Real Outcomes. Measurable Impact.

Explore how Palni applies Salesforce, ServiceNow and Databricks to practical enterprise challenges. Each use case explains the challenge, the work carried out and the outcomes reported for that engagement.

Start with a Discovery

Find Use Cases by Service

Choose a service to find examples relevant to your priorities. Cross platform examples show how more than one service contributes to a solution. Every card follows Challenge, What Palni Did, Outcome. Full stories add Industry and Technology.

ServiceNow

25%

Reduction in Hardware Spend

Challenge: Asset tracking relied on spreadsheets, with unclear ownership, over ordering and audit risk.
What Palni did: Implemented lifecycle asset governance with real time tracking, asset normalisation and automated workflows.
Outcome: 25% lower hardware spend, zero audit findings and improved asset visibility.

ServiceNow

30%

Reduction in Software Spend

Challenge: Limited visibility into software use and entitlements led to unused licences, audit exposure and reactive compliance.
What Palni did: Implemented software asset lifecycle management with licence tracking, reconciliation and compliance automation.
Outcome: Reduced shadow IT, audit ready compliance and real time monitoring.

ServiceNow

Faster Ticket Reporting with Voice Intake

Challenge: Users avoided ticketing systems because reporting felt difficult, leading to delays, weak data capture and service desk overload.
What Palni did: Enabled voice driven ticket creation and real time interaction within ServiceNow.
Outcome: Faster issue intake and reduced service desk burden.

Salesforce

Improved Pipeline Visibility and Sales Adoption

Challenge: Sales teams managed deals outside Salesforce because quoting was complex, which reduced pipeline visibility and adoption.
What Palni did: Implemented guided intake with automated opportunity and quote creation connected to email.
Outcome: More consistent deal tracking, better pipeline visibility and less manual data entry.

Salesforce

AI Driven Quoting and Opportunity Creation

Challenge: Requests arrived in Excel and PDF formats, creating delays, inconsistencies and incomplete deal data.
What Palni did: Built an AI supported intake workflow to collect missing information and create opportunities and quotes.
Outcome: Faster quote turnaround, improved data consistency and reduced manual effort.

Salesforce

Unified Customer View Across Systems

Challenge: Customer information was fragmented across multiple systems, limiting visibility and coordination.
What Palni did: Integrated Salesforce with ERP and external systems to create a unified customer data model.
Outcome: A more consistent view of customer information and better cross team coordination.

Databricks

40%

Increase in Near Real Time Data Availability

Challenge: Data was siloed across ERP, CRM and legacy systems, with inconsistent reporting and low trust in metrics.
What Palni did: Migrated more than 10TB of data to Databricks and implemented automated pipelines, governance and centralised reporting.
Outcome: Improved data trust, process efficiency and ELT savings.

Databricks

Planning Cycle Time Reduced from Hours to Seconds

Challenge: Planners relied on fragmented systems and manual reconciliation, slowing decisions and limiting risk visibility.
What Palni did: Built a multi agent AI system on Databricks with natural language queries, real time data access and automated reasoning.
Outcome: Earlier risk detection, and better root cause analysis and decision making.

Databricks

Reduce Data Migration Effort

Challenge: Manual schema mapping, SQL compatibility issues and data validation created long migration cycles and risk.
What Palni did: Deployed a migration accelerator for schema mapping, code conversion and automated validation.
Outcome: Reduced effort, faster cycles and improved validation.

Service to confirm

70%

Faster Reporting

Challenge: Data was fragmented across systems, making reporting slow, inconsistent and manual.
What Palni did: Unified data into a scalable Snowflake environment with automated pipelines and centralised reporting.
Outcome: Faster reporting, a single source of truth and real time analytics.

Service to confirm

99.6%

Auto Reconciliation Rate

Challenge: Manual invoice processing across many formats caused delays, errors and heavy manual work.
What Palni did: Built AI supported invoice reconciliation using OCR and NLP across structured and unstructured formats.
Outcome: A higher auto reconciliation rate, time savings and improved accuracy.

Cross Platform examples will appear here once a published case uses more than one service.

Frequently Asked Questions

Short answers to what people ask us most. Still unsure? Ask us directly

How are Palni use cases presented?

Each use case shows the challenge, what Palni did and the outcome. Full stories add the industry and technology.

Can I filter use cases by service?

Yes. Filter by Salesforce, ServiceNow or Databricks, or view all published examples.

Are the results in the use cases verified?

Every published outcome comes from the engagement's case owner and is reviewed before it is published.

Which use cases involve AI?

Examples include AI driven quoting in Salesforce, voice based ticket intake in ServiceNow, and a multi agent planning system on Databricks.

How can we discuss a similar project?

Start with a discovery. Tell us about the challenge and Palni will suggest a practical starting point.