AI Development
RAG Knowledge Base Integration Solutions
We build and integrate retrieval-augmented generation (RAG) knowledge bases 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 or integrates a RAG knowledge base: document ingestion, chunking, embeddings, and a retrieval strategy aligned to your content, product, 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
No. Softment designs and integrates a custom knowledge-base system around your content, permissions, existing stack, and workflows. We can reuse proven components, but the deployment and data model are specific to your requirements.
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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Regional
Delivery considerations for your region
Data and risk discovery (Australia)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Australia)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Australia)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Australia)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Need a RAG knowledge base integrated into your product?
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.