A chatbot isn’t a gimmick or a generic ChatGPT wrapper. It’s an assistant built on your documents, your policies, your product - that answers in seconds, cites where it found the answer, and knows when to say “I don’t know.” The useful ones are grounded, tested, and deployed - not demoed once and forgotten.
The answer exists - somewhere
Policies, manuals, tickets, Confluence pages, PDFs. Your team knows the knowledge is there. Finding it in time is another story.
The same questions, every week
Your best people become human search engines - interrupted, repeating themselves, doing work that doesn’t need a human.
New people take months to catch up
Tribal knowledge lives in heads and scattered docs. Onboarding means asking around and hoping someone remembers.
Real stories
Education assistant
Education · 60,000 teachers
Before
Czech teachers needed teaching methods and materials buried across millions of articles and internal resources. Searching took longer than preparing the lesson.
After
An assistant that searches 8 million articles in seconds - with sources, in plain language. 3,000 schools adopted it. An estimated 11+ years of manual work saved for educators.
60,000 users
Health platform
Health · built with a physician
Before
A physician spent decades researching type 2 diabetes and obesity. The evidence existed - in papers, talks, and notebooks - but reached only the patients in her office.
After
A platform and AI assistant grounded in her research, helping far more people than one clinic ever could. Proof that the right bot isn’t about tech - it’s about getting knowledge to the people who need it.
Family project
That’s the pattern: knowledge your organization already has, delivered to the people who need it - without another meeting, another search, or another “who do I ask about this?” If that sounds like your problem, let’s talk.
What I build
Custom AI agents
Autonomous systems that plan, route, and execute - from internal workflow bots to customer-facing assistants with guardrails and evals built in.
Enterprise RAG
Retrieval pipelines at scale - hybrid dense-sparse search, chunking strategies, citation, and the eval loops that keep answers honest in production.
Data extraction
Web mining with Playwright, structured pipelines, and clean hand-offs into your knowledge base - the data layer your bot actually needs.
Who I work with
Teams with docs, tickets, or knowledge bases that nobody can search fast enough
Companies outgrowing a chatbot demo and needing something production-ready
Enterprises that need on-prem or air-gapped LLM options, not just SaaS wrappers
Typical engagement
Scoping call
30 min · free
You describe the problem, I ask the hard questions. We leave knowing whether I am the right fit and what a first slice could look like.
Prototype
2-4 weeks
A thin vertical slice with real retrieval, citation, and an eval harness - so we measure before we scale.
Production
Ongoing
Guardrails, deployment, monitoring, and judge loops. You get a bot that improves, not one that drifts.
Fixed price
Starter build
$999
One chatbot on your own content, or one marketing site of up to five pages - designed to match your brand and deployed on your domain. Fixed price, fixed scope, nothing to estimate and no proposal to wait for.
Anything bigger than this needs a scoping call first. If your brief turns out to be the wrong size for it, I will say so on the kickoff call and refund you rather than take the money.
Ratings
What people said afterwards
5.0
3 ratings
5 stars100%
4 stars0%
3 stars0%
2 stars0%
1 star0%
—
Anonymous
Deep dive
She helped me analyze my company’s chatbot, which had been quite unreliable and often returned random answers in different scenarios. After reviewing both the chatbot architecture and my source code, she identified some fundamental design issues and suggested changes that made a significant difference in fixing the problems.
I was very happy with the quality of her analysis and the practical recommendations. I’m even considering upgrading to her $999 service for a complete chatbot redesign.
TH
Tetouani Hamid
Quick question
Quick response, and she also helped me with a few follow-up questions.
—
Anonymous
Quick question
Responded within 10 minutes and kindly answered all my questions. Great service.
Left by people who paid for an answer, through a link sent after they got it. I choose which ones go up, and I do not edit them.
How I work
01
Scoping
What should the bot know, who uses it, what does "good" look like? I define evals before writing prompts.
02
Build
RAG pipeline, agent orchestration, guardrails - your stack (.NET or Python), your data.
03
Ship
Deployment, monitoring, judge loops in production. You get a bot that improves, not one that drifts.
FAQ
RAG or fine-tuning - which do I need?
Usually RAG first. If your bot needs to know your content, retrieval beats retraining every time a doc changes.
How do you stop the bot from making things up?
Ground every answer in retrieved passages, teach explicit "I don't know" fallbacks, and evaluate before you ship.
Can you work on-prem or with our own LLMs?
Yes - I have shipped on-prem LLM infrastructure in production. We can scope air-gapped or hybrid setups from day one.
What do you need from us to start?
Sample documents or data sources, a stakeholder who knows the use case, and clarity on who the users are. I handle architecture through deployment.
How long until something is live?
A working prototype in 2-4 weeks is typical. Production depends on scope, integrations, and how messy the source data is.
I only have one question, not a project.
Then buy a written answer instead of booking a call. Fixed price, fixed deadline, refunded if I am not the right person to answer it. Read more