RAG & Knowledge
Answers from your knowledge. Not from guesses.
RAG (retrieval-augmented generation) means the AI pulls from your controlled documents before it answers. In plain business language: the AI only speaks from what you give it.
The problem
The work behind the busywork.
Generic AI answers from the internet — which is wrong for internal policies, product specs and company processes. Knowledge systems restrict the AI to your documents, so answers stay traceable and on-brand.
Typical use
- Internal knowledge assistant
- Company documentation assistant
- Support knowledge system
- Document Q&A
- Private knowledge retrieval
What we build
Engineered for this category.
Private knowledge assistants
Your teams ask questions and get answers from your own material.
Document grounding
Responses cite the source document they were drawn from.
Support knowledge systems
Agents and support staff share one accurate answer base.
Ingestion & updates
New documents refresh the system without rebuilds.
How the workflow runs
One chain, clearly mapped.
The lifecycle your system follows — every step visible, every handoff accountable.
Steps may vary in the build — this is the baseline shape.
01Your documents
Policies · specs · guides
Knowledge is imported from the sources you control.
02Index
Structured knowledge base
The material is organized so it can be found precisely.
03Question
Team or customer
Someone asks in plain language.
04Retrieve
Relevant passages
The system pulls the right passages from your data.
05Answer
Grounded response
It answers from those passages — with the source shown.
Integrations
Connects to what you already run.
Connects to the documents and knowledge stores your company already keeps.
- Documents
- Wikis
- Databases
- CRMs
- Communication channels
Human oversight
People stay in control.
Sources are curated and permission-controlled, so the AI can only speak from what you've chosen to give it — with citations your team can check.
Implementation
How it gets built.
Discover
Understand the business and workflow.
Map
Document the current process and bottlenecks.
Identify
Find the highest-value AI and automation opportunities.
Architect
Design the system, agents, integrations, data flow and human controls.
Engineer
Build the workflows, AI agents, integrations and interfaces.
Validate
Test outputs, edge cases, failures, permissions and human approval paths.
Operate
Monitor, improve and evolve the system.
Service questions
Made for business owners.
Before answering, the AI searches your own documents and answers only from them. No more outdated or invented answers — and every reply can point to its source.
Related system concepts
Related system concepts.
Engineered shapes from the Work section that carry this kind of process.
Services · Next step
Have a workflow worth automating?
Tell us what your team does manually today. We'll help map where AI, automation and human decision-making can fit.
No pitches before we understand the problem.