An assistant that answersfrom your documents, not its imagination.
The difference between a useful assistant and an embarrassing one is grounding. Ours answer from your indexed content, cite the page they used, and say they do not know rather than inventing a refund policy.
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In short
Byteinfy builds AI chatbots and assistants from $2,500, grounded in your own documents and data with retrieval so answers are citable, plus a handoff to a human and a log of every conversation you can audit.
Last updated 8 September 2026 · Written by the Byteinfy team, which builds and operates SwipeReel and myBillPlease.
Who it is for
- Support teams answering the same forty questions every week
- Businesses with documentation nobody can find
- Stores that lose sales to unanswered pre-purchase questions at 1am
- Internal teams who need to query policy, stock or order data in plain language
What you get
- Retrieval over your own content — site, documents, help centre, product catalogue — kept in sync as it changes
- Answers with citations back to the source page, so a wrong answer is traceable
- Explicit refusal behaviour and escalation to a human, with the conversation context carried over
- Actions where you want them: order lookup, ticket creation, booking, all scoped and permissioned
- Conversation logs, analytics on what people actually ask, and the gaps in your documentation it exposes
- Deployment on your site, WhatsApp or inside your existing helpdesk
How it works
- 1
Ground
We index your content first and measure answer quality against real questions before anything ships.
- 2
Constrain
Scope, tone and refusal rules written down, then tested adversarially — including the questions you would not want answered.
- 3
Launch
Live behind a handoff, with logs reviewed weekly for the first month and the retrieval tuned on what we find.
Pricing
From $2,500
A support or sales assistant grounded in your content is $2,500 to $6,000. Assistants that take actions in your systems are quoted with the integrations.
Questions we get asked
- Will it make things up?
- Retrieval-grounded answers with citations make that rare, and we test for it explicitly. We also make it say it does not know, which is the behaviour most implementations skip.
- Which model?
- Whichever fits your accuracy, latency and cost constraints — and we build so it can be swapped, because the leading model changes roughly every quarter.
- Where does our data go?
- Into your own vector store and your own model account. We do not proxy your customer conversations through infrastructure you cannot see.
- What does it cost to run?
- We estimate monthly token and hosting cost from your actual traffic during the build, so it is a number in the proposal rather than a surprise.
Further reading
From the Byteinfy blog, where we write up what we learn doing this work.
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