Softment

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.

TimelineTypical: 2–6 weeks (scope-dependent)
Starting at£1.5k
Security-first AI integrations • Evals + logging + guardrails included

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

1
2–4 days

Discovery

We define user queries, content sources, and quality expectations to shape the RAG design.

2
3–7 days

Ingestion setup

We implement parsing, chunking, and metadata rules so content is indexed consistently.

3
1–3 weeks

Retrieval tuning

We tune relevance and filters, then validate answers against a set of representative questions.

4
1–2 weeks

Integration

We expose the pipeline via API/UI and add feedback hooks for quality iteration.

5
2–4 days

Handoff

We deliver documentation for updates, reindexing, and monitoring retrieval quality over time.

Tech Stack

Technologies we use

Core

EmbeddingsVector DB (pgvector/Pinecone/Weaviate)RAG pipelinesLangChain (or equivalent)

Tools

OpenAI APIChunking + metadataEvaluation harnessAuth/permissions (optional)

Services

API + webhook integrationsMonitoring/logging

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.

Ready to start?

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.