{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"Can we just use NotebookLM or a custom GPT instead?","acceptedAnswer":{"@type":"Answer","text":"Often, yes. If you do not need a branded page of your own, do not care about seeing the question history, and are fine with per-seat pricing, an off-the-shelf tool gets you most of the way in an afternoon. We built custom because this client needed all three: a front door they own, a log of every question asked, and a flat monthly cost."}},{"@type":"Question","name":"What does it cost to run a document chatbot like this?","acceptedAnswer":{"@type":"Answer","text":"Roughly $75 to $100 a month for the three services, plus the one-time build. That is Vercel Pro at $20 a month, a $50 a month minimum on Pinecone Standard, and Neon free tier, which is genuinely sufficient at this scale."}},{"@type":"Question","name":"Can a RAG chatbot run entirely on free tiers?","acceptedAnswer":{"@type":"Answer","text":"For a prototype, yes. For a client deployment, no. Vercel Hobby is non-commercial only and their definition of commercial covers a paid consultant writing the code. Pinecone free Starter caps Assistant at 100 documents and 1.5 million input tokens a month, which a real document set will exceed."}},{"@type":"Question","name":"Do you need a separate vector database with Pinecone Assistant?","acceptedAnswer":{"@type":"Answer","text":"No. Pinecone Assistant handles chunking, embedding, indexing and retrieval for you, so you upload documents and query them. We use Neon for the prompt log, not for vectors."}},{"@type":"Question","name":"What stops the chatbot from making things up?","acceptedAnswer":{"@type":"Answer","text":"Grounding. Pinecone Assistant answers from the documents you loaded rather than from general knowledge, so it is much less likely to invent something. It is not a guarantee, which is why we log every prompt and response and review them for missing guardrails and missing source documents."}},{"@type":"Question","name":"Do users need to create an account to use it?","acceptedAnswer":{"@type":"Answer","text":"No. The front end is a branded page with a single text box and no login. Not making people sign up somewhere else was one of the main reasons we did not use an off-the-shelf tool."}},{"@type":"Question","name":"What is the hardest part of building a document chatbot?","acceptedAnswer":{"@type":"Answer","text":"Not the code. It is deciding which documents belong in the index, keeping them current when the underlying guidance changes, and actually reading the prompt logs closely enough to learn something from them."}},{"@type":"Question","name":"Why log every prompt and response?","acceptedAnswer":{"@type":"Answer","text":"Because it turns into a list of where your documentation is unclear. Read a month of questions and the repeats show you exactly which guidance people cannot find or cannot follow. For this client that has been about as valuable as the chatbot itself."}}]}
Standard Co / Field Notes /
When the documentation becomes the bottleneck
The Standard Co Blog

When the documentation becomes the bottleneck

AP
Alex Pavluck
August 24, 2026
A field of faint document icons, three highlighted in cyan, feeding into a search bar and an answer card labelled answered from your own documents.

How we built a client a chatbot that actually knows their material.

The problem

A client came to us with a problem I have run into in a lot of places. They run an assessment program. Over the years that program has generated a pile of material: the instrument itself, supporting documentation, and pretty detailed guidance on what to do next depending on what your results look like.

None of it was missing. That was the problem. Staff and users were both spending real time hunting through documents for answers those documents already contained. Finding the right page had turned into its own skill.

What they wanted was easy to say and annoying to build. Ask a normal question, like our score on section 3 was low, what do we do, and get an answer that comes from their own material.

Why not just use an off-the-shelf tool?

Fair question. We asked it first. NotebookLM or a custom GPT gets you a version of this in an afternoon.

Three reasons we did not:

  1. They needed their own front door. A branded page they can send people to, without asking every user to make an account somewhere else.
  2. They wanted to own the questions. Prompts stored somewhere they control, so they can see what people are actually asking.
  3. Cost had to stay flat. No per-seat pricing that climbs as usage grows.

