What We Do
Most AI consulting stops at proof-of-concept. Lighthouse Consulting builds AI capabilities as full-stack engineering projects: integrated into your data, connected to your APIs, tested in your environment, and deployed to production. We've built AI systems on top of enterprise data for clients in healthcare, logistics, and e-commerce — the same patterns apply regardless of the model or use case.
AI & LLM Capabilities
- LLM integration — OpenAI (GPT-4o), Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral) integrated into your existing application stack.
- Retrieval-Augmented Generation (RAG) — semantic search over your documents, knowledge bases, and data using vector databases (Pinecone, pgvector, Weaviate) so the model answers from your content, not general training.
- AI-powered workflow automation — document classification, routing, extraction, and approval workflows that reduce manual review time.
- Document processing pipelines — OCR, structured data extraction, contract review, and intake processing at scale.
- Custom AI features in existing products — adding AI functionality to web apps, internal tools, and SaaS platforms without a full rewrite.
- Prompt engineering and optimization — systematic prompt design, testing, and cost reduction for production workloads.
- AI evaluation and reliability — output testing, hallucination detection, and human-in-the-loop guardrails for high-stakes workflows.
- Fine-tuning and embeddings — domain-specific model customization and embedding pipelines for semantic search and classification.
Common Use Cases
- Internal knowledge bases that answer questions from your documentation and SOPs
- Customer support triage and response drafting with human review
- Contract and intake form review with structured extraction
- AI-assisted search across large product catalogs or document libraries
- Automated classification and routing of incoming requests
- Content generation pipelines with brand voice and guardrails
Our Approach
AI consulting is full of teams that will build you a compelling demo and then leave. We approach AI the same way we approach any engineering project: understand the data, design for the failure modes, build the integration with the rest of your stack, and ship something that works reliably under real conditions.
We also don't assume LLMs are the right answer to every problem. Some workflows need a structured classifier, a rules engine, or a simple search index. We'll help you choose the approach that matches the problem — not the one that looks most impressive in a pitch deck.