How is this different from ChatGPT or Claude?

ChatGPT and Claude do not know anything about your business. Knowledge Agent is grounded in your actual documentation and only answers from what is in your corpus. It will say "I do not know" instead of making something up.

What happens when the docs are wrong or outdated?

The agent surfaces stale answers in the dashboard so you can fix the doc, not the prompt. Most teams find this is the single biggest reason their support tickets drop: it forces them to clean up the wiki they were ignoring.

Can it answer questions about pricing or sensitive data?

Yes, but with rails. We can scope what the agent has access to, redact sensitive fields, and add a guardrail layer (AWS Bedrock Guardrails or similar) that blocks unauthorized topics. Common pattern: agent answers product questions, escalates pricing to sales.

Production RAG chatbots running on Claude or GPT typically hit 85% to 95% accuracy on questions answerable from the corpus. We tune the confidence threshold so anything below 70% certainty escalates to a human. You see the numbers in the dashboard.

What if my docs are in 12 different systems?

Common case. We ingest from all of them: Notion, Confluence, Google Drive, Zendesk, GitHub, your DB, PDFs. The agent treats them as a unified corpus and cites which source each answer came from.

Can I see what is being asked?

Yes. Admin dashboard shows every question, the answer given, the confidence score, whether it escalated, and what the user thought of the answer. This is the most underrated feature: it tells you exactly what your customers are confused about.

All agents

Chat & RAG

60% support tickets dropped in month one

Knowledge Agent

Cut support tickets 50% in 30 days using your existing docs.

85-95%Accuracy on in-corpus questions

Sub-2sResponse time

100%Answers cite source documents

Most chatbots hallucinate because they are trying to answer questions they have no information about. Knowledge Agent is different: it is grounded in your actual documentation via RAG (retrieval-augmented generation), cites every answer, and routes anything it is not sure about to a human.

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

Live in 21 daysLive or first month freeYou own the code

Start this buildSee full pricing

Default tier

Production Build

$12,000+ $1,500/mo

one-time + monthly operate · 21-day delivery

Starter Build fits a single doc source (e.g. just your Notion wiki). Production is the default when you want to combine docs + tickets + code + PDFs.

Guarantee

Live in 21 days or your first month of operate is free.

Start this buildCompare all 3 tiers →

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 self-qualify before booking a call.

Ideal fit

SaaS companies whose support team answers the same 50 questions every day
Service businesses with internal SOPs that nobody can remember the location of
Teams sitting on a Notion or Confluence wiki that nobody actually reads

Not a fit if

Companies with zero existing documentation: a RAG agent is only as good as its corpus
Use cases that require live database lookups instead of doc retrieval (use Operations Autopilot)
Teams that need outbound, proactive answers rather than reactive Q&A

What is in the box?

Production-grade. Nothing left for you to figure out.

Every Knowledge Agent engagement ships with these components. No phase-two surprises. No upsells once the contract is signed.

Vector database stood up (Pinecone, Qdrant, or AWS S3 Vectors depending on your scale)

Embedding pipeline that ingests your docs, tickets, code, and PDFs and stays in sync

RAG chatbot with citation links back to the source document for every answer

Confidence scoring with auto-escalation to a human (Slack, ticket, or live chat) below threshold

Embed code for your site, a Slack bot, or a standalone widget (your choice)

Admin dashboard: what was asked, what was answered, what got flagged, what needs new docs

Re-embedding on doc updates (manual trigger or automatic on webhook)

30 days of monitoring and prompt tuning

How does the build actually run?

Four phases. Three weeks. One engineer.

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

Discovery and corpus audit (Week 1)

I review your existing documentation. Identify gaps. Flag which docs are out of date. You get a written "knowledge health" report before we build anything.

Ingest and embed (Week 2)

Documents chunked, embedded, indexed. Initial chatbot stood up. You ask it real questions, push back on bad answers, we tune retrieval.

Production rollout (Week 3)

Embed code or Slack bot deployed to a subset of users. Real questions. Real escalations. We tune confidence thresholds based on actual data.

Full launch and handoff

Chatbot live for everyone. I monitor and tune for 30 days. You get a dashboard and a runbook so your team can add new docs 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. RAG wired to existing docs.

Read the full case study

How does the math compare to hiring?

The unit economics, plainly.

Hiring a human

L1 support engineer or CS rep at $50K to $80K/yr answering tickets that already have answers in the docs.

Hiring this agent

$12,000 once + $1,500/mo. Answers in 2 seconds. Cites the source. Escalates the hard ones.

Real outcome: B2B SaaS platform cut 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 cost.

Start the conversationSee the work