What this is
A local AI system can summarize, answer questions, and help teams find knowledge. It runs on controlled infrastructure and needs governance and evaluation to be trustworthy.
When to use what
- RAG when you need up-to-date knowledge from documents
- LoRA/partial fine-tuning when you need style/format/domain adaptation
- Both when you need grounded answers + tailored behavior
Who it’s for
- SMEs with sensitive documents (finance/legal/HR)
- Teams that can’t use external LLM APIs for compliance reasons
- Agencies building AI features for clients with privacy requirements
- Internal teams wanting a knowledge assistant for support/sales/ops
Typical engagements
Local AI Feasibility & Prototype (2–4 weeks)
- Select use case, scope, success criteria
- Minimal RAG prototype on a sample document set
- Evaluation checklist + next steps
Production Self-Hosted RAG (4–10 weeks)
- Ingestion pipeline, chunking, embeddings, indexing
- Permissions / access model concept
- Evaluation harness (regression tests)
- Monitoring (latency, retrieval quality proxies, failure logs)
Model Adaptation (LoRA / partial fine-tuning) (2–8 weeks)
- Dataset design (instructions, examples)
- LoRA/QLoRA experiments
- Evaluation against baseline
- Deployment strategy and rollback
Example tasks
- Internal “document assistant” with citations and access control
- Summarize long PDFs and produce structured outputs
- Draft internal emails/FAQs based on company docs
- Build a knowledge base that stays private
- Tailor outputs to your company style (templates, formats)
Deliverables
- Running system (prototype or production) + documentation
- Evaluation harness + test set guidance
- Deployment docs (how to run/update)
- Handover workshop + maintenance plan options
For technical readers
- Ingestion pipeline: parsing, chunking strategies, metadata
- Embeddings and retrieval: vector store selection, indexing strategy
- RAG prompt strategy with citations and traceability
- Evaluation harness: golden set, regression tests, human review loop
- LoRA/QLoRA / partial fine-tuning experiments
- Quantization and performance optimization for local hardware
- Governance: access control, audit logs, safe deployment patterns
Why I’m good at this
- Hands-on experience building local workflows with LLaMA/Mistral/BERT-style models
- Built RAG pipelines (embeddings, indexing, retrieval) for offline/internal use
- Understands performance and deployment constraints on local hardware
- Strong focus on governance, evaluation, and maintainability
How I work
- Start with a well-scoped use case and a small document set
- Define what “good” means (accuracy, citations, safety, latency)
- Iterate quickly: prototype → evaluation → production hardening
- Deliver a maintainable system with docs and handover