for Financial Services and Insurance

Your core infrastructure relies on unstructured data. Enrich and structure this untapped data helps you streamline workflows with AI-based automations across your organization.
Achieved without tapping into engineers or labeling data to train an algorithm.

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Findings from our financial institutions use cases

1. Enhance Customer Experience by analyzing every customer touchpoint, setting up alerts and enabling real-time routing

Every customer touchpoint will be categorized with the root cause so that you have a top-down view. Customize your analysis for each product: credit cards, loans, mortgages, wire transfers, checking and saving accounts. Set up a prioritization system for high-risk issues, and real-time routing to the right team or individuals.

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2. Build workflows to improve efficiency and ensure compliance using support tickets, calls notes or transcripts. helps you enrich all of your customer interactions with concept-based tags that you build in a visual interface. Drive more efficient operations by cutting response times and identifying the issue in real-time. Audit your processes to ensure they meet the requirements for each financial product and legislation.

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3. Process insurance claims defining your own  tagging system
(concept taxonomy)

Insurance claims are the most relevant moment in your customers’ journey, but they are messy. helps you process claims accurately and efficiently by allowing you to define a tagging criteria with the concepts that surfaces (concept taxonomy).  Avoid unnecessarily long wait times (hours or days) when minutes can be critical to retain your customers.

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4. Build risk models based on customer interactions like call notes or emails

Enhance your collections and credit processes by adding a new source of untapped data: customer interactions (call notes or emails or chats). If there are certain expressions indicative of risk, you’ll be able to group them visually and include a new variable in your models.

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Your benefits after using lang


Time Savings on manually reviewing tickets, surveys and cases


Average time to build a workflow in for an operations person


Monthly interactions you will structure without tapping into engineers

In our customers’ words

Alberto foto

Alberto Iriarte

Strategy and Big Data Project Lead in the Office of the CDO, CaixaBank has an impressive product that we saw early on could be used as an intelligence layer on top of all our free-text data to create our classification models.
The fact that they have such an accessible UI that our CX team loved was a great signal for us to buy it from the office of the CDO so that every business area can leverage it.

We’re also excited about how we’ve been working closely with the team on their governance roadmap as their goal of making NLP accessible is proving that the amount of users and projects can grow quickly.

Case Study

How much can you save with

How many support tickets do you receive monthly?

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processes automated
agent hours saved
32000 $
cost saved
* Calculations based on...

Automate tedious work as a CIO/CTO

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