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myKutir's AI is purpose-built for Indian residential society management — it understands maintenance billing, RWA governance, co-operative housing rules and the specific workflows of Indian housing societies. 15 AI features wired into every module, grounded in your society's own data.
Enterprise-grade AI with Retrieval-Augmented Generation (RAG) for grounded, cited answers.
Without AI
With myKutir AI
Not a generic chatbot plugged in. Every feature is purpose-built for the specific workflows of Indian residential society management.
Ask anything. Get a cited answer in seconds.
Residents spend time hunting through old WhatsApp messages and notice boards trying to find the parking rule or pet policy. myKutir's RAG search lets them type a question in plain language and get an answer cited directly from your published society notices. Not guesswork. Not a chatbot. A retrieval-grounded response with a link to the source.
One rough note in. A complete, categorised complaint out.
Most residents give up on raising complaints because they don't know how to word them formally. myKutir's AI takes a rough note — "lift stuck again 3rd floor" — and turns it into a structured complaint with category, priority level and a clear description, ready to submit in one tap.
Bullet points → official society circular.
The secretary shouldn't spend 45 minutes writing a water-supply maintenance notice. Paste your bullet points into myKutir and the AI produces a fully formatted, appropriately toned society circular — formal for AGM, friendly for events, urgent for emergencies. One click to publish to all residents.
Numbers turned into a readable summary — automatically.
Your monthly finance review shouldn't require a CA to explain the numbers to the committee. myKutir's AI reads the month's collection rate, top expense categories, overdue count and fund balances — and writes a plain-English narrative summary ready to share with the committee or read at the monthly meeting.
Raw meeting notes turned into published minutes.
The secretary takes rough notes during an AGM — incomplete sentences, short-forms, names. myKutir's AI takes those raw notes and generates properly structured AGM minutes with agenda items, resolutions passed, votes recorded and action items listed. Ready to publish to residents with one approval.
Professional replies to every complaint — in one click.
A committee member handling 15 open complaints shouldn't spend 20 minutes writing each reply. Complaint Reply Assist drafts a professional, empathetic response based on the complaint description and current status — personalised to the resident's name and the specific issue.
Set once. Runs forever without anyone touching it.
These are not AI buzzwords. myKutir's automations are reliable, production-tested triggers — payment follow-up sequences, SLA breach escalation chains, agreement expiry alerts and billing runs that happen on schedule whether or not anyone logs in.
Every AI capability is built into the platform. No add-on pricing, no API quota to manage, no separate AI subscription.
Retrieval-Augmented Generation (RAG) — the only architecture that produces grounded, citable answers from your own society data.
Every notice your society publishes is chunked into sections and converted into a semantic vector embedding. Each chunk is tagged with its visibility scope — so a resident only retrieves what they are permitted to read.
When a resident asks "Can I bring my dog to the gym?", the system finds the most semantically relevant notice chunks — not by keyword matching but by meaning. "Pets" matches "animals", "gym" matches "fitness centre".
The LLM synthesises an answer using only the retrieved chunks as context. It cannot invent rules that don't exist. Every answer includes a citation — residents tap it to read the original notice in full.
Privacy-by-design at the architecture level. Your society's data is never used to train any model, never shared across societies and always scoped to the resident asking.
Every AI query is processed strictly within the context of your society's own data. A question from one society never touches another society's notices, rules or resident information.
A resident gets answers only from notices they are permitted to see. Notices marked committee-only never surface in resident queries — enforced at the retrieval layer, not the LLM layer.
Every RAG answer includes the source notice. The LLM has no ability to invent rules that don't exist in your notice corpus. If the answer isn't in your notices, the AI says so.
Society data — notices, complaints, resident names, financial records — is never sent to any AI provider for model training. It is used only to answer the query in context.
myKutir runs on enterprise-grade, safety-focused AI models. Platform administrators can also configure a self-hosted or custom AI endpoint for full data residency control.
AI processing aligns with India's Digital Personal Data Protection Act 2023. Consent is recorded, data is not retained beyond the session and erasure requests are honoured.
myKutir includes 12 live AI features: RAG notice search (residents ask questions, get cited answers), complaint drafting (rough notes expanded into full complaints), notice authoring (bullet points to formal circulars), financial narrative generation, AGM minutes summarisation, complaint reply assist, budget forecasting, defaulter message generation, resolution tracking, complaint digest, payment reminder sequences and SLA auto-escalation. Three additional features — expense category suggestion, expense receipt OCR and bill dispute assist — are coming soon.
myKutir uses Retrieval-Augmented Generation (RAG). When a society publishes a notice, it is chunked into sections and converted into semantic vector embeddings stored in a database. When a resident asks a question, the system retrieves the most semantically relevant chunks — not by keyword but by meaning — and uses an LLM to synthesise a grounded answer. Every answer includes a citation linking to the original notice. The LLM cannot invent rules that don't exist in the society's own notice corpus.
The RAG architecture prevents hallucination by design. The LLM is given only the retrieved notice chunks as context — it cannot draw on its general training data to answer society-specific questions. If the answer to a resident's question does not exist in the society's published notices, the AI responds that it could not find a relevant notice rather than inventing a rule.
myKutir runs on enterprise-grade AI models optimised for speed, accuracy, and cost. Platform administrators can also configure a self-hosted or custom AI-compatible endpoint — including a local Ollama instance — as a fallback or primary provider for full data residency control.
Query context — the retrieved notice chunks relevant to a specific question — is sent to the configured AI service to generate the response. This is equivalent to pasting a paragraph from your notice into a chat assistant. Resident names, payment records and financial data are never included in AI queries. myKutir's data is never used to train any external AI model.
Yes. Platform administrators can configure a self-hosted AI endpoint (such as a local Ollama instance). This means all AI processing happens on your own infrastructure — no data leaves your network. Suitable for societies with strict data residency requirements.
Technical questions? Email [email protected]
30-minute live demo of RAG notice search, complaint assist, notice authoring and automations — configured for your society's actual notices and rules.