Making SaaS Intelligence Conversational at Zluri
How Darwinbox Achieved 85% Faster Onboarding with Beacon.li
Zluri is a SaaS management platform designed to simplify how organizations gain visibility and control over their software ecosystem. Through its partnership with Beacon and the adoption of Beacon’s AI orchestration layer, Zluri now delivers a conversational way for teams to discover and act on SaaS data without disrupting existing workflows.
Beacon’s orchestration fit well into our existing data and UI, which helped our customers interact with the product, giving them a more augmented experience where most common questions can now be answered through self-serve discovery.
Beacon’s orchestration fit well into our existing data and UI, which helped our customers interact with the product, giving them a more augmented experience where most common questions can now be answered through self-serve discovery.
Beacon’s orchestration fit well into our existing data and UI, which helped our customers interact with the product, giving them a more augmented experience where most common questions can now be answered through self-serve discovery.
Chaithanya Yambari
Chaithanya Yambari
Chaithanya Yambari
Co-founder at Zluri
Co-founder at Zluri
Co-founder at Zluri
Most queries resolved in < 1 minute
Most queries resolved in < 1 minute
Most queries resolved in < 1 minute
Single-step access reduced reliance on documentation and support
Single-step access reduced reliance on documentation and support
Single-step access reduced reliance on documentation and support
The Challenge of Finding Intelligence in Complex SaaS Environment
The Challenge of Finding Intelligence in Complex SaaS Environment
The Challenge of Finding Intelligence in Complex SaaS Environment
The more successful Zluri's customers became, the more data they had to manage. The next frontier became clear: helping teams unlock that intelligence faster, without the clicks and filters.
1. Data Spanning Multiple Domains
Critical information was distributed across applications, licenses, users, contracts, departments, and integrations. Each domain had its own dedicated views, meaning users needed familiarity with the platform structure to know where specific information resided.
2. Time-Intensive Navigation for Simple Questions
Answering common operational questions, such as checking license usage, identifying an owner, or reviewing subscription counts. Typically required navigating 3–5 different pages and applying filters across views to piece together the full answer.
3. Limited Analytical Derivation
While filtering was available, comparisons and summaries often took 5–10 minutes per query, especially for questions like identifying licenses with the highest subscriptions or aggregating usage patterns across entities.
4. Support Dependency for Contextual Queries
Users frequently relied on documentation or support for data validation and platform understanding, increasing response times and support load.
The more successful Zluri's customers became, the more data they had to manage. The next frontier became clear: helping teams unlock that intelligence faster, without the clicks and filters.
1. Data Spanning Multiple Domains
Critical information was distributed across applications, licenses, users, contracts, departments, and integrations. Each domain had its own dedicated views, meaning users needed familiarity with the platform structure to know where specific information resided.
2. Time-Intensive Navigation for Simple Questions
Answering common operational questions, such as checking license usage, identifying an owner, or reviewing subscription counts. Typically required navigating 3–5 different pages and applying filters across views to piece together the full answer.
3. Limited Analytical Derivation
While filtering was available, comparisons and summaries often took 5–10 minutes per query, especially for questions like identifying licenses with the highest subscriptions or aggregating usage patterns across entities.
4. Support Dependency for Contextual Queries
Users frequently relied on documentation or support for data validation and platform understanding, increasing response times and support load.
The more successful Zluri's customers became, the more data they had to manage. The next frontier became clear: helping teams unlock that intelligence faster, without the clicks and filters.
1. Data Spanning Multiple Domains
Critical information was distributed across applications, licenses, users, contracts, departments, and integrations. Each domain had its own dedicated views, meaning users needed familiarity with the platform structure to know where specific information resided.
2. Time-Intensive Navigation for Simple Questions
Answering common operational questions, such as checking license usage, identifying an owner, or reviewing subscription counts. Typically required navigating 3–5 different pages and applying filters across views to piece together the full answer.
3. Limited Analytical Derivation
While filtering was available, comparisons and summaries often took 5–10 minutes per query, especially for questions like identifying licenses with the highest subscriptions or aggregating usage patterns across entities.
4. Support Dependency for Contextual Queries
Users frequently relied on documentation or support for data validation and platform understanding, increasing response times and support load.
How Beacon Turned Data Visibility Into Data Dialogue
How Beacon Turned Data Visibility Into Data Dialogue
How Beacon Turned Data Visibility Into Data Dialogue
Beacon embedded an AI-orchestrated intelligence layer directly into Zluri’s existing UI, bringing search, insights, and contextual assistance together without changing user workflows.
Beacon embedded an AI-orchestrated intelligence layer directly into Zluri’s existing UI, bringing search, insights, and contextual assistance together without changing user workflows.
