Case Studies

Real examples of how we've helped organizations implement AI solutions, build RAG systems, optimize prompts, and enable teams with cutting-edge AI capabilities. Each case study represents a unique challenge, a tailored solution, and measurable results.

RAG SystemsSaaS / TechnologyProject-Based Consulting

Enterprise RAG System for Internal Knowledge Base

SaaS Platform Company • 8 weeks

Challenge

A fast-growing SaaS company with 200+ employees needed to make their extensive internal documentation, API references, and knowledge base searchable and accessible through an AI interface. Their support and engineering teams were spending hours searching through documentation, leading to slower ticket resolution and decreased productivity. Existing search solutions were keyword-based and often returned irrelevant results.

Solution

We designed and implemented a comprehensive RAG system using a vector database (Pinecone), optimized document chunking strategies with semantic chunking, and built a custom retrieval pipeline with reranking. The system integrated with their existing documentation infrastructure and Slack for easy access. We implemented evaluation frameworks to continuously measure and improve retrieval accuracy.

Technologies

•RAG
•Vector Databases (Pinecone)
•OpenAI Embeddings
•LLM Integration
•Document Processing
•Slack Integration

Results

✓80% reduction in time to find relevant information (from 15 minutes average to 3 minutes)
✓95% accuracy rate for technical documentation queries
✓Support ticket resolution time decreased by 40%
✓Enabled self-service for 60% of common engineering questions
✓Reduced dependency on subject matter experts for routine documentation questions
✓ROI: Estimated $150K+ annual savings in engineering and support time

Local AIHealthcare / TechnologyProject-Based Consulting + Training

Local AI Deployment for Privacy-Sensitive Healthcare Data

Healthcare Technology Firm • 12 weeks

Challenge

A healthcare technology company needed to implement AI-powered features for patient data analysis and clinical decision support, but had strict privacy and data residency requirements preventing cloud-based solutions. HIPAA compliance required all data to remain on-premises, and existing cloud API costs would be prohibitive at their scale (estimated $50K+ monthly).

Solution

We deployed a self-hosted LLM infrastructure using vLLM for optimized inference, implemented quantization (GPTQ) to reduce memory requirements by 50%, and created a hybrid architecture that balanced performance with compliance. We integrated the solution with their existing EMR system and built privacy-preserving pipelines with audit logging. The system was deployed in their existing on-premises Kubernetes cluster.

Technologies

•Local AI
•vLLM
•Self-Hosting
•Quantization (GPTQ)
•Kubernetes
•Privacy Compliance

Results

✓Fully compliant with HIPAA and data residency requirements
✓60% cost reduction compared to cloud API usage (from $50K/month estimated to $20K/month infrastructure)
✓Zero data leaves the organization's infrastructure
✓Achieved sub-200ms response times for most queries (comparable to cloud APIs)
✓Reduced dependency on external APIs and eliminated vendor lock-in
✓Built internal capabilities: team trained to maintain and extend the system

Sales Engineering EnablementEnterprise Software / SalesEmbedded Engineering + Training

AI-Powered Sales Enablement Platform

Enterprise Software Company • 10 weeks

Challenge

A mid-size enterprise software company's sales engineering team was spending significant time creating custom demos, technical proposals, and competitive battle cards for each customer engagement. Response times to RFPs were slow (often 2-3 weeks), and content quality varied across team members. The SE team needed tools to work more efficiently while maintaining high-quality, personalized content.

Solution

We built an AI-powered sales enablement platform specifically designed for Sales Engineers. The system includes demo automation tools, technical content generation (architecture diagrams, solution briefs, proposals), competitive positioning analysis, and customer research automation. We integrated with their CRM (Salesforce) and documentation systems, and created SE-specific prompt libraries for common tasks.

Technologies

•AI Content Generation
•CRM Integration (Salesforce)
•Prompt Engineering
•LLM APIs
•Documentation Systems

Results

✓70% reduction in time to create technical proposals (from 8 hours to 2.5 hours average)
✓RFP response time decreased from 2-3 weeks to 5-7 days
✓Consistent, high-quality content across all SE team members
✓SE team can handle 40% more customer engagements
✓Improved win rate: 15% increase in closed deals (attributed to better-prepared proposals)
✓Knowledge base created: All generated content stored and reusable for future engagements

Advanced RAG OptimizationLegal ServicesProject-Based Consulting

Advanced RAG System with Reranking for Legal Research

Law Firm • 14 weeks

Challenge

A large law firm needed to search through decades of case law, legal precedents, and internal memos quickly and accurately. Traditional keyword search often missed relevant cases or returned too many irrelevant results. Lawyers were spending 20-30% of their time on research that could be automated or accelerated with AI.

Solution

We implemented an advanced RAG system with multi-stage retrieval: initial vector search, followed by reranking using cross-encoder models (BGE-reranker). We used hybrid search combining semantic and keyword matching for better coverage. The system was trained on their specific legal domain with custom embeddings, and we built citation tracking to ensure all generated answers could be verified.

