Discover how SearchUnify’s GenAI-powered support agent desktop and SCORE framework transform customer support. This comprehensive guide explores overcoming LLM challenges, enhancing agent productivity, and boosting customer satisfaction with AI-driven solutions.
The LLM Deluge and the Quest
for the Ideal GenAI-Powered
Support Agent Desktop
The promise of Large Language Models (LLMs) transforming customer support is no longer a
futuristic vision – it’s the new frontier businesses are rushing to conquer. The explosion of GenAI
startups, however, has created a double-edged sword. While choice is abundant, navigating the
LLM landscape to pinpoint the right solution for your support organization is increasingly
challenging.
Many ready-made LLM applications, despite their initial allure, fall short of delivering on the full
potential of GenAI for support. Let’s delve into the key considerations for SaaS leaders evaluating
LLM vendors and why generic solutions may not be the answer.
1. The Customization Conundrum: One Size Doesn’t Fit All
CHALLENGE
Many LLM vendors promote a “plug-and-play” approach, offering pre-trained models built on vast,
general-purpose datasets. However, the reality is far more nuanced. Generic models lack the
domain-specific knowledge and linguistic fluency needed to handle the complexities of customer
support interactions.
Generic responses lead to frustrated customers- Imagine a customer seeking help with a
highly technical software bug receiving a generic answer about resetting their password. This
disconnect arises from models trained on data that lacks context about your specific
products, industry terminology, and customer base.
Limited customization options create roadblocks- Tailoring pre-trained LLMs to your unique
workflows and knowledge base should be seamless, not a herculean effort. Many vendors fall
short in providing intuitive tools and frameworks for fine-tuning models, integrating
proprietary knowledge, or even adapting the conversational style to match your brand voice.
2. The Integration Imperative: Breaking Down Data Silos
CHALLENGE
A GenAI-powered support solution should seamlessly integrate into your existing ecosystem, not
exist in a vacuum. This includes your CRM, ticketing system, knowledge base, and communication
channels.
Siloed solutions create operational nightmares. Standalone LLM applications lead to
fragmented data, broken workflows, and an incomplete view of the customer journey.
Imagine the inefficiencies of an LLM that can’t access a customer’s previous support tickets or
pull relevant information from your knowledge base in real time.
Lack of open APIs and integration support increases complexity. Choosing a vendor that
prioritizes open APIs and comprehensive documentation is crucial. This enables seamless
integration with your existing tech stack, eliminating the need for costly and time-consuming
custom development or reliance on rigid vendor-specific solutions.
3. Hype vs. Reality: Separating Promise from Overpromise
CHALLENGE
The excitement surrounding LLMs can lead to inflated expectations. Vendors often overpromise
capabilities, leaving businesses disillusioned when models fail to live up to the hype.
Setting realistic goals is paramount. While LLMs can automate tasks, they are not a magic
bullet. Clearly understand the limitations of current technology and focus on achievable use
cases, such as automating responses to frequently asked questions or providing agents with
real-time knowledge assistance.
Transparency and explainability build trust. “Black box” LLMs that provide little insight into
their decision-making process erode trust and make it challenging to identify and address
issues like bias or factual inaccuracies. Look for vendors who prioritize transparency and offer
tools for understanding and explaining LLM outputs.
4. Responsible AI: Navigating Ethical Considerations
CHALLENGE
The ethical implications of LLMs cannot be overstated. From bias amplification to data privacy
concerns, responsible AI development and deployment are non-negotiable.
Bias in training data leads to biased outputs. LLMs trained on biased data will perpetuate
and even amplify those biases. Ensure your chosen vendor has robust processes for mitigating
bias in their training data and monitoring LLM outputs for fairness and accuracy.
Lack of governance and oversight raises red flags. Look for vendors who prioritize
responsible AI principles, providing clear guidelines and frameworks for data governance,
model development, and ongoing monitoring to mitigate unintended consequences and
ensure ethical AI usage.
5. The Human Element: Empowering, Not Replacing
CHALLENGE
The goal of GenAI in support should be to augment human capabilities, not to replace human
agents altogether.
Addressing fears of job displacement is critical. Successfully integrating LLMs into the
support workflow requires addressing concerns about job security head-on. Emphasize how
these tools will empower agents to handle more complex tasks, improve their efficiency, and
ultimately enhance the customer experience.
Inadequate agent training creates friction. Transitioning to an LLM-powered support model
requires robust training programs. Don’t underestimate the importance of investing in your
agents’ ability to effectively utilize these tools, interpret LLM outputs, and seamlessly handle
exceptions or escalations.
