AI Development
RAG Knowledge Base Solutions
We build retrieval-augmented generation (RAG) systems that use your documents as the source of truth—so assistants answer accurately, cite context, and stay safer in production.
Overview
What this service is
This service builds a RAG pipeline: document ingestion, chunking, embeddings, and retrieval strategy aligned to your content and user queries.
We tune retrieval quality and response behaviour so answers stay grounded, include relevant context, and fail gracefully when the content doesn’t contain an answer.
Delivery includes evaluation hooks and update guidance so your team can keep the knowledge base current without breaking retrieval quality.
Benefits
What you get
More accurate answers
Ground responses in approved content to reduce hallucinations and improve trust.
Faster support and onboarding
Teams and customers find answers quickly without waiting for human availability.
Control what the assistant knows
Use your docs and rules as the source of truth, not generic internet knowledge.
Maintainable updates
Ingestion and indexing workflows designed for ongoing content changes.
Permission-aware patterns (optional)
Scope access rules so sensitive docs aren’t exposed to the wrong users.
Measurable quality improvements
Evaluation and feedback hooks so accuracy gets better over time.
Features
What we deliver
Document ingestion pipeline
Import PDFs, docs, web pages, and structured content with normalisation and metadata.
Chunking + embeddings strategy
Chunk sizing and embedding configuration tuned to your content types and query patterns.
Retrieval tuning
Ranking, filters, and guardrails that improve relevance and reduce noisy context.
Grounded response behaviour
Answer formatting, citations/context, and fallback behaviour when retrieval is weak.
Evaluation + feedback hooks
Quality checks and feedback capture so you can improve retrieval and answers iteratively.
Deployment + update guidance
Runbook-style notes for adding new sources, reindexing, and monitoring retrieval health.
Process
How we work
Discovery
We define user queries, content sources, and quality expectations to shape the RAG design.
Ingestion setup
We implement parsing, chunking, and metadata rules so content is indexed consistently.
Retrieval tuning
We tune relevance and filters, then validate answers against a set of representative questions.
Integration
We expose the pipeline via API/UI and add feedback hooks for quality iteration.
Handoff
We deliver documentation for updates, reindexing, and monitoring retrieval quality over time.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Internal SOP and policy assistant
Answer questions from internal docs with permission-aware retrieval for different teams.
Customer help centre assistant
Grounded answers that match your official docs and reduce support ticket volume.
Sales enablement search
Help teams find product details, pricing rules, and approved collateral quickly.
Technical documentation assistant
Answer developer questions with context from API docs, guides, and changelogs.
Compliance and audit support
Find policy text and evidence faster with traceable context from approved sources.
FAQ
Frequently asked questions
Yes. We can include retrieved context and citations/links so users can verify answers and build trust.
Yes. Common formats like PDF, Markdown, HTML, and exported docs can be handled. We’ll confirm your formats during discovery.
We build ingestion and reindexing workflows with clear guidance so new documents can be added safely without breaking retrieval quality.
Yes. We can scope retrieval by user roles or access groups so sensitive sources aren’t visible to the wrong users.
Often, yes—especially when your content changes frequently. We’ll recommend the best approach based on update cadence and accuracy needs.
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Need answers grounded in your documents?
Share sample docs and the assistant’s goals. We’ll design a RAG pipeline and rollout plan that fits your users and constraints.
Indexing + tuning + handoff included.