# How to add an AI chatbot to TallyPrime > Source: https://netray.co/integrations/tally-ai-chatbot > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Integrations ## Short answer TallyPrime has no user portal or chat layer, so a chatbot is a separate web or messaging front end that queries Tally through its HTTP interface or a nightly export. Ground every answer in retrieved Tally rows, keep it read-only, and decide whether it serves staff, customers or both. ## System TallyPrime ## Capability ai-chatbot ## Intro Owners of Tally-based businesses field the same questions all day: what is the balance for this customer, is this item in stock, has that payment been received. Answers sit in Tally, but only a few people have access, so the accountant becomes the human chatbot. A chatbot changes that, provided it answers from live data rather than a language model's guesses. For Tally, grounding means the bot calls a small set of lookups and then phrases the result, with the source shown. The harder design question is audience. A staff bot can see more; a customer-facing bot should see only that customer's statement. Those are different systems with different risk. ## Use Cases ### Staff balance and stock questions **Description:** Salespeople ask the bot for a customer's outstanding or an item's stock before quoting, without calling accounts. ### Customer statement bot **Description:** A customer, after verification, asks for their open invoices and last payment; the bot answers from that party's ledger only. ### Owner daily summary Q&A **Description:** The owner asks about today's sales, cash and bank balances, and receivables over 60 days, with each number sourced. ### Policy and process helper **Description:** Retrieval over internal documents, such as credit policy and returns process, so staff get consistent answers alongside Tally numbers. ### GST filing status prompts **Description:** The bot reminds the accountant of pending vouchers with missing GST details found by a scheduled check. ## System Surface - HTTP interface of the running TallyPrime for live lookups of outstandings, ledgers, vouchers and stock summaries. - Scheduled export or sync into a reporting database, which decouples the chatbot from whether Tally is open and makes customer-facing use safer. - TDL collections to return lean, role-specific data for the bot's lookups. - Party master fields such as contact numbers and GSTIN, usable to verify customer identity, with care about privacy. - No native chat or portal in Tally, so channel choice (web widget, WhatsApp, Teams) is independent of Tally. ## How It Works ### Define the audience and scope **Detail:** Write down which questions the bot may answer, for whom, and what it must refuse. Customer bots need strict per-party scoping. ### Choose live access or a synced copy **Detail:** Staff bots can call Tally directly; customer bots should read from a synced database so Tally never faces the public path. ### Implement lookup tools **Detail:** Build outstanding, ledger, stock and recent-payments lookups returning small JSON results with source references. ### Add retrieval for documents **Detail:** Index policies, price lists and FAQ documents in a vector store so non-numerical questions are grounded too. ### Prompt for grounded answers **Detail:** Instruct the model to answer only from tool results and documents, show the source and say so when data is missing. ### Deploy on the chosen channel **Detail:** Embed in an internal web page or connect to a messaging channel; add login for staff and OTP verification for customers. ## Example Flows ### Salesperson checks credit before quoting **Trigger:** Salesperson asks the staff bot about a customer's position. **Steps:** - Bot calls the outstanding lookup for the named party. - It also checks the credit limit field or policy document. - It replies with overdue amounts, oldest bill date and policy guidance. **Result:** A quote goes out with credit context, no call to accounts. ### Customer asks for last invoice **Trigger:** A verified customer asks for their most recent invoice. **Steps:** - Bot confirms identity using the registered mobile and an OTP. - It queries the synced database for that party's latest sales vouchers. - It returns invoice number, amount, due date and a link to the PDF if stored. **Result:** A routine request handled without staff effort. ## On Prem Because many Tally businesses already run everything locally, the whole chatbot can too: an open-weight model on a modest GPU server, a vector store for documents, and a service that reads Tally directly or from a synced database on the LAN. External exposure, for customers, should be a thin proxy that only forwards verified, scoped questions. If a GPU is not available, a small model can handle lookup-style questions with the numbers coming from tools rather than the model. ## Security - Enforce row-level scoping in the lookup service, not in the prompt: a customer token can only query its own party. - Keep Tally off the public path; serve customers from a read-only synced copy. - Block write operations entirely in the chatbot; hand off to a person for changes. - Log questions and retrieved rows, and review refused or low-confidence cases regularly. ## Options ### No-code chatbot platform with exports **Fit:** Small teams needing FAQ and basic statements. **Effort:** Days; freshness limited to export schedule. ### Custom bot over Tally lookups **Fit:** Businesses with a developer and clear scope. **Effort:** Several weeks including verification flows. ### Netray on-prem assistant **Fit:** Firms needing private models and strict scoping. **Effort:** Pilot for staff first, customers later. ## Pitfalls - Fuzzy party-name matching sends one customer another's balance; use IDs and confirmation. - Letting the model compute totals from raw rows invites arithmetic errors; compute in code. - Answering without a date makes numbers ambiguous; always state the as-of time. - A customer bot reading directly from the live Tally machine slows accounting work during peak hours. ## Faq ### Can the chatbot run inside TallyPrime? **Answer:** No. TallyPrime does not host chat interfaces. The bot runs as a separate service and reads Tally data through its HTTP interface or a synced copy, then appears in a web page or messaging channel. ### Is it safe to let customers query their balance? **Answer:** It can be, with verified identity, strict per-party scoping in code, and a read-only synced database. Without those controls, we would not recommend a customer-facing bot. ### Does it need an internet connection? **Answer:** Staff use can be fully offline on the LAN with a local model. Customer access needs a public entry point, which should be a thin proxy to a synced copy rather than Tally itself. ### How accurate are the answers? **Answer:** Numbers come from Tally lookups, so they are as accurate as the books. The model only phrases them. Test with real questions and review failures before wider rollout. ### Can it answer in regional languages? **Answer:** Many open and hosted models handle Hindi and other Indian languages to varying degrees. Test on your own phrasing, including mixed-language messages, before promising it. ## Search terms tally chatbot, ai chatbot for tally, tallyprime chatbot, chat with tally data, tally customer statement bot, ask tally questions ai, on-prem chatbot tally, tally natural language query --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json