A support bot, a helpdesk bot and a sales assistant share the same retrieval core, but differ in content, permissions and what counts as a good answer.
A chatbot tends to pay off when the answers already exist in writing and people still have to ask for them.
A large share of tickets is answered by pasting a link to an article that already exists.
The help center has the answer, but search returns a list of pages and people give up.
Senior people answer the same process and policy questions in Slack or Teams every week.
Reps can't answer a technical or pricing question on a call without pinging an engineer.
Your decision-tree bot handles a handful of flows and sends everything else to a form.
You tried a model with a prompt and no retrieval, and it made things up in front of customers.
We connect your help center, wiki, PDFs and ticket history, remove duplicates and outdated pages, and split the rest into passages the bot can retrieve.
Keyword and vector search combined, with reranking where it helps, tested against real questions your customers or staff have asked.
Each reply is written from the retrieved passages and links to the articles it used, so anyone can open the source and check.
When retrieval finds nothing relevant, the bot says it has no answer and offers the next step. It does not fill the gap with a guess.
Rules for when to escalate, and a transfer to your help desk or live chat that carries the conversation and the sources the bot looked at.
A website widget, an assistant inside your web or mobile app, a Slack or Teams bot, all served by one backend and one knowledge base.
Retrieval is filtered by what the user is allowed to see. We test the bot against prompt injection and attempts to pull out other people's data.
A test set of real questions that runs on every change, plus a view of unanswered questions, escalations and feedback from live conversations.
Three decisions the bot makes on every message. Each one is designed, tested and visible in logs.
Rewrites the question using the conversation so far
Searches only content this user may see
Prefers current pages over archived ones
Checks that the passages actually cover the question
Asks a clarifying question when the request is vague
Declines topics you have ruled out, such as legal advice
Replies from the passages and cites them
Says it doesn't know when the content is missing
Transfers to a person with the full transcript
A Python backend for retrieval and generation, React and TypeScript for the widget and admin screens. The model provider and the vector store are chosen with you per project: a hosted model or one you run yourself, vector search inside PostgreSQL or a dedicated vector database.
Python
FastAPI
PostgreSQL
TypeScript
React
Node.js
AWS
DockerYou want fewer repeat tickets without customers getting stuck in a bot that won't let them through.
You want an assistant inside the product that knows your docs and the user's account.
You answer the same internal questions every week and the wiki isn't solving it.
You need accurate product answers during a call, taken from approved material.
Find out if your content can support one, and what it would take.
Keep the flows that work, add answers from your content.
Add retrieval, citations and refusal rules to a bot that guesses.
Bring a working website bot into your app, Slack or Teams.
Handoff, ticket creation and account lookups through your APIs.
Answers that respect who may read which document.
We collect real questions from tickets and chat logs, review the content that should answer them, and list the gaps where no answer is written down.
We agree on a set of questions with expected answers, then build a working bot on a slice of your content and measure it against that set.
Full ingestion, retrieval, citations, refusal and handoff rules, the channel clients and the integration with your help desk or CRM.
A limited release to your own team or a share of traffic. We read the transcripts with you and fix what the test set missed.
Rollout to everyone, with content sync on a schedule and the test set run before each change to prompts, models or content structure.
2-week fixed-price start
Fixed price.
For teams that want to see the bot answer their own questions before committing to a full build.
Review of your content and its gaps
Test set built from real questions
Prototype bot on a slice of your docs
Measured results and failure examples
Scope and estimate for the full build
Engineers on your chatbot from build to launch
Monthly, team-based.
For a production chatbot with several channels, integrations and ongoing improvement after launch.
Python and AI engineers
Frontend and mobile developers for the clients
QA that tests answers, not only screens
Regular demos with transcript reviews
Team size adjusted as scope changes
An AI-powered payroll validation platform that extracts data from salary slips, verifies payroll calculations, and automates reporting and approval workflows for HR and finance teams
An AI-powered fashion commerce platform that helps retailers deliver personalized shopping experiences through virtual styling, AI recommendations, image search, and intelligent product discovery.
Verified reviews from Clutch. The products differ, the way we work does not.
“They were incredibly fast and efficient at scoping the project and then delivered within very tight deadlines.”
“I was impressed with how quickly we got a stable and well-designed product.”
“The team met all commitments, aligning with our company's objectives.”
A chatbot is a product, not a model call: clients on several channels, a backend, integrations and content that changes every week. We build all of those parts in one team.
We build web, mobile and backend products and the AI features inside them, so the widget, the API and the retrieval come from one team.
On an AI payroll platform we built, data extraction from salary slips with automated validation led to 17% fewer salary calculation errors.
70+ products launched for startups, SMBs and product companies. 85% of clients come back.
Rated 5.0 on Clutch from 5 verified reviews.
Projects can start in about two weeks.
You see results on a test set of your own questions, not a demo on hand-picked ones.
We design and test what the bot does when it has no answer, and treat a wrong answer as a bug.
The path to a human is part of the first version, not something added after complaints.
If your docs can't support a bot yet, we tell you which articles to write first.
Our team in Europe overlaps with part of the US working day.
No sales pitch
Tell us what your chatbot should answer
Describe who will ask, where your content lives and which channel you need first. We'll reply with an approach and a quote. If your content isn't ready for a bot, we'll tell you what to fix first.
By subscribing you agree to receive emails from Gilzor. You can unsubscribe at any time. Privacy Policy.