Human oversight remains essential. While LLMs excel at automating routine tasks, complex
or emotionally charged situations require human empathy and intuition. A successful support
strategy strikes a balance between automation and human intervention, leveraging the
strengths of both.
SearchUnify: Purpose-Built for Support, Powered by GenAI
In a sea of generic LLM applications, SearchUnify stands out as a solution meticulously designed
to address the specific needs of modern support organizations.
Cognitive Search
Agent Helper
Knowbler
SearchUnify
Virtual Assistant
Community
Helper
Insights Engine
Cognitive Tech
In-house Generative AI
Product Manuals
Generation
A
Augmented
R
Retrieval
F
Federated
Documentation
Knowledge
Base
Public LLMs
G
Blogs
SearchUnify Partner Provisioned LLMs
Bring Your Own LLM
Here’s how SearchUnify empowers you to build a truly effective
GenAI-powered support agent desktop
Deep domain expertise: Our LLMs are trained on a massive dataset of support interactions,
giving them a nuanced understanding of industry-specific terminology, common customer
issues, and effective resolution strategies.
Unparalleled customization: Our intuitive platform makes it easy to fine-tune models with
your proprietary knowledge base, tailor conversational styles to your brand, and seamlessly
integrate with your existing support ecosystem.
Human-in-the-loop focus: We believe in empowering agents, not replacing them. Our
solutions are designed to augment human capabilities, providing real-time assistance,
automating mundane tasks, and enabling agents to focus on delivering exceptional customer
experiences.
Robust security and ethical AI: We prioritize data security and responsible AI practices,
offering robust governance frameworks, transparency tools, and a commitment to mitigating
bias and ensuring ethical use of our technology.
Our USPs with Respect to GenAI-fueled Support Applications
a) Business Differentiators Over Maintaining an In-house RAG Ecosystem
Reduced Complexity: SearchUnify handles the complexities of managing LLMs,
allowing businesses to focus on core competencies.
Faster Time-to-Value: SearchUnify’s pre-built integrations and optimized platform
enable rapid deployment of GenAI capabilities.
Lower Total Cost of Ownership: SearchUnify eliminates the need for costly
investments in infrastructure, talent, and ongoing maintenance.
b) Pricing Differentiators
Standardized Tokenization: SearchUnify optimizes token usage for both input and
output, maximizing value from LLM interactions.
Caching and Trigger Mechanisms: Caching frequently used responses and
implementing intelligent trigger mechanisms for LLM usage reduce costs and
improve efficiency.
c) Close Collaboration with Respect to Customizations Offered to Clients
Prompt Engineering: SearchUnify experts collaborate with clients to design and
fine-tune prompts for specific use cases, optimizing accuracy and relevance.
LLM Bias Monitoring: SearchUnify implements mechanisms to detect and mitigate
potential biases in LLM responses, ensuring fairness and trust.
LLM Response Time Monitoring: SearchUnify continuously tracks and optimizes LLM
response times for seamless user experiences.
Persona Creation: SearchUnify helps create custom personas that align with specific
user groups, tailoring responses and interactions.
d) Technology Framework for LLM Optimization: SearchUnifyFRAG™
Federation: Provides a 360-degree view of customer data, enriching LLM input with
essential context.
Retrieval: Employs advanced algorithms and semantic understanding to retrieve
highly relevant information from knowledge sources.
Augmented Generation: Generates natural, coherent, and personalized responses by
leveraging the power of LLMs.
e) Security and Compliance
ISO 27701:2019 Compliant: Adheres to the highest standards of data privacy and
security management.
SSAE 18 SOC 1, SOC 2 Type II, SOC 3 Certified: Demonstrates adherence to rigorous
security controls and auditing standards.
HIPAA Compliant: Meets the requirements for handling sensitive healthcare information.
GDPR Compliant: Ensures compliance with the General Data Protection Regulation for
data privacy.
CCPA Compliant: Compliant with the California Consumer Privacy Act.
PIMS (BS 10012) Compliant: Adheres to the British Standard for personal information
management.
f) BYOL Approach: Flexibility to Leverage the Right Large Language
Model for Your Use Case
SearchUnify’s Bring Your Own License (BYOL) approach allows businesses to seamlessly
integrate their preferred LLMs, providing flexibility and control.
g) Flexibility with Respect to Embedding Techniques Offered
SearchUnify supports a range of advanced embedding techniques, including:
Traditional Word Embeddings (e.g., Word2Vec, GloVe): Capture semantic
relationships between words.
Deep Learning-Based Embeddings (e.g., BERT, SBERT): Capture deeper
contextualized representations of words and sentences.
