Our Services
Fluximetry helps organizations implement AI solutions, build RAG systems, optimize prompts, deploy local AI, and enable teams through hands-on consulting and training. Our engagements are tailored to your needs, infrastructure, and goals.
From initial strategy to production deployment, we work alongside your team to deliver practical, scalable AI solutions that drive real business value. Every engagement includes knowledge transfer, best practices, and the tools you need to succeed independently.
RAG (Retrieval Augmented Generation) Systems
Transform your knowledge base into an intelligent AI assistant
Design and implement production-ready RAG systems that enhance LLMs with your organization's data for accurate, contextual responses. RAG combines the power of large language models with your proprietary information, enabling AI assistants that understand your business, products, and processes. We build systems that deliver reliable answers, cite sources, and continuously improve through feedback loops.
What We Deliver
- ✓End-to-end RAG system architecture and implementation
- ✓Vector database design optimized for your data and query patterns
- ✓Intelligent document ingestion with semantic chunking strategies
- ✓Retrieval pipeline optimization for accuracy and speed
- ✓Context window management and prompt construction
- ✓Evaluation frameworks and metrics for continuous improvement
Ideal For
- →Internal knowledge bases and documentation systems
- →Customer support and help desk automation
- →Technical documentation and API reference systems
- →Research and information retrieval applications
- →Enterprise search and content discovery
Technologies: Vector databases (Pinecone, Weaviate, Chroma, Qdrant), embedding models (OpenAI, Cohere, local), LangChain, LlamaIndex, LLM APIs
Prompt Engineering & Optimization
Maximize LLM performance and minimize costs through expert prompt design
Craft effective prompts and optimize AI interactions for better results, cost efficiency, and reliable outputs. Prompt engineering is both an art and a science—we combine proven methodologies with iterative testing to deliver prompts that consistently produce high-quality results. Our approach reduces token usage, improves accuracy, and ensures predictable behavior across different models and use cases.
Our Approach
- ✓Prompt design methodologies based on task type and model capabilities
- ✓Few-shot learning and chain-of-thought prompting techniques
- ✓Prompt versioning, A/B testing, and performance tracking
- ✓Token optimization strategies to reduce costs by 20-40%
- ✓Role-based prompt templates and reusable patterns
- ✓Best practices documentation and prompt libraries
Key Benefits
- →Significant cost reduction through optimized token usage
- →Improved response quality and consistency
- →Faster time-to-production with proven patterns
- →Reduced hallucinations and incorrect outputs
- →Team enablement with prompt engineering skills
Deliverables: Prompt libraries, testing frameworks, optimization reports, team training, documentation and best practices guides
Local AI Deployment
Deploy and run AI models on your infrastructure for privacy, control, and cost savings
Deploy and run AI models locally for privacy, cost control, and offline capabilities. Local AI deployment gives you complete control over your data, eliminates API costs at scale, and ensures compliance with data residency requirements. We help you choose the right models, optimize for your hardware, and integrate seamlessly with your existing infrastructure. Our home lab expertise includes everything from hardware selection to production-ready infrastructure setup.
Implementation Services
- ✓Model selection based on use case, hardware, and requirements
- ✓Self-hosted LLM infrastructure setup and configuration
- ✓Quantization and optimization for efficient local deployment
- ✓GPU/CPU configuration and resource management
- ✓Integration with existing systems and workflows
- ✓Monitoring, scaling, and performance optimization
Home Lab & Infrastructure Setup
- →Hardware recommendations (GPUs, RAM, storage) for AI workloads
- →Proxmox, Docker, and Kubernetes setup for containerized AI
- →Network configuration and optimization (10GbE setup)
- →Storage solutions (ZFS, NFS) for model and data storage
- →Monitoring stack (Grafana, Prometheus) for observability
- →Backup strategies and disaster recovery planning
When to Choose Local AI
- →Strict data privacy or compliance requirements (HIPAA, GDPR, etc.)
