01 Litehouse AI Technologies

The operating system for regulated finance.

Litehouse AI Technologies designs and builds the AI infrastructure that regulated lending runs on — compliance, credit, audit and property intelligence. What follows is a record of that work: the platform, the products, and the way they are engineered. A showcase, not a sales page.

A showcase of work by Litehouse AI Technologies · Not a product offered to institutions

RBI · NHB · SEBI
Indexed regulatory corpus
20 / 60
Agents · Maker–Checker–Validator
On-prem
Air-gappable deployment
DPDP-ready
Data sovereignty by design
02 What we build

Regulated lending is not a horizontal problem. We don't treat it like one.

Litehouse AI Technologies builds Litehouse AI OS — an AI-native operating system for India's regulated lenders. It reads the rulebook, maps obligations to controls, tests those controls, prepares audit evidence, and embeds the same discipline into credit and operations.

Most AI tools generalise. Ours is built narrow and deep: on the Indian regulatory corpus, on the way an NBFC actually files and defends a decision, on the difference between an answer and an answer an auditor will accept.

01
Regulatory first, alwaysEvery capability starts from the obligation, not the feature list.
02
Audit-grade AIMaker–Checker–Validator governance on every material action. Nothing reaches a human unchecked.
03
Data sovereignty by designRuns inside your perimeter — on-premise or air-gapped. Your data does not leave.
04
Depth over breadthOne domain, understood completely, beats ten touched lightly.
03 The platform

One operating system. Twenty agents. A governance layer a regulator can follow.

Litehouse AI OS is an SLM-first, retrieval-grounded platform: a multi-LLM router directs work across a mesh of specialised agents, each grounded in a live index of Indian regulation and each subject to the same maker–checker–validator control.

Layer 01
Regulatory corpus
A deeply indexed, continuously refreshed corpus of Indian BFSI regulation — RBI NHB SEBI DPDP and more — with every obligation traceable to its source.
Layer 02
Retrieval & reasoning
SLM-first retrieval-augmented generation with a multi-LLM router, a Digital Twin of the borrower and portfolio, and the Validator reasoning engine that grades and defends every inference.
Layer 03
Agent mesh
Twenty primary agents across five clusters, each fronted by MakerCheckerValidator sub-agents — sixty in all — so drafting, verification and sign-off are separated by design.
Layer 04
Decision & evidence
A decision engine that produces the output and the audit trail behind it — controls tested, exceptions flagged, evidence packaged, ready for a board or an examiner.
· Five agent clusters
C1

Compliance & Regulatory

Reads regulation, maps obligations, tracks change, drafts and files.

C2

Audit & Controls

Tests controls, gathers evidence, reconciles, prepares audit-ready packs.

C3

Credit & Underwriting

Scores, builds the digital twin, reasons on risk, defends the decision.

C4

Operations & Customer

Servicing, collections nudges, KYC and document intake at the edge.

C5

Intelligence & Growth

Portfolio insight, sourcing, and the signals that move the estimate.

Every material action passes: Maker Checker Validator Human sign-off
04 How it's built

Built to be examined. Not just to demo.

Regulated software has to be defensible, not just demoable. This is the discipline the work is built to — designed for the audit that comes after the deployment. Proof you can check, not a borrowed headline.

01 — Deployment

Your perimeter, your terms

On-premise or air-gapped. The platform runs inside your infrastructure; sensitive data never has to leave your control. Open-source-native backend, no black-box dependency.

02 — Governance

Separation of duties, enforced

Maker–Checker–Validator is not a policy document — it is how the agents are wired. Drafting, verification and sign-off are handled by distinct agents, with humans on judgment and escalation.

03 — Traceability

Every number traces to a source

Each obligation maps to the regulation it came from; each decision carries the evidence behind it. Nothing is asserted that cannot be shown.

04 — Data protection

DPDP-ready, sovereignty first

Data residency, consent and purpose limitation are treated as architecture, not afterthoughts — aligned to India's Digital Personal Data Protection framework.

· Deployment architecture

How it runs behind your edge.

PUBLIC CLOUDFLARE EDGE YOUR PERIMETER Borrowers · Staff HTTPS WAF + DDoS Managed rules TLS · DNSSEC Full (strict) Hidden origin IP not exposed Litehouse AI OS Corpus · Agent mesh Maker–Checker–Validator On-prem / air-gapped Authenticated Origin Pulls — only Cloudflare can reach the origin
06 What the work addresses

The problems these systems are built around.

S1

NBFCs & HFCs

Turn a growing regulatory surface into a system that reads change, tests controls and keeps audit evidence current — without adding headcount.

S2

MSME & SME lenders

Score thin-file borrowers on alternative data, defend each decision, and route to the right capital with the underwriting trail intact.

S3

Banks & co-lending

Bring partner-originated books onto a shared, examinable standard so co-lending scales without diluting control.

S4

Mortgage & property

Cross-check title and valuation in minutes, flag defects and adverse remarks, keep the legal opinion human.

S5

Compliance & audit teams

Automate the 80% that is gathering, drafting and reconciling; keep the 20% of judgment where it belongs — with your people.

S6

Boards & examiners

A single, traceable view of obligations, controls and exceptions — ready when the board or the regulator asks.

07 Proof & perspective

Case studies and field notes, as they publish.

Case study — placeholder

Compliance close, automated

An NBFC's quarterly regulatory close, and what changed when the gathering and drafting moved to agents. Coming soon.

Insight — placeholder

What audit-grade AI means

Why separation of duties has to be architectural, and how maker–checker–validator holds up under examination. Coming soon.

Field note — placeholder

Title defects at scale

Reading title chains and valuation reports across a mortgage book — where the machine helps and where it must stop.

Partner logoPartner logoPartner logoAudit allianceSystem integrator
08 Get in touch

A record of what we've built.

This site is a showcase of work by Litehouse AI Technologies. It is not an offer of products or services, and the platform is not available to institutions through this page. For general or media enquiries:

Focus
AI infrastructure for regulated finance
Status
Showcase · not offered to institutions
Based in
India · Built for the Indian rulebook