AI Services

RAG systems for your private data

Turn policies, manuals, contracts, and support tickets into intelligent Q&A — grounded in your documents with source citations, not generic internet answers.

many languages RAG case study

94%

Answer accuracy

On internal doc benchmarks

6–8

Week delivery

Typical RAG MVP timeline

100%

Source citations

Every answer linked to docs

Training leakage

Your data never trains models

AI that knows your business

Generic LLMs don't know your products, policies, or procedures — and fine-tuning is expensive, slow to update, and risky for compliance. Retrieval-Augmented Generation (RAG) connects language models to your proprietary documents through semantic search, delivering accurate, cited answers from internal wikis, PDFs, contracts, and tickets without sending sensitive data to model training pipelines.

Document sources

PDFs, wikis, SharePoint, Google Drive, Zendesk tickets, and databases

Ingestion & chunking

Parse, clean, split, embed, and tag chunks with metadata and access control

Vector retrieval

Semantic search, hybrid keyword queries, reranking, and relevance scoring

Grounded generation

LLM answers with citations, guardrails, feedback loops, and analytics

Policy Assistant

You

What is our refund policy for enterprise annual plans?

Assistant

Enterprise annual plans include a 30-day full refund window from the contract start date. After 30 days, refunds are prorated for unused months and require manager approval documented in the billing system.

enterprise-terms.pdf · Page 12 · Section 4.2
billing-playbook.docx · Refund approval workflow

Challenges we solve

Why document Q&A projects fail — and how we engineer RAG pipelines that stay accurate, secure, and maintainable.

Hallucinated answers

LLMs invent facts when retrieval misses the right context. We tune chunk sizes, add rerankers, enforce citation requirements, and evaluate answer faithfulness against your document set before launch.

Stale knowledge bases

Documents change but embeddings don't. We build automated re-indexing pipelines triggered by uploads, CMS webhooks, or scheduled syncs — so answers reflect current policies, not last quarter's versions.

Access control gaps

Sensitive HR or legal docs must not appear in general Q&A. We implement document-level permissions, role-based retrieval filters, and audit logs for every query and source accessed.

Poor multilingual support

English-only embeddings fail on Swahili, Amharic, or mixed-language docs. We select multilingual embedding models and tune retrieval for language content — see our many languages RAG case study.

What we deliver

Core capabilities included in every rag systems engagement.

Document ingestion pipelines

PDF, Word, HTML, Markdown, and database content parsed, chunked, and indexed with metadata.

Vector search & reranking

Semantic retrieval with pgvector, Pinecone, or Weaviate — plus hybrid keyword search and rerankers.

Grounded Q&A interfaces

Chat widgets, Slack bots, and internal portals that cite source documents in every answer.

Knowledge base admin

Upload, re-index, tag collections, and monitor ingestion status via admin dashboards.

Evaluation & feedback loops

Benchmark suites, thumbs-up/down tracking, and retrieval quality dashboards for continuous tuning.

Security & compliance

Private cloud deployment, encryption at rest, access controls, and applicable data protection law-aligned data handling.

Where this fits

Where RAG delivers measurable ROI for enterprises and startups.

Internal policy Q&A

HR, legal, and finance teams get instant answers from handbooks and SOPs with verified citations.

Customer support copilots

Agents and self-service portals resolve tickets faster using product docs, FAQs, and release notes.

Sales enablement

Reps query pricing sheets, case studies, and competitor battle cards during live calls.

Multilingual knowledge

Serve Swahili, French, and English content from the same corpus with locale-aware retrieval.

Key benefits

Accurate answers grounded in your actual documents — with clickable source citations
Private data stays in your cloud — never used for LLM provider model training
Reduced support load through self-service document Q&A on web and Slack
Continuous improvement as new documents are ingested and indexed automatically
Multilingual retrieval tuned for language content and mixed-language docs

Why LesterTechLabs

Source Citations

Every answer links back to the original document page for verification.
Private & Secure

Your data never leaves your infrastructure or enters model training.
Self-Updating

Automatic re-indexing when documents are added, edited, or removed.

Our process

A proven delivery framework from kickoff to production.

01

Data assessment

Inventory document sources, formats, access rules, and evaluation criteria.
02

Pipeline build

Ingestion jobs, chunking strategy, embedding model selection, and vector store setup.
03

Retrieval tuning

Optimize chunk size, hybrid search, reranking, and citation formatting.
04

Deploy & monitor

User-facing Q&A with feedback loops, analytics, and re-indexing automation.

Technologies we use

Embedding models, vector stores, and LLM orchestration for production RAG workloads.

LangChain pgvector Pinecone Weaviate OpenAI Anthropic Python PostgreSQL Redis Docker Hugging Face FastAPI

Related services

Other ai services offerings you may need.

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LLM Integration

Task agents, support copilots, and model integrations for your product — OpenAI, Claude, and Gemini, with security and cost controls.

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Frequently asked questions

Common questions about rag systems.

What is RAG and why do I need it?

RAG (Retrieval-Augmented Generation) lets LLMs answer questions using your private documents via semantic search. It's essential when generic AI doesn't know your products, policies, pricing, or internal procedures — and when fine-tuning is too slow or risky to maintain.

What document formats and sources do you support?

PDF, Word, Excel, HTML, Markdown, plain text, and database records. We also connect to SharePoint, Google Drive, Confluence, Notion, Zendesk, and custom APIs for automated sync.

Is our data secure?

Yes. Documents are processed in your cloud environment (AWS, Azure, or on-premise). Embeddings are stored in your vector database. Queries go to LLM APIs with retrieved context only — your data is never used for model training.

How long does a RAG system take to build?

A focused MVP with one document collection, chat interface, and admin upload typically ships in 6–8 weeks. Multi-source ingestion, access control, Slack integration, and evaluation suites extend to 10–14 weeks depending on scope.

Can RAG work with Swahili and other many languages?

Yes. We use multilingual embedding models and tune chunking and retrieval for Swahili, Amharic, Yoruba, French, and mixed-language documents. Read our case study on building RAG for languages.

How do you prevent wrong or made-up answers?

We require source citations on every response, tune retrieval with rerankers and hybrid search, run faithfulness benchmarks before launch, and add guardrails that refuse to answer when confidence or retrieval scores are too low.