- →High API costs at scale (thousands of requests per day)
- →Need for offline capabilities or air-gapped environments
- →Custom fine-tuning or model modification requirements
- →Latency-sensitive applications requiring sub-100ms responses
Solutions: Ollama, vLLM, TGI (Text Generation Inference), LocalLlama models, quantization tools (GGUF, AWQ), containerization strategies
Agentic Coding & AI Development Tools
Enhance developer productivity with intelligent AI agents and coding assistants
Implement AI agents and tools that enhance developer productivity and automate coding workflows. Agentic coding goes beyond simple code completion—we build sophisticated AI agents that can plan, execute, and verify complex development tasks. Our solutions integrate seamlessly with your development environment and workflows.
Capabilities
- ✓AI coding assistant integration (GitHub Copilot, Cursor, custom solutions)
- ✓Agent architecture design for autonomous coding tasks
- ✓Tool use and function calling strategies for agent workflows
- ✓Code generation, refactoring, and optimization automation
- ✓Automated testing and quality assurance with AI tools
- ✓Documentation generation and code review automation
Use Cases
- →Automated code generation from specifications or documentation
- →Legacy code modernization and refactoring
- →Test suite generation and maintenance
- →Bug detection and automated fixes
- →Code review and quality assurance automation
LLM Evaluation & Quality Assurance
Ensure reliable AI outputs with comprehensive evaluation frameworks
Build evaluation frameworks, quality metrics, and A/B testing systems to ensure reliable AI outputs and optimal performance. Effective LLM evaluation is critical for production systems—we design comprehensive testing strategies that measure accuracy, relevance, safety, and cost-effectiveness across different models and configurations.
Evaluation Framework
- ✓Custom evaluation metrics tailored to your use case
- ✓Automated testing pipelines and continuous evaluation
- ✓A/B testing frameworks for model and prompt comparison
- ✓Performance monitoring and alerting systems
- ✓Quality scoring and regression detection
- ✓Cost analysis and optimization recommendations
What We Measure
- →Accuracy and correctness of responses
- →Relevance and context understanding
- →Safety, toxicity, and bias detection
- →Latency and response time metrics
- →Token usage and cost per query
- →User satisfaction and feedback integration
AI Coaching Programs
Structured programs to enable your team with AI skills and best practices
Structured coaching frameworks and learning programs to enable teams with AI skills, best practices, and prompt libraries. Our coaching programs combine hands-on learning with practical frameworks your team can apply immediately. We focus on building internal AI capabilities rather than creating dependency on external consultants.
Program Components
- ✓Customized curriculum based on team needs and experience level
- ✓Hands-on workshops with real-world projects
- ✓Prompt libraries and reusable templates
- ✓Best practices documentation and guidelines
- ✓Regular check-ins and ongoing support
- ✓Knowledge sharing and internal enablement strategies
Topics Covered
- →Fundamentals of LLMs and AI capabilities
- →Prompt engineering techniques and patterns
- →RAG system design and implementation
- →Evaluation and quality assurance strategies
- →Cost optimization and best practices
Advanced RAG Optimization
Enterprise-grade RAG systems with reranking, hybrid search, and multi-stage retrieval
Reranker integration, hybrid search, multi-stage retrieval, and RAG agent architectures for enterprise-grade systems. Move beyond basic RAG implementations to sophisticated systems that handle complex queries, large document collections, and high-stakes use cases. We implement advanced techniques that significantly improve retrieval accuracy and response quality.
Advanced Features
- ✓Reranker integration for improved relevance (BGE, Cohere, cross-encoders)
- ✓Hybrid search combining semantic and keyword matching
- ✓Multi-stage retrieval pipelines (coarse-to-fine search)
- ✓RAG agent architectures with planning and tool use
- ✓Query decomposition and multi-query strategies
- ✓Metadata filtering and structured data integration
Performance Improvements
- →20-40% improvement in retrieval accuracy with reranking
- →Better handling of complex, multi-part questions
- →Reduced false positives and irrelevant results
- →Scalability for millions of documents
- →Optimized latency for real-time applications
Enterprise Context Engineering
Optimize context windows and manage knowledge at scale for large AI deployments
Optimize context windows, design enterprise knowledge bases, and manage context at scale for large AI deployments. Context engineering is crucial for enterprise AI systems that must handle vast amounts of information efficiently. We design strategies that maximize information density while minimizing costs and latency.
