AIKIT SOLUTIONS
AI Assistants for Support, Sales & Knowledge
Assistants that answer from approved knowledge, hand off to a person when confidence is low, and connect to the APIs a business already uses.
What this service is
An assistant is not a novelty chat widget. It is a retrieval-and-reply loop: question, search, grounded answer, optional tool call, optional human escalation.
That includes AI customer support, website chatbots, internal assistants, and RAG chatbots that stay inside a documented knowledge set.
Problems it solves
Repeat questions that already have a written answer, and staff time spent hunting the same PDF.
Bots that invent policy because they were never connected to a knowledge base or an escalation path.
What I build
- Customer support assistants
- Website chatbots
- Internal assistants
- Lead qualification helpers
- Document assistants
- Knowledge retrieval
- Source-grounded answers
- Human escalation
- API integrations
- RAG systems
Common use cases
- On-site support that cites policy pages
- Intake bots that prepare a brief for sales
- Internal Q&A over documentation
- After-hours answers that still refuse to guess
How the workflow works
The user asks a question. The system searches approved documents (RAG), drafts a short answer, and attaches sources.
If retrieval is weak or the topic is high-risk, the assistant should say so and offer a human handoff — not fill the gap with fiction.
Retrieval quality depends on knowledge systems. A DEMO / CONCEPT of grounded support is AI Customer Support; document lookup is Smart Document Search.
When this is useful
Useful when policies, product docs, or FAQs already exist and you want them reachable in chat without replacing your team.
FAQ
What is a RAG assistant?
RAG (retrieval-augmented generation) means the model looks up approved passages first, then drafts an answer from those passages instead of relying on memory alone. That keeps replies closer to your policies and product text. If nothing relevant is found, the assistant should say so rather than invent an answer.
Can the assistant answer from our documents?
A production design can ingest your files, chunk them, and keep the index in sync as documents change. Access control and update jobs are part of that design. Portfolio demos use mock knowledge only and are not connected to a private store.
Can answers include sources?
Showing the file or page used for the answer is the default goal. Sources let a person verify the passage and make empty retrieval obvious. If there is no supporting fragment, the assistant should not invent a citation.
Can a conversation be handed to a person?
Handoff is part of the design: low confidence, missing documents, or topics you mark as human-only. The assistant can collect context and pass it to an operator, helpdesk, or CRM when those systems are connected. The demos on this site illustrate the path; they do not open live tickets.
How is this different from a typical chatbot?
A typical chatbot often answers from general training. A RAG assistant is constrained to your approved corpus and should fail closed when that corpus is silent. Quality still depends on document coverage, evaluation questions, and a human path for risk.
Can it connect to our existing systems?
Tickets, CRM notes, order lookup, and similar actions can be added through APIs. Each connection needs authentication, logging, and a rule for what the assistant may change. Risky writes can wait for a person. Nothing on these portfolio pages is a live business integration.