How is this different from ChatGPT or Claude?

ChatGPT and Claude do not know anything about your business. This chatbot is grounded in your actual documentation and only answers from what is in your content. It will say 'I do not know' instead of making something up.

What happens if our docs are wrong or out of date?

The dashboard surfaces stale answers so you can fix the doc, not the prompt. Most teams find this is the biggest reason their support tickets drop — it forces a wiki cleanup they were ignoring.

Can it handle sensitive data like pricing?

Yes, with limits. We can scope what the agent has access to, hide certain fields, and block off-limits topics. Common setup: agent answers product questions, escalates pricing to a human.

How accurate is it really?

On questions that have answers in your docs, a good production setup hits 85-95% accuracy. We tune the confidence threshold so anything under that escalates to a human. You see the numbers in the dashboard.

What if our docs are scattered across 12 different tools?

Normal case. We pull from all of them — Notion, Confluence, Google Drive, your help desk, GitHub, PDFs. The agent treats them as one searchable library and tells you which source each answer came from.

How much does it cost?

Every project is custom. Tell me how much documentation you have and how many people will use it — I will give you a real number on a call.

All agents

Chat & RAG

60% support tickets dropped in month one

Knowledge Agent

A chatbot that actually knows your business.

85-95%Accuracy on questions in your docs

Under 2sTime to first answer

100%Answers come with a source link

Most chatbots make things up because they have no idea what your business actually does. This one is grounded in your real documents. It cites every answer so people can check it, and it knows when to hand off.

Built by Jahanzaib Ahmed, AI Systems Engineer·Updated May 23, 2026

Live in 21 days21-day live guaranteeYou own the code

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What it actually does

Plain English. No jargon.

Here is exactly what happens after the agent is live. If any of this is unclear, ask me — I would rather over-explain than have you guess.

Reads your existing help docs, support emails, FAQs, and internal SOPs

Answers questions in plain English with a link to the source document

Asks for human help when it is not confident in the answer

Tells you what people keep asking so you can fix gaps in your docs

Embeds anywhere — on your site, in Slack, on WhatsApp, or as a standalone link

Who is this for?

Built for the people who already know what is broken.

I would rather lose a deal than take on a project that is not a fit. Honest fit signals below so you can decide before we even get on a call.

Good fit if

SaaS companies whose support team answers the same 50 questions every day.
Service businesses with internal procedures nobody can find when they need them.
Teams sitting on a Notion or Confluence wiki nobody actually reads.

Not a fit if

Businesses without any documentation. The chatbot is only as good as what you give it.
Use cases that need live database lookups instead of doc retrieval — Operations Autopilot fits better.
Teams that need outbound, proactive messaging. This one is reactive: you ask, it answers.

What is in the box?

Everything you need. Nothing left for you to figure out.

No phase-two surprises. No upsells after the contract is signed. This is what every Knowledge Agent engagement ships with.

A document indexing pipeline that ingests your docs, tickets, and PDFs

A chatbot that answers from your real content and links to the source

A confidence score so it asks for human help when it is not sure

Embed code for your site, a Slack bot, or a standalone widget — your pick

An admin dashboard: what got asked, what got answered, what got flagged

Auto-refresh when your docs change (manual or on a schedule)

30 days of tuning after launch

How does the build actually run?

Four phases. About three weeks. One engineer.

I do not disappear and surface with a demo. You see progress every day. You sign off at each phase. If something is wrong, we catch it before it ships.

Week 1: I audit your knowledge

I look at your existing documentation. I flag what is out of date. You get a written health report before we build anything.

Week 2: I ingest and stand up the chatbot

Your documents get indexed. A working chatbot goes live in staging. You ask it real questions, push back on bad answers, we tune.

Week 3: Soft rollout

Embedded for a subset of users. Real questions. Real escalations. We tune the confidence threshold based on actual data.

Full launch and 30-day tune

Live for everyone. I monitor and tune for 30 days. You get a dashboard and a guide so your team can add new documents without my involvement.

Real client. Real outcome.

What does this look like in the wild?

Case study

B2B SaaS: support tickets dropped 60% in month one. The chatbot reads the same docs the team does.

Read the full case study

How does this compare to hiring?

The honest comparison.

Hiring a human

A support rep answering tickets that already have answers in the docs.

Hiring this agent

An AI answers in two seconds, cites the doc, and escalates the hard ones to a human.

Real outcome: One B2B SaaS dropped support tickets 60% in month one. Same team, same docs, agent on top.

The questions everyone asks

Knowledge Agent FAQ

Go deeper

Not quite the right fit?

Voice

Ready to ship?

Tell me what you are trying to fix. Twenty minutes on a call. I will tell you in plain English whether Knowledge Agent is the right fit and what it will look like.

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