They say you can only pick two. We build working software — integrations, AI agents, internal tools and MVPs — in days, for a fixed price. And you talk directly to the developers who write the code.
Four small working demos of what we build. Click around: everything here runs right in your browser.
It answers customers, checks stock, reserves items and passes the conversation to a person when needed. Your CRM updates itself as the chat goes.
AI agent for WhatsApp · about 7 days →Demo data. Real projects connect to your systems.
Fast, good, cheap — pick two, says the old rule. It holds when every project starts from zero. Ours don't.
Sign-in, admin panels, payments, dashboards, bots, AI assistants — we've built them for our own products and keep them as ready blocks. We write only what's unique to you.
The same blocks run live products with real users and real money. Every build goes through one pipeline, from requirements to QA and release.
No hourly billing and no layers of managers: a small team of developers with AI inside the workflow. You get the price in writing before any work begins.
Pick any two — the third one switches off.
We take tasks of any size and send each down one of two paths. Simple things we generate on our own production line; complex ones we build ourselves.
Landing pages, simple bots and standard services come off our production line, assembled from ready-made blocks. You see the result first and buy it if it fits — or take it on a subscription.
Integrations, AI agents, MVPs and internal tools are built by our developers working with AI. The price is fixed from the spec before we start.
Each with a clear scope and timeline. Need something else? Describe the task and we'll scope it.
An assistant that answers customers, qualifies leads and hands conversations over to your team — on your own number and WhatsApp Business account.
Connect the systems you already use — marketplaces, payments, CRM, accounting, messengers — and let routine run by itself.
A working product with sign-in, roles, an admin panel, payments and dashboards — ready for your first users or your team.
Let AI assistants such as Claude and ChatGPT work with your product through a secure MCP server.
Every format comes with a fixed price and a written scope.
Not a list of technologies — skills, each already running in our own products. Any of them can move into yours.
We refresh data in about a second with zero downtime. Heavy recalculation runs in the background, closed periods are never recomputed, and the new version of the data replaces the old one instantly — reports never break mid-load. Analysts edit formulas in SQL right in the interface, no release needed.
DuckDB · Parquet · cache keyed by data version
Built in:BI platformQuanta
We don't retrain the model. We give it live data through the same functions that build the screens, a knowledge base and answer rules — so the numbers in its answers match the report. Every miss becomes a rule and a test. That's how we taught the assistant in our BI platform.
tools · knowledge base · quality log
Built in:BI platformQuanta
We connect AI to every function of your product through an MCP server. The assistant doesn't just answer — it acts: changes prices and stock, logs expenses, builds reports. Writes happen only after a human confirms, and actions are logged.
MCP · roles and permissions · action log
Built in:QuantaBI platform
Competitor prices, sellers, reviews and catalogs collect themselves — on schedule, no manual monitoring. Request limits and auto-pause on failures keep collection steady without babysitting, while price history is stored and queried in seconds.
Python · queues · Parquet + DuckDB
Built in:Quanta
For security and fault tolerance, we test every process: sign-in and permissions, payments, data exchange, AI answers, injection and leak protection. Tests run on every change, and the live site checks itself on a schedule — we catch failures before your customers do. Releases are verified and roll back on their own; a manual rollback takes seconds.
pytest · Playwright · GitHub Actions · GitLab CI
Built in:QuantaBI platform
Notifications with action buttons: accept an order right from the chat. PDF documents, sign-in codes and an AI chat that respects staff permissions. And a Mini App is a whole dashboard inside Telegram: orders, products, prices and team, no password needed.
Bot API · Mini Apps · Telegram sign-in
Built in:Quanta
AI drafts replies to customers in WhatsApp, leads and conversations land in the CRM, voice messages get transcribed. Summaries arrive in Teams and Jira tasks are linked to KPIs, so the team doesn't juggle windows.
WhatsApp Business API · CRM · Teams · Jira
Built in:QuantaBI platform
Orders, waybills, prices and stock through the marketplace API — and prices adjust themselves by the strategy you pick. Plus an online storefront with QR and in-app invoice payments, and subscriptions with auto-renewal.
REST API · signed webhooks · queues
Built in:Quanta
We set up employee KPIs across the whole hierarchy, points-based motivation with plans and rankings, fraud detection and HR exports to Excel and PowerPoint — all already running in our BI platform. Next up is a standalone HR platform: bonuses by transparent rules, assessment, training and employee profiles on one data model.
SQL methodology · anomaly detection · Excel and PowerPoint
Built in:BI platform
Running in the BI platform
Designing now
We build our own products first — that's where the blocks and the pipeline come from.
Automation for sellers on Kaspi.kz
Kaspi.kz is Kazakhstan's largest marketplace. Quanta gives its sellers one workspace: orders and waybills, automatic repricing against competitors, marketing analytics, an online storefront with Kaspi Pay, Telegram and WhatsApp notifications — and an AI assistant that answers questions about the business and can act on them.
Interface in Russian — the product serves sellers in Kazakhstan. Demo data.
Preparation for IT certifications
Question banks with explanations, timed mock exams, an AI tutor and an in-browser SQL sandbox for 18 certifications from Microsoft, AWS, Google, Databricks, Tableau and Salesforce. The first product assembled on our block factory.
For a large company: dashboards, KPIs and employee motivation
An in-house platform that replaced Power BI reports. Managers track SLAs and KPIs by day, week, month, quarter and year across the whole hierarchy, and HR runs employee motivation in it. Analysts edit formulas in SQL right in the interface — no release needed — and data refreshes in about a second with zero downtime.
data as of 09:41
SELECT division, 100 * AVG( CASE WHEN hours_to_close <= 24 THEN 1 ELSE 0 END) AS slaFROM requestsWHERE created_at >= :period_startGROUP BY division;+340% in a week while peers grew +7%. Points are under review and stay out of the ranking.
Ask the assistant
Demo on made-up data — the client's name and data stay private.
Plus 20+ smaller projects: Telegram bots, MCP servers, AI tools and games.
MVP Studio is our production line. An AI-assisted pipeline takes each project from requirements to release, assembles it from tested blocks and writes only what's missing. Every good new part goes back into the catalog — so each project makes the next one faster.
You describe the task, we ask the questions that matter. Thirty minutes is usually enough.
Scope, timeline and price in writing. The price doesn't change unless the scope does.
You follow progress on a working demo, not in status reports.
The code lives in your repository — deployed, documented and yours.
You do. We work in your repository or hand ours over, and everything we write for the project belongs to you.
Most of any project is made of parts that have been built many times: sign-in, admin panels, payments, dashboards, bots. We keep them as tested blocks and use AI inside our workflow, so the time goes only into what's unique to you.
We estimate the change and agree on it before doing it. No surprise invoices.
Yes. Integrations and automation are exactly about connecting what you already have.
No. Fine-tuning is expensive and goes stale with the first change in your processes. We give the model live data through tools, a knowledge base and answer rules, and turn every miss into a rule and a test. Behavior changes in a day, and the model can run inside your own infrastructure.
TypeScript, React and Next.js; Python and FastAPI; PostgreSQL; Docker. For AI — Claude and other large language models, plus MCP.
In Kazakhstan (UTC+5). We work remotely, in English and Russian.
A few lines about the task are enough. Within 24 hours you'll get the scope, the timeline and a fixed price.
Contacts are on their way