Transfer Learning and Pre-trained Embeddings: Leverages pre-trained language
models for enhanced performance and faster development.
h) SCORE Framework for Hybrid Search Optimization
Semantic Understanding: Incorporates semantic analysis to understand the
meaning and intent behind queries.
Contextual Relevance: Factors in user context, such as location, history, and
preferences, to deliver personalized results.
Optimized Ranking: Employs advanced ranking algorithms to surface the most
relevant results from various data sources.
Relevance Feedback: Continuously learns from user interactions (clicks, dwell time)
to refine search results over time.
Enriched Results: Enhances search results with summaries, key phrases, and related
content for a richer user experience.
Search
Query
Relevant
chunks
Vector
Search
Keyword
Search
Database
Relevant
chunks
Chunks
Semantic
Tuning Model
#1
Keywords
#2
Content Sources
Chunks Relevance
Score
#3
0.9
#1
0.8
User Behaviour
#5
0.5
#4
Synonyms
#4
0.5
#5
Historical
click-throughs
#2
0.3
#3
+
Rerank
Generative AI Applications Offered by SearchUnify
SearchUnify leverages its FRAG™ approach and LLM integrations to power a suite of GenAI
applications that transform customer support and self-service:
A. For Self-Service Success
Direct Answers on Community/Help Portals (SearchUnifyGPT™): Provides users with
direct, concise answers to their queries directly within community forums and help
portals, eliminating the need to sift through lengthy documents.
Intent Recognition: Accurately understands user intent, even from complex or
conversational queries, enabling more relevant search results and self-service experiences.
Named Entity Recognition: Identifies and extracts key entities from user queries
(e.g., product names, error codes), allowing for more targeted information retrieval
and personalized support.
Conversational AI (SUVA): Offers natural, human-like conversational experiences
through chatbots, resolving queries, guiding users, and providing 24/7 support.
Synthetic Utterance Generation: Expands chatbot capabilities by automatically generating
variations of user utterances, improving intent recognition and conversational flow.
Welcome to Community
User
Facing trouble parsing texts in data columns
All Content
Community
Case
Developer
Product
Help
Blogs
Articles
Content
KB
SearchUnifyGPTTM
To help solve problems with parsing and indexing
documents, follow these steps:
Rename your .XLSZ fle to .XLS and try again…
Change the Excel type to Excel1 Legacy.
Create a named range with Excel2 and connect to it.
Category
Just fill the number of records1 you shoud skip in
the N option.
Citations
[1]
[2]
Powered by
B. Knowledge Creation and Optimization
Article Title Generation: Auto-generates clear, concise, and SEO-friendly titles for
knowledge articles, streamlining content creation.
Article Summary Generation: Automatically creates concise summaries of lengthy
knowledge articles, improving content discoverability and user comprehension.
Knowledge Graphs: Automatically builds and maintains knowledge graphs from
enterprise data, uncovering relationships between concepts and enhancing
knowledge discovery.
Content Standard Checklist (LLM Accountability): Provides automated quality checks
for knowledge articles, ensuring content adheres to style guides, accuracy standards,
and best practices.
Knowledge Gap Visualization: Identifies and visualizes potential gaps in the knowledge
base, enabling content teams to create missing articles and improve self-service.
C. Agent Productivity and Support Success
First Response Generation: Generates automated first responses to customer inquiries,
saving agents time and ensuring prompt acknowledgements.
Direct Answers (Case Resolving Suggestions via SearchUnifyGPT™): Provides agents
with direct answers and potential solutions to customer issues directly within their
workflow, accelerating case resolution.
Case Summarization: Automatically summarizes lengthy email threads, chat
transcripts, and case histories, providing agents with concise overviews and saving
them valuable time.
CRM
Search Here
Agent Console
Cases
00001026
Agent Helper
Details
Feed
Case Owner
Case Number
#installation
#laptop
Case Details
Overview
Case Timeline
Actions
New!
#CASE CREATED
Contact Name
29.04.24
#USER CHECKED CONTENT
29.04.24
30.04.24
#USER CHECKED CONTENT
30.04.24
30.04.24
Account Name
#SEARCH HISTORY
Type
Case Reason
Community ID
Community URL
Category
User
Agent
# JOHN REPLIED
Positive
Neutral
# JOHN REPLIED
Opened Since 4 Days
Negative
Case Summary
Brief Detailed
Last Updated:04 Mar 2024
Problem description:
– Brief overview of the issue or problem reported by the customer.
Request:
Topics
– Specific action or resolution requested by the customer.
Customer details:
– Basic information about the customer (name, contact details, account number, etc.).
Ticket ID:
– Unique identifier for tracking and reference purposes.
Ready to unlock the true potential of GenAI
for your support organization