Beacon embedded an AI-orchestrated intelligence layer directly into Zluri’s existing UI, bringing search, insights, and contextual assistance together without changing user workflows.
1. Natural-Language Search Across the Platform
Users can search across 16+ core Zluri entities, including applications, licenses, users, subscriptions, and contracts, using simple keywords or natural language without navigating multiple pages.
2. Embedded AI Copilot in the Search Experience
An AI Copilot was built directly into the search bar, enabling users to ask questions, receive answers in context, and continue conversations where they already work.
3. Context-Aware, Conversational Interactions
The Copilot retains conversational context, making follow-up questions easy and reducing the effort needed to explore related information.
4. Multi-Agent AI Orchestration
A multi-agent orchestration framework routes each query to the most relevant specialized AI agent, improving response accuracy while keeping AI usage efficient and scalable.
5. Grounded Responses with Source-Level Validation
Every AI-generated answer includes direct source references with one-click navigation to filtered Zluri pages, allowing users to quickly validate and trust the results.
1. Natural-Language Search Across the Platform
Users can search across 16+ core Zluri entities, including applications, licenses, users, subscriptions, and contracts, using simple keywords or natural language without navigating multiple pages.
2. Embedded AI Copilot in the Search Experience
An AI Copilot was built directly into the search bar, enabling users to ask questions, receive answers in context, and continue conversations where they already work.
3. Context-Aware, Conversational Interactions
The Copilot retains conversational context, making follow-up questions easy and reducing the effort needed to explore related information.
4. Multi-Agent AI Orchestration
A multi-agent orchestration framework routes each query to the most relevant specialized AI agent, improving response accuracy while keeping AI usage efficient and scalable.
5. Grounded Responses with Source-Level Validation
Every AI-generated answer includes direct source references with one-click navigation to filtered Zluri pages, allowing users to quickly validate and trust the results.
1. Natural-Language Search Across the Platform
Users can search across 16+ core Zluri entities, including applications, licenses, users, subscriptions, and contracts, using simple keywords or natural language without navigating multiple pages.
2. Embedded AI Copilot in the Search Experience
An AI Copilot was built directly into the search bar, enabling users to ask questions, receive answers in context, and continue conversations where they already work.
3. Context-Aware, Conversational Interactions
The Copilot retains conversational context, making follow-up questions easy and reducing the effort needed to explore related information.
4. Multi-Agent AI Orchestration
A multi-agent orchestration framework routes each query to the most relevant specialized AI agent, improving response accuracy while keeping AI usage efficient and scalable.
5. Grounded Responses with Source-Level Validation
Every AI-generated answer includes direct source references with one-click navigation to filtered Zluri pages, allowing users to quickly validate and trust the results.
6. On-Demand Analytics Without Manual Filterings
Beacon enabled the AI layer to perform lightweight analytics and aggregations on top of Zluri data. It allows users to count, compare, and analyze SaaS data without manual filtering or cross-page analysis.
6. On-Demand Analytics Without Manual Filterings
Beacon enabled the AI layer to perform lightweight analytics and aggregations on top of Zluri data. It allows users to count, compare, and analyze SaaS data without manual filtering or cross-page analysis.
6. On-Demand Analytics Without Manual Filterings
Beacon enabled the AI layer to perform lightweight analytics and aggregations on top of Zluri data. It allows users to count, compare, and analyze SaaS data without manual filtering or cross-page analysis.
Real Impact on Daily Operations
Real Impact on Daily Operations
Real Impact on Daily Operations
While the engagement focused on capability enablement rather than hard ROI tracking, Zluri teams observed clear operational gains.
While the engagement focused on capability enablement rather than hard ROI tracking, Zluri teams observed clear operational gains.
While the engagement focused on capability enablement rather than hard ROI tracking, Zluri teams observed clear operational gains.
Metric
Metric
Before Beacon
Before Beacon
With Beacon.li
With Beacon.li
Data Discovery Time
Navigation Effort
Navigation Effort
Insight Generation
Support Deflection
Support Deflection
10–15+ minutes per query
10–15+ minutes per query
4–6 screens per query
4–6 screens per query
15–20+ minutes for comparisons or summaries
Limited self-serve resolution for contextual queries
Under 1 minute
Under 1 minute
Single-step, conversational access
Single-step, conversational access
Near real-time, conversational
Near real-time, conversational
High self-serve resolution through AI Copilot
Metric
Data Discovery Time
Navigation Effort
Insight Generation
Support Deflection
Before Beacon
10–15+ minutes per query
4–6 screens per query
15–20+ minutes for comparisons or summaries
Limited self-serve resolution for contextual queries
With Beacon.li
Under 1 minute
Single-step, conversational access
Near real-time, conversational
High self-serve resolution through AI Copilot
Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration
Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration
Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration
