AI Sales Assistant & Conversation Copilot for ERPNext

प्रमुख सुविधाएँ

AI reply drafting on Lead, Opportunity and CRM Deal forms via a client-script-injected copilot panel, with tone/length controls and one-click insert into the Email/Communication composer

WhatsApp reply drafting through a whitelisted method callable from your existing WhatsApp gateway or the ERPNext WhatsApp integration, using the deal's full timeline as context

Deal and thread summarization that reads the `Communication` and `CRM Note` history and writes a persistent `AI Deal Summary` DocType record, regenerated on new activity

Next-best-action suggestions persisted as `Next Best Action` records and optionally auto-created as `ToDo`/`Event` against the deal owner

Intent scoring written to a custom `intent_score` field on Opportunity so you can filter List Views, build Kanban lanes and Query Reports on hot deals natively

Sentiment scoring per inbound Communication, surfaced as an indicator on the timeline and rolled up to the deal

Meeting-note extraction: paste raw notes into a CRM Note and a whitelisted parser creates structured follow-up activities, action items and updates deal fields

Server-side LLM connector supporting OpenAI, Anthropic, Azure OpenAI or a self-hosted/on-prem model, keys stored encrypted in `AI Copilot Settings` single DocType — never exposed to the browser

`hooks.py` `doc_events` on Communication/Opportunity that enqueue summarization and re-scoring via `frappe.enqueue` so saves are never blocked

`scheduler_events` that re-score stale open deals, expire old drafts and post a daily 'deals needing attention' digest

Full permission-awareness: every whitelisted method runs as the acting user, honouring Role Profiles, DocType permissions and permlevel field security

Every generated draft stored as an editable `AI Draft` record with model, prompt version and token usage logged for audit and cost tracking

All copilot capabilities exposed over the standard Frappe REST API for reuse from mobile PWA, n8n/automation or third-party channels

Prompt templates managed as data (not hardcoded) in a `Prompt Template` DocType so your sales language and playbook can be tuned without a code deploy

Rate-limiting and per-user/per-day token budgets enforced server-side to keep LLM spend predictable

ऑर्डर पर निर्मित, पूरी तरह आपके लिए तैयार

स्वयं सेटअप की ज़रूरत नहीं — ECOSIRE द्वारा निर्मित, इंस्टॉल और समर्थित एक कार्यशील ऐप।

आप ऑर्डर करते हैं

एक बार के निर्माण मूल्य से शुरू करें। किकऑफ़ पर हम आपके साथ मिलकर दायरा तय करते हैं।

हम बनाते और इंस्टॉल करते हैं

ECOSIRE इसे आपके ERPNext पर बनाता, कॉन्फ़िगर और इंस्टॉल करता है।

लाइव + समर्थन

आप लगभग 2–4 सप्ताह में लाइव हो जाते हैं, लॉन्च के बाद समर्थन अवधि के साथ।

इस उत्पाद के बारे में

Your sales team already lives in ERPNext — Leads, Opportunities, CRM Notes, the communication timeline — but the actual selling work happens in their heads and their inboxes. Reps re-read long email threads to remember where a deal stands, retype the same qualifying replies, and forget the next follow-up because ERPNext core has no notion of "what should I do next on this deal." ERPNext's native CRM captures the record of what happened; it does not read the conversation, draft the reply, or tell a rep which of forty open Opportunities is actually about to close. That gap is where hours leak and warm deals go cold.

The AI Sales Assistant & Conversation Copilot is a proper Frappe app (ai_sales_copilot) that ECOSIRE builds and installs directly on your ERPNext bench. It adds a copilot panel to the Lead, Opportunity and CRM Deal forms via client scripts and a bundled Vue/JS widget, backed by whitelisted server methods (@frappe.whitelist()) that assemble context from the Communication and CRM Note DocTypes, call your chosen LLM (OpenAI, Anthropic, Azure OpenAI or a self-hosted model) through a server-side connector, and stream drafts back into the form. Because every call runs server-side, your API keys live in Frappe's encrypted Site Config / a dedicated AI Copilot Settings single DocType — never in the browser — and every request respects the acting user's role permissions and DocType-level permlevels, so a copilot suggestion can never surface a record the rep isn't allowed to see.

Technically, the copilot is wired into ERPNext's own event fabric. hooks.py doc_events on Communication and Opportunity trigger background enqueues (frappe.enqueue) so summarization and intent re-scoring happen off the request thread and never block a save. Scheduler events (scheduler_events hourly/daily) re-score stale open deals and post digests. Custom DocTypes — AI Draft, AI Deal Summary, Intent Score and Next Best Action — persist every generated artifact so output is auditable, editable and reportable, not ephemeral chat. Intent and sentiment scores are written to custom fields on Opportunity so you can build native ERPNext List View filters, Kanban lanes and Query Reports on "hot" deals without any external BI tool. Meeting notes pasted into a CRM Note are parsed by a whitelisted extraction method into structured ToDo / Event / follow-up activity records against the deal. Everything is exposed over the standard Frappe REST API, so the same drafting and scoring methods are callable from your WhatsApp gateway, a mobile PWA, or an n8n workflow.

Because this is build-to-order, nothing is a black box you download and hope fits. We start with a scoping call to confirm your CRM flavour (ERPNext CRM vs the standalone Frappe CRM app), your LLM provider and data-residency constraints, the channels you want drafting for (email via the Email Account, WhatsApp via your existing gateway), and your role profiles. ECOSIRE then builds your version of the app against Frappe/ERPNext v15 or v16, validates it on a staging copy of your site with your own anonymised deals, and installs it on production with a rollback plan. Typical delivery is 2–4 weeks from confirmed scope. You receive the full source in a git repo, so you (or any Frappe developer) own and can extend it forever — no per-seat lock-in, no calling home.

