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Bringing Better Results with AI: Matillion’s Product Journey

Who doesn’t want to be the first to incorporate AI capabilities in their product? Creating that wow factor that makes everyone want your product (rather than your nearest and dearest competitor).

While it may seem like a bit of a space race for everyone to build AI into product offerings, at Matillion, we wanted to ensure that we are incorporating AI into our platform in the right way. We believe that building an AI-forward platform requires a thoughtful approach that prioritizes human productivity, transparency, and customer obsession.

In line with this, we have established our AI Charter – a set of core principles that guide our integration of AI into our platform. We believe that AI should be used with strong ethical guardrails in place. These principles govern our own AI development efforts and ensure we stay customer-focused as we collectively figure out how to use this new technology.

Six Principles that Drive Our AI Efforts

1. Human in the loop: At Matillion, we believe in leveraging AI to make humans more productive, not replaceable. Our goal is to enable users to use/veto/edit/reject a response from a large language model rather than blindly sending a response to an end customer. 

2. We need a kill switch: We understand the importance of maintaining control over critical processes and systems. No process or system should be wholly dependent on AI to ensure that there is always a fail-safe option available.

3. Do no harm: AI should be used for good, benefiting both individuals and organizations. Sure, we are committed to leveraging AI to solve complex problems but not at all costs - we try not to contribute to any harm or negative outcomes (e.g., privacy violations, algorithmic bias, inaccuracies, etc.).

4. There is always an opt-out: Alongside an overarching fail-safe option, everyone needs to respect their end users' data privacy wishes and understand that not everyone may be comfortable with AI. As such, we need to provide the tools to help manage opt-out decisions for all AI interactions.

5. Feels Good to Me (FGTM): We promote openness and transparency by open-sourcing our AI models, allowing users to understand how our AI tools work and make informed decisions.

6. Show your work: To enable transparency and accountability, we log all prompts between users and chatbots, enabling future audits by our users.

Key Use Cases: Why leverage AI in data integration?

We can think of millions of ways to apply AI in a business. Well, certainly thousands. Here are some examples to get the ball rolling. 

Data Analytics:

Put all your data to work (unstructured, semi-structured, structured) for AI and analytics like never before, leveraging generative AI. 

  1. Transforming Unstructured Data for Analytics
    • Analyze internal documented notes (e.g., clinical notes) to find patient feedback
    • Review customer feedback or surveys to better understand sentiment and identify key issues
    • Extract valuable information from survey data, 10K reports, and other unstructured sources
  2. Data Management 
    • Use LLMs to clean, standardize, and format data for AI and analytics (e.g., normalizing date formats and handling missing values)
    • Leverage AI for data classification and uncovering relationships (e.g., identify PII or claims data)
    • Enrich analytics with AI-generated insights from multiple data sources (e.g., integrating customer data from CRM and social media)
Business Processes + Operations:

Empower the business with AI, open up new-gen AI use cases, and lay the foundation for custom AI applications.

Customer Support

  • Categorize issues and generate insightful summaries for better support and product improvement
  • Automate responses to customer tickets by integrating ticketing systems with LLMs (e.g., generating answers based on knowledge base)
  • Supplement LLM prompts with documentation retrieved from a vector database (RAG) (e.g., providing context-specific information)

Sentiment Analysis and Forecasting

  • Analyze unstructured customer survey data for customer sentiment analysis (e.g., identifying positive and negative feedback)
  • Analyze market trends, customer sentiment, and social media data for accurate forecasting
  • Identify at-risk customers and tailor retention strategies based on AI-driven insights

Reverse ETL AI Back to Source Systems

  • Write back AI-generated insights and predictions to source systems for seamless integration
  • Enable real-time decision-making by integrating AI insights into business processes
  • Automate data updates across systems to maintain consistency and accuracy

Interactive Applications for Knowledge Workers:

Empower your knowledge workers with AI-powered tools and self-service capabilities to get contextualized answers specific to your business.

RAG with Vector Database for Contextualized Answers

  • Enable RAG by curating business documentation to support numerous AI use cases (e.g., creating a searchable knowledge base)
  • Integrate data from websites, documentation management systems, CRMs, HRMs, and other document stores into a vector database (e.g., creating a unified data source)
  • Enable knowledge workers to quickly lookup contextualized answers to business questions using RAG (e.g., providing instant access to relevant information)

Building Interactive Chatbots

  • Develop AI-powered chatbots for internal and external use cases (e.g., HR support chatbot, customer service chatbot)
  • Integrate chatbots with existing business systems and data sources (e.g., CRM, ERP, HR systems)
  • Provide knowledge workers with self-service access to critical information and insights 

Empowering Knowledge Workers with AI

  • Build AI-assisted tools for data analysis, reporting, and decision-making (e.g., automated report generation, predictive analytics)
  • Analyze and understand employee sentiment using AI techniques (e.g., sentiment analysis on employee feedback)
  • Streamline workflows and increase productivity with AI-powered automation (e.g., intelligent document processing, task prioritization)

What's Next: Request a demo!

Matillion's AI-driven data pipeline platform empowers data teams with efficiency, insights, and enhanced user experiences, all while prioritizing transparency and data security.

Get started today! Request a demo now to explore our cutting-edge AI features and learn how to implement AI in your organization.

Ciaran Dynes
Ciaran Dynes

Chief Product Officer

Ciaran Dynes is Chief Product Officer at Matillion, leading the product strategy to provide users with the technology they need to improve data productivity. Ciaran is an accomplished product leader with over 20 years of experience in global product development companies, driving cross-functional teams, managing products from cradle to maturity, and providing the foundation for new product development investments. Before joining Matillion, he held a series of roles at leading integration software vendors including Talend, Progress Software, and IONA Technologies.