If none of those matter to you, use the off-the-shelf tool. Be honest with yourself about that before you start building anything.

What we built

Three pieces.

Pinecone Assistant does the heavy lifting. You upload the source documents and it handles chunking, indexing, retrieval, and generating an answer grounded in what it pulled back. That grounding is the whole ballgame. It is the difference between a chatbot that knows your material and one that improvises around it.

A front end on Vercel gives people a simple, branded place to type a question.

Neon, a serverless Postgres database, stores every prompt and every response. That log is ours, not buried in a vendor dashboard, which means we can categorize the questions and show the client what people are struggling with. Read it for a month and it stops being a log and starts being a list of where the documentation is unclear.

What about wrong answers?

No retrieval system is perfect, and a tool that gives guidance based on assessment findings should be treated accordingly.

Two things help. The assistant answers from the loaded documents rather than from general knowledge, which cuts down on invented answers. And because we log every prompt and response, we can see when we need more guardrails or a source document we forgot to load.

What it costs, honestly

You can prototype all three services on their free tiers. You cannot run a client deployment on them.

Vercel Hobby is non-commercial only, and their definition of commercial is broad enough to cover a consultant getting paid to write the code, so Pro at $20 a month is the floor for real work. Pinecone free Starter caps Assistant at 100 documents and 1.5 million input tokens a month, which a serious document set will blow through. Their Standard plan carries a $50 a month minimum. Neon free tier is genuinely fine at this scale.

Call it $75 to $100 a month to run, plus the build. For something that replaces hours of document-hunting every week, that math gets easy fast.

Doing this yourself

If you want to try it: open accounts on all three, load your documents into Pinecone Assistant, and wire up a simple front end. AI coding tools make the front end and the database plumbing far less work than they were two years ago.

The hard parts have not changed, and they are where these projects stall. Deciding which documents belong in the index. Keeping them current when the guidance changes. Actually reading the prompt logs closely enough to learn something from them.

That is the part we are good at. If you would rather not build it yourself, we will walk you through the setup, or run it for you.

The stack

Common questions

Can we just use NotebookLM or a custom GPT instead?

Often, yes. If you do not need a branded page of your own, do not care about seeing the question history, and are fine with per-seat pricing, an off-the-shelf tool gets you most of the way in an afternoon. We built custom because this client needed all three: a front door they own, a log of every question asked, and a flat monthly cost.

What does it cost to run a document chatbot like this?

Roughly $75 to $100 a month for the three services, plus the one-time build. That is Vercel Pro at $20 a month, a $50 a month minimum on Pinecone Standard, and Neon free tier, which is genuinely sufficient at this scale.

Can it run entirely on free tiers?

For a prototype, yes. For a client deployment, no. Vercel Hobby is non-commercial only and their definition of commercial covers a paid consultant writing the code. Pinecone free Starter caps Assistant at 100 documents and 1.5 million input tokens a month, which a real document set will exceed.

Do you need a separate vector database?

No. Pinecone Assistant handles chunking, embedding, indexing and retrieval for you, so you upload documents and query them. We use Neon for the prompt log, not for vectors.

What stops the chatbot from making things up?

Grounding. Pinecone Assistant answers from the documents you loaded rather than from general knowledge, so it is much less likely to invent something. It is not a guarantee, which is why we log every prompt and response and review them for missing guardrails and missing source documents.

Do users need to create an account to use it?

No. The front end is a branded page with a single text box and no login. Not making people sign up somewhere else was one of the main reasons we did not use an off-the-shelf tool.

What is the hardest part of building one of these?

Not the code. It is deciding which documents belong in the index, keeping them current when the underlying guidance changes, and actually reading the prompt logs closely enough to learn something from them.

Why log every prompt and response?

Because it turns into a list of where your documentation is unclear. Read a month of questions and the repeats show you exactly which guidance people cannot find or cannot follow. For this client that has been about as valuable as the chatbot itself.