What you get

Installable source code for your version of the `ai_sales_copilot` Frappe app, built for Frappe/ERPNext v15 or v16

Installation and configuration on your ERPNext instance (LLM provider wiring, `AI Copilot Settings`, role/permission setup)

Technical documentation: DocType schema, whitelisted method reference, hooks.py events, scheduler jobs and REST API endpoints

End-user guide plus a live training session for your sales team and CRM admins

UAT on a staging copy of your site with your anonymised deals, plus a documented rollback plan before production cutover

Post-go-live support window for bug fixes and configuration adjustments

Git repository handover with full commit history so you or any Frappe developer can extend it

Prompt-template starter set tuned to your sales playbook during scoping

Who this is for

Head of Sales / Sales Director

Runs a team already on ERPNext CRM and wants measurable productivity: faster reply times, consistent qualifying language and a reliable 'which deals are hot' signal, without ripping out ERPNext for a separate AI sales tool.

ERPNext / Frappe System Administrator

Owns the bench and cares that AI runs server-side, keeps API keys out of the browser, respects role permissions and permlevels, and ships as a clean installable app they can upgrade across v15/v16 rather than a pile of ad-hoc client scripts.

SDR / Account Executive

Lives in the Opportunity form all day and needs the copilot to draft the next email or WhatsApp reply, remind them of the next step, and summarize a long thread in seconds — inside the tool they already use, not another tab.

RevOps / CRM Manager

Wants intent and sentiment scores as native ERPNext fields so they can build Kanban boards, Query Reports and dashboards on pipeline health, and wants prompt templates that encode the company's actual sales methodology.

How AI Sales Assistant & Conversation Copilot for ERPNext Compares

Criterion	ECOSIRE	Custom Build	Competitor	ERPNext नेटिव
Runs inside ERPNext CRM	Native panel on Lead/Opportunity forms via client scripts + whitelisted methods	Possible but you build the Frappe integration yourself	Often a generic add-on with shallow form hooks	No AI drafting or scoring in ERPNext core
Reply drafting (email + WhatsApp)	Both channels, context-aware, one-click insert	Whatever you scope and build	Usually email-only or template macros	Manual typing only
Intent & sentiment scoring	Written to custom fields — filterable in List/Kanban/Reports	Buildable but needs schema + scoring design	Rarely; if present, siloed in the add-on	Not available
LLM key security	Server-side only, encrypted in AI Copilot Settings	Depends entirely on your implementation	Varies; some route through a third-party cloud	N/A
Permission awareness	Honours Role Profiles + permlevels on every call	You must enforce it yourself	Often bypasses fine-grained permissions	Standard ERPNext permissions, no AI layer
Data & output ownership	Full git repo handover, you own the source	You own it but carry all build effort	Closed source, vendor-hosted lock-in	You own ERPNext, but no AI capability
Delivery model	Build-to-order, 2–4 weeks, UAT + rollback	Months of in-house dev and iteration	Instant install, generic fit	Already installed, no AI
Ongoing support	Post-go-live window + optional retainer	Your own team maintains it	Vendor SaaS support, upgrade at their pace	Community/your admin

Frequently Asked Questions about AI Sales Assistant & Conversation Copilot for ERPNext

How long does delivery take?

This is a build-to-order app, not an instant download. Typical delivery is 2–4 weeks from confirmed scope. After a short scoping call we lock the DocTypes, channels, LLM provider and permissions, build your version, validate it on a staging copy of your site, then install on production with a rollback plan. Complex WhatsApp or multi-model requirements sit at the longer end of that range.

Which LLM providers can it use, and do you send my data anywhere I can't control?

You choose the provider: OpenAI, Anthropic, Azure OpenAI, or a self-hosted/on-prem open model if data residency matters. All calls run server-side from your Frappe instance; the API key is stored encrypted in an `AI Copilot Settings` DocType and is never exposed to the browser. Only the deal context needed for a given draft is sent to your chosen model — nothing routes through ECOSIRE.

Which versions of ERPNext and Frappe are supported?

We build against Frappe/ERPNext v15 and v16. During scoping we confirm your exact version and whether you use ERPNext's built-in CRM or the standalone Frappe CRM app, and target the build accordingly. The app is packaged as a standard installable Frappe app so it upgrades cleanly with your bench.

How do you handle support and updates after go-live?

Every engagement includes a post-go-live support window for bug fixes and configuration adjustments. Because you receive the full git repository, you or any Frappe developer can extend it independently. Beyond the included window, ECOSIRE offers ongoing support and enhancement retainers — but you are never locked in, since you own the source.

Will the AI ever show a rep records they aren't allowed to see?

No. Every whitelisted method runs as the acting user and honours ERPNext Role Profiles, DocType permissions and permlevel field-level security. The copilot assembles context only from records the current user can already read, so an AI suggestion cannot leak a restricted Lead, Opportunity or Communication.

Is this just a chatbot bolted on, or is it real ERPNext data?

It's real ERPNext data. Drafts, summaries, intent scores and next-best-actions are stored in dedicated DocTypes (`AI Draft`, `AI Deal Summary`, `Intent Score`, `Next Best Action`) and custom fields on Opportunity — so they're auditable, editable, reportable and filterable in native List Views, Kanban and Query Reports, not throwaway chat messages.

Can we tune the AI's language to our own sales playbook?

Yes. Prompt templates live in a `Prompt Template` DocType as editable data, not hardcoded strings, so your qualifying questions, tone and methodology can be adjusted by an admin without a code deploy. We seed a starter set matched to your playbook during the build.