1. Scoping the task
You describe the problem — I identify the system constraints and turn them into technical requirements.
Web application and automation systems development in Bishkek
Sites and services that handle the routine for you: pull data from other websites, prepare documents, crunch the numbers and send you the finished result.
How I approach an automation project
2. Designing the agent architecture
I design how modules and data flows interact. No code yet, just clean logic.
3. Building and testing
I get to a working version faster than building from scratch by hand — modern tools speed up the draft code. Debugging, testing hypotheses and the final decision are always mine; I don't hand responsibility over to automation.
4. Handover and documentation
You get a finished solution with an architecture diagram and clear instructions. Timeline depends on complexity: from 1 week to 3 months.
Every commit, rollback and fix is my personal responsibility. Quality isn't delegated to AI.
A few examples of what's possible
These are just a few examples, not the full list. If your task doesn't look like any of them, describe it via the , and we'll work through it individually.
A sales site with a catalog and lead capture
A site with product cards, prices and text blocks you edit yourself, no need to contact me or touch the code. Leads from the contact form land straight in your Telegram and on your phone, and in the admin panel you can assign them between staff and set tasks for each other, so nothing gets lost or forgotten.
Automated data collection and processing
Every day the site visits the pages you need on its own, pulls fresh data, cleans it up and shows it in a clear table or dashboard, no manual work required.
Collector
затемQueue
затемProcessor
затемStorage
затемDashboard
Document and report generation
Dynamic generation of contracts, acts and commercial proposals from templates and external data.
Template engine
иAccounting system API
затемFinal PDF/Excel
AI agents for client communication
AI agents that handle chat and social media inquiries: intent recognition, translation, auto-replies, lead routing.
Intake
затемNLP layer
затемScenario
затемHandoff to a human
Monitoring and predictive analytics systems
Tracking the metrics that matter to you — traffic, service response, campaign progress — with automatic highlighting of deviations from the norm.
Data connector
затемAnalytics engine
затемDeviation rule
затемNotification
Online store with its own warehouse
Product catalog, stock tracking, order and delivery statuses — all in one place, no manual duplication in Excel. I connect payments through a certified acquiring bank's ready-made gateway rather than building my own: storing and protecting card data requires a PCI DSS license, which by law only the receiving bank holds. That's not a limitation on my part — it's simply how secure payment is supposed to work.
What the architectural approach looks like
A hypothetical case: a rental listings aggregator wants to automatically check which listings have disappeared from other platforms and update its own database. (This isn't a real project, just an illustration of the method.)
This example doesn't reveal any real project, but it shows the way of thinking: the task is broken into modules, and AI speeds up the most labor-intensive step — matching the data.
# Solution architecture (excerpt)
Collector (Scheduler)
→ Data collection from external platforms (Headless + proxy)
→ Data cleanup and normalization
→ AI listing matching (semantic model)
→ Analytics layer (comparison, changes)
→ Control panel (green — new, red — gone)How it's built in practice
Isolation. Every project runs separately — nothing overlaps or breaks a neighboring task.
Server with backups. A dedicated VPS with backups, not cheap shared hosting with no control over your data.
Attack protection. SSL and baseline DDoS protection via Cloudflare — in 2026 that's the standard, not an option.
Mail delivery. Emails reach the recipient even if the provider has sending blocked.
Server housekeeping. Scheduled background jobs keep things from silently filling up with logs and junk over time.
And there have been quite a few such projects: I've already built 5 fairly complex, business-oriented web systems comprising 15–20 custom-architecture modules — from sites that track information on their own and prepare documents, to systems with their own network architecture and AI agents. I don't disclose details of specific projects — that's NDA.
How automation pricing breaks down
A sales site with a catalog, lead capture and a team admin panel — as an example:
$600 — turnkey base, can run right on your own PC with no server
+ cost of a suitable VPS
+ cost of the domain you need
Sometimes the domain you want is already taken and sitting on an auction for unreasonable money — in that case we figure it out together: a different name, or an honest estimate of what exactly what you need will cost.
For example: one click pulls a product from your ERP system via API and gives you a ready Excel file in the structure you need. Runs locally — via Docker or a simple script autorun, no interface or design, just the result. Since the server doesn't need to run all the time, a module like this starts at $200 turnkey.
If the module needs to run on its own — say, pulling data on a schedule instead of on a button press — it needs a server running continuously. That option starts at $300–400 turnkey: the price goes up because a year of server upkeep becomes part of the cost, but in return you get your own interface where you can download or view the finished report from anywhere. A domain isn't always necessary — if you don't need the system to be found through search, an IP address with basic protection (login and password) is enough.
Service-level automation and complex AI systems I quote individually after a free breakdown of the task — there are too many components and integrations to give an honest number upfront.
Describe the task, I'll describe the architecture
A free architectural breakdown, no strings attached. You'll get a clear picture of the solution and a realistic timeline estimate — even if you don't end up working with me.
Takes a couple of minutes — then we decide if a call is needed
What you get from the breakdown
A diagram of which modules are needed and how they connect, an honest risk assessment (where data collection from someone else's site might break, where a third-party service might fail), and a concrete stack — without generic just-in-case recommendations.