-first, responsible-AI approach.
Fin AI is an AI-powered financial intelligence platform. InQAI is the sovereign intelligence infrastructure for governance, welfare, financial, compliance, and public sector. It is aligned to DPDP, RBI, SEBI, IRDAI, and CERT-In.
InQAI is Fin100X.AI's sovereign intelligence infrastructure for governance, welfare and the public sector. It turns a state's own data into evidence-grade decisions across household vulnerability, welfare outcomes, district planning and policy scenarios, deployed inside sovereign cloud regions and owned by India.
InQAI was previously called Maha AI, and briefly IndQI. It is the same platform from the same company, Fin100X.AI. The name changed because the earlier one read as specific to Maharashtra, while the platform serves institutions across India.
InQAI is a pan-India platform, available to state and central government institutions anywhere in India. Its first deployments were in Maharashtra, under an MoU with MahaIT, but the platform is not specific to any one state: it is built on open standards, is DPI-compatible and NIC-aligned, and deploys into any sovereign cloud region in India.
InQAI is built for governments, ministries and public institutions: departments, district administrations and the decision-makers who need evidence rather than estimates. Its first government deployment targets the Government of Maharashtra through MahaIT and the Chief Minister's Office, ahead of expansion to additional states and central institutions.
InQAI works across three areas. Household and welfare intelligence identifies vulnerable households and measures programme impact in real time. Governance and policy reasoning provides evidence-based scenario modelling for departments and decision-makers. Command-grade deployment covers mission platforms such as SACAP for events at national scale.
InQAI's State Intelligence group covers State Intelligence, Administrative Intelligence, CMO Intelligence, Health Intelligence, InQAI Intelligence and Khumba AI. Each one is an intelligence surface on the same sovereign foundation rather than a separate stack, and all of them are reachable from the InQAI page.
InQAI is DPDP-aligned and consent-driven by design rather than compliance retrofitted after the product exists. It runs on encryption and access control, keeps end-to-end logs for audit-ready traceability, uses transparent and explainable models, tests for bias and fairness, and keeps a human in the loop on every critical decision.
Yes. InQAI is built on Indian soil and deployed inside sovereign cloud regions under Indian law, rather than being processed or stored outside Indian jurisdiction. The architecture is DPI-compatible, NIC-aligned and API-first, so it interoperates with India's Digital Public Infrastructure and state systems instead of sitting beside them.
Fin AI is built on a compliance-first model: it operates as an intelligence layer, relies on licensed partners for regulated transactions, follows DPDP Act 2023 data principles, and never guarantees returns. This structure keeps regulated activity with appropriately licensed entities.
Fin100X.AI Pvt. Ltd. is based in Maharashtra, India. Its first government deployment targets the Government of Maharashtra through MahaIT and the Chief Minister's Office, ahead of expansion to additional states and central institutions.
Fin100X.AI is best described as AI-native intelligence infrastructure rather than a traditional fintech. It provides intelligence layers across financial services, compliance, and governance. Fin AI serves financial intelligence, but the company's scope extends to regulatory and sovereign public-sector AI.
it follows DPDP Act 2023 principles, keeps regulated financial transactions with licensed partners, never guarantees returns, and builds human oversight and explainability into its AI across all products.
Fin100X.AI's vision is to build India's sovereign AI intelligence infrastructure responsible, compliance-first, and India-controlled serving institutions, regulators and government across financial services and public administration. Starting in Maharashtra and expanding nationally, it aims to make trustworthy, explainable AI the foundation for governance, welfare, and financial intelligence at scale.