Technologies

•Advanced RAG
•Reranking (BGE)
•Hybrid Search
•Legal Domain Models
•Citation Tracking

Results

✓40% improvement in retrieval accuracy compared to basic RAG (measured by lawyer review)
✓90% reduction in irrelevant search results
✓Research time decreased by 50% (from 8 hours average to 4 hours per case)
✓Better handling of complex, multi-part legal questions
✓Citation accuracy: 98% of generated answers properly cite source documents
✓Lawyers can focus on analysis and strategy rather than information gathering

LLM Evaluation & QAE-commerce / Customer ServiceProject-Based Consulting

LLM Evaluation Framework for Customer Support Chatbot

E-commerce Platform • 6 weeks

Challenge

An e-commerce company deployed a customer support chatbot but had no systematic way to measure its performance, quality, or accuracy. Customer satisfaction was declining, and they couldn't identify specific failure modes or improvement opportunities. They needed a comprehensive evaluation system to ensure the chatbot was actually helping customers.

Solution

We built a comprehensive LLM evaluation framework including automated testing, quality metrics (accuracy, relevance, tone), A/B testing infrastructure, and performance monitoring. We created evaluation datasets based on real customer interactions, implemented human-in-the-loop feedback collection, and built dashboards for tracking metrics over time. The system automatically flags regressions and suggests improvements.

Technologies

•LLM Evaluation
•A/B Testing
•Quality Metrics
•Performance Monitoring
•Feedback Systems

Results

✓Clear visibility into chatbot performance with measurable metrics
✓Identified and fixed 15+ failure modes that were impacting customer satisfaction
✓Customer satisfaction (CSAT) improved from 3.2/5 to 4.1/5
✓30% reduction in escalations to human agents
✓Cost savings: Reduced customer support costs by $80K annually
✓Continuous improvement: System now automatically identifies quality issues
✓Data-driven decisions: Can now confidently deploy prompt and model changes

AI Coaching ProgramsFinancial Services / TechnologyTraining & Enablement

AI Coaching Program for Engineering Team

Financial Services Company • 8 weeks

Challenge

A financial services company wanted to adopt AI tools and practices across their engineering organization, but the team lacked AI expertise. They needed a structured program to enable engineers with AI skills, best practices, and hands-on experience. Leadership wanted to build internal capabilities rather than depend entirely on external consultants.

Solution

We designed and delivered a comprehensive 8-week AI coaching program including workshops, hands-on labs, prompt libraries, and ongoing support. The program covered LLM fundamentals, prompt engineering, RAG basics, evaluation techniques, and best practices. We created customized content based on their tech stack and use cases, and established an internal AI community for knowledge sharing.

Technologies

•AI Training
•Prompt Engineering
•Hands-on Workshops
•Knowledge Transfer

Results

✓50 engineers trained with hands-on AI experience
✓Internal AI capabilities established: Team can now build basic AI features independently
✓3 pilot projects successfully completed by trained engineers
✓Prompt library created: 100+ reusable prompts for common tasks
✓Internal knowledge sharing: Engineers helping each other with AI questions
✓Reduced dependency on external consultants for routine AI tasks
✓Foundation for future AI initiatives: Team equipped to evaluate and implement AI solutions

AWS InfrastructureEnterprise / TechnologyProject-Based Consulting

AWS-Based RAG System for Enterprise Knowledge Management

Enterprise Technology Company • 10 weeks

Challenge

A large enterprise technology company needed to deploy a RAG system to make their extensive technical documentation, architecture diagrams, and internal knowledge base searchable across their global engineering team. They required a scalable, highly available solution on AWS that could handle millions of documents and thousands of concurrent users. The system needed to integrate with existing AWS infrastructure, comply with security policies, and optimize costs while maintaining sub-second query response times.

Solution

We architected and deployed a production-ready RAG system on AWS using a multi-service approach. The solution leveraged AWS Bedrock for LLM APIs, ECS for containerized embedding and retrieval services, Amazon OpenSearch for vector storage and search, S3 for document storage, API Gateway for external access, and ElastiCache for response caching. We implemented intelligent caching strategies, auto-scaling policies, and cost optimization techniques including Spot instances for non-critical workloads. The architecture included multi-AZ deployment for high availability, comprehensive monitoring with CloudWatch and X-Ray, and security hardening with VPC isolation and IAM policies.

Technologies

•AWS ECS
•AWS Bedrock
•Amazon OpenSearch
•S3
•Lambda
•API Gateway
•ElastiCache
•CloudWatch
•X-Ray
•VPC
•RAG
•Vector Search

Results

✓Scalable to millions of documents and 10,000+ concurrent users
✓Sub-second query response times (avg 800ms) with caching
✓60% cost reduction compared to initial design through optimization
✓99.9% uptime with multi-AZ deployment and auto-scaling
✓Handles 5M+ queries per month with consistent performance
✓Integrates seamlessly with existing AWS infrastructure and services
✓Security compliance: VPC isolation, encryption at rest and in transit, audit logging
✓Cost optimization: Reduced from estimated $50K/month to $20K/month through Spot instances, caching, and right-sizing
✓Team enablement: Comprehensive documentation and knowledge transfer completed

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