ERPNext AI Knowledge Assistant (RAG Chatbot)

Key Features

Retrieval-augmented generation grounded strictly on retrieved ERPNext records and document chunks — answers cite their source documents

Vector search index over admin-selected DocTypes (Customer, Item, Sales Order, plus your custom doctypes)

Permission-aware retrieval — queries run under the user's session and honor Frappe permissions, role profiles, and User Permissions

Incremental sync via `hooks.py` doc events (`after_insert`, `on_update`, `on_trash`) so the index tracks record changes automatically

Scheduled re-embedding through `scheduler_events` for periodic full or delta re-indexing

Knowledge-base grounding: ingests uploaded PDFs, Markdown, and Frappe/ERPNext docs alongside structured data

Configurable chunking and embedding strategy set in a dedicated Settings DocType — no code edits to retune

Pluggable LLM and embedding provider (self-hosted model or external API) selectable per instance for data-residency control

Whitelisted server methods and Frappe REST API endpoints so the assistant is callable from portals or external apps

Chat UI delivered as a native Frappe page / client script, embedded in the Desk

Source citations link directly back to the underlying ERPNext document for verification

Query and answer audit log DocType capturing who asked what, retrieved context, and latency

Rate limiting and token-budget controls per user/role to keep LLM costs predictable

Configurable fallback response when no relevant grounded context is found (refuses to guess)

Built and tested against Frappe/ERPNext v15 and v16

Built to order, done for you

No DIY setup — a working app, built, installed and supported by ECOSIRE.

You order

Start with a one-time build price. We scope it with you at kickoff.

We build & install

ECOSIRE builds, configures and installs it on your ERPNext.

Go live + support

You go live in about 2–4 weeks, with a post-launch support window.

About this Product

Your team already lives in ERPNext, but the answers they need are scattered across thousands of Sales Orders, Items, Stock Ledger Entries, HR policies, and internal SOPs. ERPNext ships excellent list views, report builders, and the Frappe global search, but none of them answer a plain-English question like "which customers in the GCC bought Item X last quarter and still have an open balance?" or "what's our return policy for perishable goods?" Staff end up pinging colleagues, exporting to spreadsheets, or clicking through five reports. Native full-text search matches keywords, not meaning, and it has no idea how to combine structured DocType data with the prose sitting in your knowledge base.

The ERPNext AI Knowledge Assistant is a proper Frappe app (its own module, hooks.py, and DocTypes) that we build and install directly on your bench. It adds a retrieval-augmented-generation layer over your instance: a background pipeline embeds the DocTypes you choose (Customer, Item, Sales Order, HR policy documents, uploaded PDFs, and your own custom doctypes) into a vector index, and a chat interface lets users ask questions in natural language. When a question comes in, the app runs vector search to retrieve the most relevant records and document chunks, grounds the LLM strictly on that retrieved context so answers are traceable to real ERPNext data, and returns a cited response with links back to the source documents. Because it is grounded, it does not "hallucinate" figures — if the data is not in your instance, it says so.

Technically, the app is built the Frappe-native way. Ingestion runs on hooks.py doc events (on_update, after_insert, on_trash) plus scheduler_events for periodic re-indexing, so the vector store stays in sync as records change — no manual re-import. Every query is executed under the calling user's session, and retrieval respects Frappe permissions, role profiles, and User Permissions: a user only ever gets answers grounded in documents they are allowed to read, so a Sales user can't surface HR salary data through the chatbot. The chat UI is delivered as a client script / Frappe page, and all server logic is exposed through whitelisted methods and the standard Frappe REST API, so you can also call the assistant from a portal, a custom app, or an external tool. Configuration (which doctypes to index, chunk sizes, the embedding/LLM provider and endpoint) lives in a Settings DocType, editable by administrators without code changes.

This is build-to-order, not an off-the-shelf download. We start with a short scoping call to map the doctypes, documents, and questions that matter to you, plus your preferred LLM provider (self-hosted or API) and data-residency requirements. We then build your version of the app against Frappe/ERPNext v15 or v16, validate it on a staging copy of your data, run UAT with your team, and deploy to production with a rollback plan. Typical delivery is 2 to 4 weeks from confirmed scope. You receive the full source code and git repository so nothing is locked behind a black box, followed by a post-go-live support window.

