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Case studies

Impact from enterprise AI and agentic systems

Representative outcomes across industries—structured as executive briefings: situation, approach, implementation levers, and quantified results.

Financial ServicesGlobal Investment Bank

Autonomous Incident Response: Reducing MTTR by 65%

Impact at a glance

65%

Reduction in MTTR

80%

Incidents resolved autonomously

$2.4M

Annual cost savings

24/7

Autonomous operations

Situation

A Fortune 100 investment bank faced escalating operational costs and prolonged downtime due to manual incident response processes. Their infrastructure generated 2,000+ alerts daily, overwhelming the SRE team and leading to alert fatigue.

Our approach

We deployed an agentic incident response system that autonomously detects anomalies, correlates events across distributed systems, diagnoses root causes using RAG-powered knowledge bases, and executes remediation scripts with human-in-the-loop approval for critical changes.

Implementation levers

  1. 1.Integrated with existing monitoring stack (Datadog, PagerDuty, Splunk)
  2. 2.Built custom RAG layer indexing 10 years of incident history and runbooks
  3. 3.Deployed multi-agent system with specialized agents for detection, diagnosis, and remediation
  4. 4.Implemented approval workflows for high-risk automated actions

Technology stack Python · LangChain · Vector DB · Kubernetes · Terraform

TechnologySaaS Platform Provider

Agentic Testing: Accelerating Software Delivery by 3x

Impact at a glance

3x

Faster deployment cycle

95%

Test coverage

0

Production incidents

40%

Engineering time saved

Situation

A fast-growing SaaS company struggled with manual code reviews, inconsistent testing, and deployment bottlenecks. Their engineering team spent 40% of their time on operational tasks rather than feature development.

Our approach

We implemented an end-to-end agentic CI/CD pipeline that autonomously reviews code for style and security, generates and executes test cases, manages infrastructure provisioning, and orchestrates zero-downtime deployments across multi-cloud environments.

Implementation levers

  1. 1.Integrated with GitHub, GitLab, and Bitbucket repositories
  2. 2.Built AI agents for automated code review, security scanning (SAST/DAST), and test generation
  3. 3.Deployed infrastructure-as-code agents for AWS, GCP, and Azure
  4. 4.Implemented progressive rollout strategies with automated rollback

Technology stack TypeScript · GitHub Actions · Docker · ArgoCD · OpenAI GPT-4

HealthcareMulti-Hospital Health System

Intelligent Support Automation: 85% First-Contact Resolution

Impact at a glance

85%

First-contact resolution

2hrs

Average resolution time

$1.8M

Annual labor savings

95%

User satisfaction score

Situation

A healthcare system with 15,000+ employees faced overwhelming IT support requests (500+ tickets/day). Average resolution time was 48 hours, impacting clinical operations and staff productivity.

Our approach

We deployed an autonomous support system powered by RAG that handles Level 1 and Level 2 support tickets. The system accesses internal documentation, executes diagnostic scripts, and resolves issues autonomously while escalating complex cases to human specialists.

Implementation levers

  1. 1.Indexed 50,000+ pages of internal IT documentation and SOPs
  2. 2.Built RAG system with semantic search across tickets, wikis, and runbooks
  3. 3.Deployed agents with tool-calling capabilities for password resets, access provisioning, and system diagnostics
  4. 4.Integrated with ServiceNow and Active Directory

Technology stack Python · LangChain · Pinecone · ServiceNow API · Azure AD

RetailE-Commerce Platform

Agentic Social Media: 10x Engagement Growth

Impact at a glance

10x

Engagement growth

24/7

Community coverage

90%

Responses under 5 min

200hrs

Monthly time saved

Situation

A rapidly scaling e-commerce brand struggled to maintain consistent social media presence across 8 platforms and 12 regional markets. Manual content creation and community management couldn't keep pace with growth.

Our approach

We deployed an autonomous social media engine that generates brand-compliant content, manages community interactions, monitors sentiment in real-time, and optimizes posting strategies based on engagement data—all while maintaining human oversight for brand-critical decisions.

Implementation levers

  1. 1.Built multi-platform content generation agents trained on brand guidelines
  2. 2.Deployed 24/7 community management agents with sentiment analysis
  3. 3.Implemented real-time social listening across 15 languages
  4. 4.Created automated crisis detection and escalation workflows

Technology stack GPT-4 · Claude · Twitter API · Meta API · Sentiment Analysis

ManufacturingGlobal Manufacturing Conglomerate

Cloud Migration: Zero-Downtime Multi-Cloud Transformation

Impact at a glance

100%

Zero-downtime migration

18 mo

Program duration

40%

Infrastructure cost reduction

99.99%

Post-migration uptime

Situation

A manufacturing giant needed to migrate 200+ legacy applications from on-premises data centers to multi-cloud infrastructure (AWS, Azure) without disrupting 24/7 global operations.

Our approach

We deployed autonomous migration agents that assessed application dependencies, refactored code for cloud-native patterns, orchestrated phased migrations with automated testing, and optimized cloud costs in real-time.

Implementation levers

  1. 1.Built dependency mapping agents that analyzed 15 years of legacy code
  2. 2.Deployed refactoring agents for containerization and microservices conversion
  3. 3.Implemented automated testing and validation for each migration phase
  4. 4.Created FinOps agents for continuous cost optimization

Technology stack Python · Terraform · Kubernetes · AWS · Azure

InsuranceNational Insurance Provider

Data Intelligence: Text-to-SQL for Business Users

Impact at a glance

90%

Query success rate

10 min

Average query time

500+

Daily queries

$800K

Annual productivity gains

Situation

Business analysts spent 60% of their time waiting for data engineering teams to write SQL queries, creating a bottleneck in decision-making and reporting.

Our approach

We built an agentic text-to-SQL system that allows non-technical users to query complex databases in natural language. The system generates optimized SQL, validates queries for security, and produces narrative-driven reports with visualizations.

Implementation levers

  1. 1.Indexed database schemas and business logic across 50+ data sources
  2. 2.Built RAG system with semantic understanding of business terminology
  3. 3.Deployed query optimization agents that rewrite SQL for performance
  4. 4.Implemented row-level security and PII detection

Technology stack GPT-4 · DuckDB · PostgreSQL · Snowflake · Tableau

RetailOmni-Channel Retail Group

How We Customized Llama for Customer Complaints

Impact at a glance

42%

Faster mean resolution time

88%

Policy-safe draft acceptance

35%

Reduction in escalations

4.2/5

Customer satisfaction (CSAT)

Situation

A large retailer received tens of thousands of written complaints per month across email, chat, and web forms. Off-the-shelf LLMs misclassified intent, suggested responses that conflicted with refund and legal policy, and could not reliably mirror the brand’s tone—forcing agents to rewrite most AI drafts and slowing resolution.

Our approach

We fine-tuned a Llama family model on curated, policy-aligned complaint–resolution pairs, with human-reviewed labels for severity, intent, and compliant actions. The customized model drafts first-pass classifications, suggested replies within guardrails, and routes edge cases to specialists—keeping sensitive data out of third-party APIs where required.

Implementation levers

  1. 1.Prepared a de-identified training corpus from historical tickets with PII redaction and consent-aligned retention
  2. 2.Applied parameter-efficient fine-tuning (LoRA) on Llama with domain-specific system prompts and refusal rules
  3. 3.Aligned outputs to refund, shipping, and legal playbooks via constrained decoding and retrieval over policy snippets
  4. 4.Deployed an evaluation harness measuring policy adherence, tone, and escalation accuracy before production rollout

Technology stack Llama 3 · PyTorch · LoRA · vLLM · PostgreSQL · Redis

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