Our team is developing both the frontend and backend systems for The Elefant, an AI-powered sales assistant platform. We're building comprehensive solutions that automate customer interactions on websites, including appointment booking functionality, product recommendation engines, and CRM integration systems to boost conversion rates and enhance customer satisfaction.
Traditional online sales processes lacked personalization and efficiency, leading to lower conversion rates and customer engagement.
We developed the entire project, delivering a comprehensive AI sales assistant solution that seamlessly integrates with websites to enhance user interaction and drive sales.
Frontend development
Backend systems development
Appointment booking functionality
Product recommendation engines
CRM integration systems
Customer interaction automation
Conversion rate optimization
Coordinating frontend and backend development teams to ensure seamless integration and consistent functionality across the platform.
Designing a flexible appointment booking system that could accommodate various business types, time zones, and availability patterns.
Creating intelligent recommendation algorithms that could effectively suggest relevant products based on limited user interaction data.
Developing a standardized approach to integrate with diverse CRM systems while maintaining data consistency and synchronization.
Implemented a collaborative development process where frontend and backend teams worked in synchronized sprints with shared planning and review sessions.
Built the platform using reusable components that could be assembled in different configurations to meet various business requirements and use cases.
Designed sophisticated conversation flows that could intelligently guide customers through appointment booking and product selection processes.
Developed an adapter pattern implementation that standardized data exchange with various CRM systems through a unified API interface.
Implemented comprehensive performance monitoring and optimization strategies to ensure real-time responsiveness across all user interactions.
The Elefant is an AI sales assistant that businesses drop into their websites to chat with visitors, recommend products, book appointments and push leads into their CRM. It runs as a Next.js and React front end backed by a Node.js and Express GraphQL API over PostgreSQL, with a conversation orchestrator that calls a large language model and a set of tool services for booking, recommendations and CRM sync. A modular monolith with clear service boundaries suited a fast-moving product that had to serve many small businesses from one deployment.
A lightweight embeddable chat widget on customer websites, plus a Next.js admin console where businesses train the assistant and review leads.
A single GraphQL endpoint on Node.js and Express authenticates widgets by site key and operators by session, and streams chat responses.
A conversation orchestrator manages each visitor dialogue and invokes tools: appointment booking, product recommendation and CRM update.
PostgreSQL holds tenants, conversations, products, bookings and leads; a vector index stores embedded business knowledge for retrieval.
A large language model drives dialogue over retrieved business knowledge, while adapters connect to CRMs, calendars and Razorpay for payments.
Cloud-hosted Node services behind a CDN that serves the widget script globally, with performance monitoring on every chat turn.
A business pastes the widget script into its site; the admin console ingests site pages and product data into the vector knowledge index.
A visitor opens the chat; the widget calls the GraphQL gateway with the site key and the orchestrator loads tenant settings.
The orchestrator retrieves relevant business knowledge, prompts the language model, and streams the reply back to the widget.
When the visitor wants a product or a slot, the model invokes the recommendation or booking tool, which reads availability from PostgreSQL.
A confirmed booking or qualified lead is written to PostgreSQL and pushed through the CRM adapter to the business's CRM.
Payments, where needed, are collected through Razorpay, and the admin console shows conversations, leads and conversion analytics.
The widget, admin console and mobile apps needed different slices of the same data; one typed GraphQL schema avoided a sprawl of REST endpoints across frontend and backend teams.
Booking and recommendations need deterministic business logic; keeping them as explicit tools the model invokes kept the LLM conversational while real actions ran in tested code.
Every client used a different CRM; normalising lead and contact exchange behind one adapter interface let the team add CRMs without touching the conversation flow.
Serving dozens of small businesses from one Node.js deployment with tenant isolation in PostgreSQL was cheaper and faster to iterate than per-client infrastructure at this stage.
“The AI integration and development work has transformed our sales process, delivering exceptional results for our clients across various industries.”
We ship MVPs, AI agents, and production systems in 8 weeks. Bring the rough spec — we'll scope the build inside a week with a fixed timeline and quote.
