AI agents / Knowledge & action
AI agents and RAG chatbots grounded in business data
Friday Works builds AI agents and RAG chatbots that retrieve authoritative business knowledge, cite sources and perform carefully bounded actions. The system is designed around access control, answer quality, cost and a route to human help when confidence is insufficient.
Best-fit scope
When does this service create value?
Internal knowledge chatbot
Answer from policy, technical and process documents according to each user's access.
Customer assistance
Explain approved information, collect context and transfer cases that need a person.
Task-support agent
Prepare drafts, create tickets or call APIs within explicit permissions and approval points.
Deliverables
What your team receives
- 01
Data-source, retrieval, model and permission architecture.
- 02
A chat interface or agent workflow connected to relevant systems.
- 03
An evaluation set, guardrails, source citations and fallback behaviour.
- 04
Logs, analytics and guidance for knowledge updates and operation.
Process
From business problem to operating system
01. Define the job
Agree users, question types, permitted actions and cases the system must refuse.
02. Prepare knowledge
Select authoritative sources, manage versions and permissions, and create a representative evaluation set.
03. Prototype & evaluate
Measure relevance, groundedness, fallback rate, latency and cost on real examples.
04. Integrate & observe
Connect actions in stages, add approval and monitor feedback for improvement.
Timeline
A roadmap shaped by scope and evidence
Focused RAG chatbot
Often 3–6 weeks when documents and permissions are clear.
Integrated agent
Often 6–12 weeks depending on tools, actions and safety requirements.
Expanded autonomy
Only considered when evaluation data shows suitable quality and controls.
Frequently asked questions
Before we begin
01Can a RAG chatbot still give a wrong answer?
Yes. RAG helps ground an answer in sources but cannot eliminate every error. The system needs citations, an evaluation set, fallback thresholds and a route to a person.
02How is an AI agent different from a chatbot?
A chatbot mainly exchanges information; an agent can plan steps and use tools to act. That is why agents require stronger permission limits, approvals and logging.
03Can internal data remain permissioned?
The architecture should preserve least-privilege access. Users should retrieve only the documents and actions their role permits; the exact controls are defined during discovery.
Talk to Friday Works
Bring business knowledge into the right workflow
Share the users, document sources and actions the assistant should support. We will define a RAG or agent scope that can be evaluated.
Describe your project