Services
- ✓Context window optimization and management strategies
- ✓Enterprise knowledge base architecture and design
- ✓Information compression and summarization techniques
- ✓Hierarchical context management for complex documents
- ✓Multi-source context aggregation strategies
- ✓Cost and performance optimization for large-scale deployments
Enterprise Challenges Solved
- →Managing context limits with large knowledge bases
- →Reducing token costs for high-volume applications
- →Integrating multiple data sources and systems
- →Maintaining accuracy with compressed context
- →Scaling to millions of documents and users
Sales Engineering Enablement
AI tools and frameworks specifically designed for Sales Engineers
AI tools and frameworks specifically for Sales Engineers: demo automation, technical content generation, and competitive positioning. Sales Engineers face unique challenges—they need to quickly understand customer requirements, create compelling technical demonstrations, and articulate competitive advantages. Our AI solutions are designed specifically for these needs.
SE-Specific Tools
- ✓Demo automation and interactive demo generation
- ✓Technical content generation (architecture diagrams, solution briefs)
- ✓Competitive positioning and battle card generation
- ✓Customer research and discovery automation
- ✓Technical proposal and RFP response generation
- ✓Solution architecture and design assistance
Key Benefits
- →Faster response times to customer inquiries and RFPs
- →Consistent, high-quality technical content
- →More time for customer engagement vs. content creation
- →Better competitive positioning with data-driven insights
- →Improved win rates through better-prepared technical presentations
Technical Training & Workshops
Comprehensive training programs to empower your technical teams
Empower your technical teams with AI skills, best practices, and hands-on experience through comprehensive training. Our training programs are designed by practitioners, for practitioners. We focus on real-world scenarios, hands-on exercises, and immediately applicable skills that your team can use the next day.
Training Formats
- ✓Multi-day intensive workshops with hands-on labs
- ✓Half-day and full-day focused sessions
- ✓Ongoing coaching and office hours
- ✓Custom curriculum tailored to your tech stack
- ✓Follow-up sessions and advanced topics
- ✓Remote and on-site delivery options
Course Topics
- →LLM fundamentals and capabilities deep-dive
- →Advanced prompt engineering techniques
- →RAG system design and implementation
- →Evaluation and quality assurance
- →Production deployment and scaling
AWS AI Infrastructure & Services
Scalable AI solutions on AWS with optimized architecture and cost management
Design and deploy production-ready AI solutions on AWS. We architect scalable infrastructure using SageMaker, Bedrock, ECS, Lambda, and other AWS services to deliver cost-effective, reliable AI systems that integrate seamlessly with your existing AWS environment.
AWS Services & Solutions
- ✓AWS SageMaker for model training and deployment
- ✓AWS Bedrock integration for managed LLM APIs
- ✓ECS/EKS container orchestration for AI workloads
- ✓Lambda functions for serverless AI processing
- ✓S3 + Athena for AI data storage and querying
- ✓VPC design for secure AI infrastructure
- ✓API Gateway for AI service endpoints
- ✓CloudWatch and X-Ray for monitoring and observability
- ✓Cost optimization and resource management strategies
Implementation Benefits
- →Scalable architecture for growing workloads and traffic
- →Cost-effective use of AWS resources with right-sizing
- →Seamless integration with existing AWS services and infrastructure
- →High availability and disaster recovery built-in
- →Security and compliance best practices (IAM, encryption, audit logging)
- →Multi-region deployments for global applications
- →Managed services reduce operational overhead
- →Pay-as-you-go pricing model for cost control
Common Use Cases
Ready to Get Started?
Let's discuss how we can help you implement AI solutions that drive real business value. Every engagement is tailored to your specific needs, timeline, and goals.