What you get

Installable source code for your version of the ERPNext AI Knowledge Assistant app

Installation and configuration on your bench (staging then production), including provider and index setup

Technical documentation: architecture, DocTypes, hooks, whitelisted methods, and Settings reference

User guide plus a live training session for your team

UAT on a staging copy of your data with a documented rollback plan

Post-go-live support window for defect fixes and tuning

Full git repository handover with commit history and README

Configuration handbook for adding new doctypes/documents to the index later

Who this is for

ERPNext Operations Lead

Runs day-to-day ERPNext for a growing company and is tired of staff re-asking the same questions or exporting reports. Wants a single natural-language front door to sales, inventory, and policy data without training everyone on the report builder.

IT / ERP System Owner

Owns the ERPNext instance and its integrations. Needs an AI layer that respects Frappe permissions, keeps data on their chosen LLM provider, and ships as auditable source code and a git repo rather than an opaque SaaS black box.

Customer Support & Knowledge Manager

Manages SOPs, policies, and product docs. Wants agents to get grounded, cited answers pulled from both ERPNext records and the knowledge base, so responses are accurate and traceable rather than invented.

Finance / Operations Analyst

Frequently answers ad-hoc cross-doctype questions (open balances, orders by region, item movement). Wants to ask in plain English and get answers grounded in live ERPNext data with links back to the source records.

How ERPNext AI Knowledge Assistant (RAG Chatbot) Compares

Criterion	ECOSIRE	Custom Build	Competitor	ERPNext Native
Answers plain-English questions over ERPNext data	Yes — RAG grounded on your DocTypes and docs	Possible if you build the retrieval layer yourself	Usually keyword search or canned reports only	No — list views, reports, and keyword global search only
Permission-aware answers	Enforces Frappe permissions, role profiles, User Permissions per query	Depends entirely on your implementation discipline	Often bolted on and inconsistent	Permissions apply to Desk, but no AI query layer exists
Grounding / hallucination control	Strictly grounded on retrieved records, cites sources, refuses when absent	Only if you engineer grounding correctly	Frequently ungrounded generic LLM wrapper	Not applicable — no LLM layer
Index stays in sync with data	Auto via `hooks.py` doc events + `scheduler_events`	You must build and maintain the sync jobs	Often manual or batch re-import	Global search reindex only, no vector store
LLM provider & data residency	Configurable — self-hosted or API, chosen with you	Whatever you wire up and maintain	Usually locked to the vendor's cloud	None
Fit to your doctypes and questions	Scoped to your data, custom doctypes, and workflows	Fully bespoke but you own all the effort	Generic, limited configuration	Generic ERPNext only
Source code & ownership	Full source + git repo handover	You own it — and its full build cost	Closed-source, subscription lock-in	Open source core, but no such feature
Delivery & support	2-4 weeks build-to-order, UAT, rollback, post-go-live support	Depends on your team's bandwidth and skills	Install fast, but limited ERPNext-specific support	Immediate, but nothing to deliver

Frequently Asked Questions about ERPNext AI Knowledge Assistant (RAG Chatbot)

Is this an existing app I can download and install today?

No. It is build-to-order. ECOSIRE builds your version of the app against your ERPNext instance, DocTypes, and chosen LLM provider, then installs and supports it. There is no instant download or off-the-shelf marketplace listing.

How long does delivery take?

Typically 2 to 4 weeks from confirmed scope. After a short scoping call we agree the doctypes, documents, and provider, then build, validate on staging, run UAT with your team, and deploy to production. Larger or highly custom scopes can run longer and we will tell you upfront.

Will the chatbot expose data users aren't allowed to see?

No. Retrieval runs under the calling user's session and honors Frappe permissions, role profiles, and User Permissions. A user only gets answers grounded in documents they are already permitted to read, so a Sales user cannot surface HR or salary data through the assistant.

How does it stay accurate and avoid making up numbers?

It uses retrieval-augmented generation: the LLM is grounded strictly on records and document chunks retrieved from your ERPNext instance, and every answer cites its source documents. If the relevant data isn't in your instance, the assistant says so instead of guessing.

Which LLM do we have to use, and where does our data go?

The provider is configurable per instance. You can point it at a self-hosted model for full data residency or at an external API. We set this in a Settings DocType during the build to match your privacy and compliance requirements.

Which ERPNext versions are supported, and how are updates handled?

We build and test against Frappe/ERPNext v15 and v16. You receive the full source and git repo, plus a post-go-live support window for defect fixes and tuning. Ongoing enhancements, new-doctype indexing, or version upgrades can be handled under a support arrangement.

Does the index stay current as our data changes?

Yes. Ingestion is wired to `hooks.py` doc events so new and updated records are re-embedded automatically, and `scheduler_events` run periodic delta or full re-indexing. You don't re-import anything manually.