How It Works:
The Proliferation of Unstructured Data in Today’s Enterprise
Enterprise companies deal with massive data sets. In a typical company, over 80% of enterprise data is unstructured.
This data is stored in many forms, from emails, documents, images, social media conversations, and more, growing at more than 55% per year.
This data needs to be analyzed to improve business operations and customer service.
Every customer conversation contains valuable information about the customer’s preferences and needs, and the company’s products, services, and processes.
Email is the most popular communication medium between a company and its external customers and vendors. 95% of customer service teams use email to communicate with customers.
With billions of dollars lost annually due to poor customer service and experience, understanding what your customers want, recognizing their intent automatically, and handling incoming service requests at scale, have never been more critical for enterprise companies.
130 Billion+
Business Emails Sent Daily
SmartBots Use Natural Language to Understand Accounting Emails
What Should be Automated?
It has become critical to unlocking the value in business communications by mining, monitoring, and extracting context and semantics from the unstructured data in email and other communications methods.
Then, using AI/ML and Natural language technologies, the ability to take automated action once intent detection is complete is the holy grail of customer service and fulfillment. These are often referred to as “last-mile-workflows” in the business world.
The hallmarks of the new era of technology include leveraging AI-driven last-mile workflows and decision support across the massive volumes of data that the enterprise generates.
This approach means augmenting people and human operators with intelligent automation, delivering insights consumable in multiple form factors, and enabling the business to be responsive to customer and vendor needs at all times.
AI-powered Automation transforms your finance office. It improves accuracy and accelerates decision-making by delivering critical insights in minutes while minimizing errors in executing essential business tasks.
Natural Language ML/AI – Three Key Functional Pillars
Auditoria built a library of intent categories—and sub-intents within these categories—to determine specific actions according to intent-matching outcomes.
1
Email Intent Analysis
Context-aware, intent understanding, and relevance filtering
- Interpret emails written in plain English
- Extract the meaning (“intent”) of the messages
- Extract data points communicated in the messages
- Take actions as configured
2
Email Generation
Fully-automated contextual email replies and dialogue flows based on SmartFlow Skill themes
Streamlined handover to human process as needed and takeover of human-operator-initiated communication when authorized
3
Intelligent Document Processing
Embedded info extraction from email body and attachments through Auditoria’s proprietary “form-extractor” service
Seamlessly document processing in large volumes and large sizes with precision, accuracy, and industry-leading performance
Natural Language Processing (NLP) and Understanding (NLU)
Attachments come through the shared email inboxes as invoices or other forms.
The advanced technology recognizes what is attached, classifies it for relevance, converts it to a data object, and extracts appropriate info.
If a response is needed, the technology responds accordingly.
Auditoria delivers domain-specific meta-language enhancements for scalability that supports execution across SmartFlow Skills and forms.
The advanced NLP /NLU technology provides ML-driven predictions based on stakeholder interaction.
Auditoria’s Natural Language Strategic Benefits for Intelligent Applications
ML/AI Abstraction Layers
Intelligent Semantic Layer
The Auditoria semantic layer handles some of the uncertainty of the latest advanced models, reading the inputs and outputs and aggregating them to take the higher level algorithm and compose it into the specific machine code for the appropriate output.
Auditoria Brings Generative AI to the CFO Office with SmartBots Trained in Finance
The explosive surge of ChatGPT and generative AI across industries shows that automation is being readily accepted at both the home and the office. While many organizations are just now incorporating GPT-3 into platforms and applications, Auditoria’s technology has leveraged large language models for years, ensuring that the models are properly trained in the specifics of corporate finance. With Auditoria, the vision of the fully interactive and automated enterprise is here.
Integrating advanced technology, such as generative AI and natural language technology, is necessary for businesses to stay one step ahead of the competition. Composite ML models that combine the best of breed LLMs and other supervised trained models that are domain or finance-specific are the true answer to enhanced productivity – providing the accuracy and precision that finance teams need.
Adina Simu, Chief Product and
Commercial Officer, Co-founder
Auditoria SmartBots, based on Advanced AI, machine learning, and natural language technology, are the industry’s first built-for-finance SaaS applications that combines intelligent, cognitive automation, and multi-stakeholder collaboration in a single-user experience. SmartBots intelligently automate complex finance workflows while collaborating and engaging with the company’s customers, suppliers, vendors, and internal stakeholders through conversational email.
Interested in Learning More?
We’d be happy to help you understand how Auditoria SmartBots transform your Finance and Accounting functions.
