As customer expectations continue to rise, real-time responses are no longer optional. To meet this demand, businesses must provide instant, accurate responses while running operations efficiently. Whether it’s a customer asking “Where’s my order?” at midnight or a sales team requiring real-time insights, manual handling is inefficient and costly. Smart chatbot agents powered by large language models (LLMs) like OpenAI’s GPT-4 or Google’s Gemini are revolutionizing this by understanding natural language, retrieving data, and responding conversationally saving time, cutting costs, and enhancing customer satisfaction.
However, connecting LLMs to business data has often required complex coding and expertise. DataFinz changes this with its no-code platform, allowing users to ingest data from diverse sources, build intelligent prompts, and integrate seamlessly with LLMs to create powerful chatbot agents. Ideal for e-commerce, customer support, or sales, DataFinz enables anyone to harness AI without coding. In this blog post, we wil guide you through using DataFinz to build a chatbot agent for e-commerce order tracking, highlighting its no-code features for data ingestion, prompt creation, LLM integration, API exposure, and built-in chatbot UI.
Consider an e-commerce business selling clothing online, overwhelmed by queries like “Where’s my order #123?” or “When will my package arrive?” These distract staff, delay responses, and frustrate customers. A chatbot agent integrated with real-time order data could handle these 24/7 with natural, accurate replies.
The data such as customer IDs, order details, shipping statuses, resides in various systems like AWS S3 buckets, OMS databases (e.g., SQL Server), or platforms like Shopify. DataFinz unifies these into a no-code pipeline, creating prompts and APIs that LLMs can use for conversational responses. Let’s explore the process.
Follow this user-friendly guide to create an order-tracking chatbot agent using DataFinz’s no-code dashboard. The process leverages the ODS (Operational Data Store) Pipeline for data ingestion and the AI Pipeline for prompt building, LLM integration, and chatbot enablement, allowing even non-technical users to complete it. We will reference relevant screenshots to illustrate each step.
Start by loading your data into DataFinz’s centralized store from any source or format.
3. Configure the Pipeline: Choose your source (e.g., AWS S3 for files containing order data or OMS Data Sources) and destination (e.g., SQL Server DB or ODS). DataFinz handles the extraction and loading automatically.
4. Select Objects and Define Structures: View available objects from the source (e.g., CSV files in S3, as shown in the image below). Select primary keys for tables and let DataFinz generate complete DDLs (Data Definition Language) for table structures. Referential integrity is set after tables are defined and loaded, ensuring data consistency without coding.
Why It Matters: This no-code ingestion unifies disparate data sources, providing a solid foundation for your chatbot agent to access accurate, real-time information.
Organize your ingested data into an intelligent prompt that defines how the LLM interacts with it.
1. Access the AI Pipeline Tab: Switch to the AI Pipeline section in the dashboard (as shown in the image below).
2. Create a New Prompt: Connect to the table structure/model from your ODS Pipeline. Choose options like using a Vector DB for advanced search, specifying the purpose (e.g., Q&A for order queries or analytics for insights), and incorporating algorithms to process data.
3. Select Tables: Pick the relevant tables for this prompt (as shown in the image below), such as Orders (with columns for order ID, customer ID, product SKU, order date, and status), Customers (with ID, name, email), and Shipping (with order ID, tracking number, carrier, estimated delivery).
4. Build Relationships: Use the visual interface to define relationships between tables (as shown in the image below). This can involve simple SELECT statements or linking data (e.g., joining Orders and Shipping via order ID). This is the only step where minimal SQL-like input may be needed for complex joins, but it’s straightforward and code-optional.
Why It Matters: The prompt structures your data model, enabling the LLM to query and reason over related information for context-aware responses.
Link your prompt to an LLM for natural language processing and access the built-in chatbot interface.
1. Go to the LLM Tab in AI Pipeline: Select this to integrate your built prompt with available LLM connections (e.g., OpenAI’s GPT-4, as shown in the image below).
2. Set Up Authentication: Enter credentials like API keys or tokens (as shown in the image below). DataFinz handles secure authentication, ensuring privacy and compliance. The entire prompt enablement is managed in the backend using the LangChain framework, without any codes (except optional relationship building for table data).
3. Access the Chatbot Agent UI: In the LLM tab, click the fourth icon to open the DataFinz Chatbot Agent UI (as shown in the image below). This enables one-way interactions to query business-related functions based on the defined data model and relationships. The UI view (as shown in the image below) allows simple testing of queries, such as order tracking. However, DataFinz prefers using highly customized two-way communication chatbot agents through available open-source options for more advanced, interactive experiences.
Why It Matters: This integration powers the chatbot agent’s intelligence, with the built-in UI providing an immediate way to test and interact, while supporting preferences for customized agents.
Make your setup accessible to external chatbots.
2. Configure for Consumption: Share the API endpoint with chatbot platforms like Amazon Q, Microsoft Copilot, or custom agents. The API handles requests, querying your data via the prompt and LLM for responses.
3. Test the API: Use Postman ilke tools to send sample requests (e.g., querying order #123) and verify JSON outputs like:
Tailor the agent to your needs using DataFinz’s flexible features.
1. Enhance Prompts: Add business rules to prompts, like prioritizing urgent orders or suggesting upsells based on order data.
2. Fine-Tune LLM Behavior: Use DataFinz’s options to align responses with your brand tone (e.g., friendly for e-commerce).
3. Incorporate Advanced Features: Enable Vector DB for semantic search or algorithms for predictive analytics in queries. Customize further by integrating with open-source tools for two-way interactions beyond the built-in UI.
Why It Matters: Customization ensures the agent delivers personalized, brand-aligned interactions that engage users effectively.
Tailor the agent to your needs using DataFinz’s flexible features.
1. Enhance Prompts: Add business rules to prompts, like prioritizing urgent orders or suggesting upsells based on order data.
2. Fine-Tune LLM Behavior: Use DataFinz’s options to align responses with your brand tone (e.g., friendly for e-commerce).
3. Incorporate Advanced Features: Enable Vector DB for semantic search or algorithms for predictive analytics in queries. Customize further by integrating with open-source tools for two-way interactions beyond the built-in UI.
Why It Matters: Customization ensures the agent delivers personalized, brand-aligned interactions that engage users effectively.
Launch and verify your setup.
1. Integrate with Chatbot Platforms: Connect the API or use the built-in Chatbot Agent UI for deployment on websites, apps, or messaging platforms. For advanced two-way communication, leverage open-source options as preferred by DataFinz.
2. Test Thoroughly: Simulate queries (e.g., “Where’s my order #123?”) via the API or Chatbot UI to check response accuracy and naturalness.
3. Monitor Performance: Use DataFinz’s dashboard for API logs, uptime, latency, and alerts to maintain reliability.
Why It Matters: Easy deployment and monitoring ensure scalability and a flawless user experience, with the built-in UI simplifying initial testing.
DataFinz’s no-code platform, with ODS and AI Pipelines, excels in building LLM-powered chatbot agents:
Ready to automate customer support or sales with a smart chatbot agent? DataFinz lets you ingest data, create prompts, integrate LLMs, and use built-in UIs or APIs in hours—no code needed. Get started:
Make the most of AI with DataFinz—start your free trial and improve your business today!
