Frequently Asked Questions

Common questions about our AI consulting services and approach

General

Q:What does Fluximetry do?

A: Fluximetry specializes in AI consulting, helping organizations implement RAG systems, optimize prompts, deploy local AI, build agentic coding tools, and enable teams through training and hands-on consulting. We focus on practical, production-ready AI solutions.

Q:What types of organizations do you work with?

A: We work with engineering teams, sales organizations, technical startups, and enterprises looking to leverage AI. Our clients range from companies building custom AI solutions to teams seeking enablement and training.

Q:What is your approach to AI consulting?

A: We focus on hands-on, practical solutions. We build alongside your team, provide training and enablement, and ensure knowledge transfer. Our approach is collaborative, vendor-neutral, and results-oriented.

Q:Do you work remotely or on-site?

A: We primarily work remotely, but can arrange on-site engagements when needed. Most of our consulting and training can be effectively delivered remotely with modern collaboration tools.

Services

Q:What is a RAG system?

A: RAG (Retrieval Augmented Generation) systems combine vector databases with LLMs to provide AI-powered search and Q&A over your documents. They retrieve relevant context from your data and use it to generate accurate, contextual responses. This is ideal for knowledge bases, documentation, and internal search systems.

Q:How long does a typical RAG implementation take?

A: Implementation timelines vary based on scope. A basic RAG system might take 2-4 weeks, while enterprise deployments with advanced features (reranking, hybrid search, multiple data sources) can take 2-3 months. We work with your timeline and can deliver in phases.

Q:What is prompt engineering and why do I need it?

A: Prompt engineering is the practice of designing effective prompts to get the best results from LLMs. Well-engineered prompts can improve accuracy, reduce costs, and ensure consistent outputs. We help you develop prompt libraries, testing frameworks, and optimization strategies.

Q:Can you help us deploy AI models locally?

A: Yes! We specialize in local AI deployment for privacy, cost control, and compliance. We help with model selection, hardware configuration, quantization, and integration with your existing infrastructure.

Q:What is agentic coding?

A: Agentic coding uses AI agents to assist developers with code generation, refactoring, testing, and documentation. We help you integrate and customize AI coding assistants, design agent workflows, and implement best practices for AI-assisted development.

Q:Do you provide training for our team?

A: Yes, we offer comprehensive training programs including workshops, hands-on labs, best practices, and ongoing support. Training can be customized to your team's needs and experience level.

Engagement & Pricing

Q:What engagement models do you offer?

A: We offer project-based consulting, embedded engineering (short or long-term), training and workshops, and strategic advisory. Engagements can range from a few weeks to ongoing support. We tailor our approach to your needs.

Q:How do you price your services?

A: Pricing depends on engagement type, scope, and duration. Project-based consulting is typically quoted per project. Embedded engineering is usually a monthly retainer. Training is priced per session with group discounts. Contact us for a custom quote.

Q:What is the typical engagement length?

A: It varies: consulting projects typically last 2-12 weeks, embedded engineering can be 1-12 months or ongoing, and training workshops are usually 1-5 days. We work with your timeline and can adjust as needed.

Q:Do you offer ongoing support?

A: Yes, we offer ongoing support and maintenance options. This can include regular check-ins, troubleshooting, optimization, and updates to keep your AI systems running smoothly.

Technical

Q:What technologies and tools do you work with?

A: We work with a wide range of AI tools and frameworks including OpenAI, Anthropic, local LLMs (Ollama, vLLM), vector databases (Pinecone, Weaviate, Chroma, Qdrant), LangChain, LlamaIndex, and various cloud and self-hosted solutions. We're vendor-neutral and recommend what's best for your use case.

Q:Can you integrate with our existing systems?

A: Absolutely. We specialize in integrating AI solutions with existing infrastructure, databases, APIs, and workflows. We work within your technology stack and constraints.

Q:How do you handle data privacy and security?

A: Security and privacy are priorities. We can deploy solutions that keep data on-premises, implement privacy-preserving techniques, and ensure compliance with regulations like HIPAA, GDPR, etc. We follow security best practices and can work with your security team.

Q:Do you help with LLM evaluation and quality assurance?

A: Yes! We build comprehensive evaluation frameworks, quality metrics, and testing systems. This includes A/B testing, performance monitoring, cost analysis, and ongoing optimization of your AI systems.

Q:What is the difference between basic RAG and advanced RAG optimization?

A: Basic RAG uses simple vector search to retrieve chunks and generate responses. Advanced RAG adds reranking (using cross-encoder models to re-score results), hybrid search (combining vector and keyword search), multi-stage retrieval (coarse-to-fine search), and RAG agent architectures. Advanced RAG typically improves accuracy by 20-40% but requires more complexity.

Q:Can you help us reduce our LLM API costs?

A: Absolutely! We specialize in cost optimization through prompt engineering (reducing token usage by 20-40%), model selection (using cheaper models where appropriate), caching strategies, and local AI deployment for high-volume use cases. We've helped clients reduce costs by 30-60% while maintaining quality.

Q:What is enterprise context engineering?

A: Enterprise context engineering is the practice of optimizing how you manage and use context windows in LLM applications at scale. This includes context window optimization, designing enterprise knowledge bases, managing multi-source data integration, information compression, and strategies for handling large document collections efficiently. It's crucial for enterprise deployments that need to handle vast amounts of information.

Implementation & Process

Q:What does your implementation process look like?

A: Our process is iterative and collaborative: 1) Discovery and requirements gathering, 2) Proof of concept to validate approach, 3) Iterative development with regular feedback, 4) Evaluation and optimization, 5) Production deployment, 6) Knowledge transfer and documentation. We work in short cycles, typically 1-2 weeks, delivering value continuously.

Q:How long does a typical project take?

A: Timelines vary by scope: Basic RAG systems (2-4 weeks), Advanced RAG with reranking (4-8 weeks), Local AI deployment (4-12 weeks), Evaluation frameworks (3-6 weeks), Training programs (4-12 weeks depending on depth). Most projects can be delivered in phases, starting with core functionality and adding features iteratively.

Q:Do you work with our existing tech stack?

A: Yes, we integrate with your existing infrastructure, databases, APIs, and workflows. We work with all major cloud providers (AWS, GCP, Azure), programming languages (Python, JavaScript/TypeScript, Go, etc.), and frameworks. We're vendor-neutral and recommend solutions that fit your stack.

Q:What kind of documentation do you provide?

A: We provide comprehensive documentation including: architecture diagrams, API documentation, deployment guides, operational runbooks, prompt libraries, evaluation frameworks, troubleshooting guides, and knowledge transfer materials. All documentation is tailored to your team's needs and experience level.

Q:How do you ensure knowledge transfer?

A: Knowledge transfer is built into every engagement: pair programming and collaborative development, comprehensive documentation, training sessions and workshops, code reviews and architectural walkthroughs, regular knowledge sharing sessions, and ongoing support during transition. We measure success by your team's ability to maintain and extend solutions independently.

Still have questions?

We're here to help! Get in touch and we'll answer any questions about how we can help your organization leverage AI.