Putting AI to work in your business does not require rebuilding anything. Innosoft connects an AI layer to the CRM, 1C, warehouse, document flow and Telegram channels you already use, so orders and documents are processed automatically instead of by hand. Pricing is 10–35 mln UZS and delivery takes 20–45 days — starting with an audit and a pilot, not a signature on a large build.
Updated: July 2026 · Written by the Innosoft engineering team, Tashkent
AI integration is the process of adding an artificial intelligence layer to the systems a company already operates — CRM, 1C, ERP, warehouse, document flow, Telegram. No new application is built from scratch: your existing data and workflow stay exactly where they are, while the AI takes over the repetitive work of data entry, classification, replying and forecasting.
Most of the work in an integration is finding the right connection point and getting the data flow right. Your CRM, your accounting system and your team's habits all stay put. That is why the change your staff experience is minimal, and why going live is faster than building a new system.
You already hold years of order, customer and document history. That history is exactly what the AI feeds on: forecasting, classification and automated replies all run on top of it. A system built from zero would have none of it on day one.
This page is about connecting AI to systems you already run. If you have no system yet, or what you want is a standalone AI agent, chatbot or virtual assistant built from scratch to talk to your customers, that is a different service with its own pricing and timeline.
The table below covers the six directions clients ask for most often when they want business processes automated with AI. Each row states which system is connected, what the AI actually does inside it, and what changes as a result.
| System | What AI does | Result |
|---|---|---|
| CRM (Bitrix24, AmoCRM, in-house) | Lead scoring, automatic enquiry categorisation, drafting the first reply, writing a conversation summary into the record | Sales stops spending its day on cold leads, and first-response time drops |
| 1C and accounting systems (REGOS and similar) | Recognising documents and orders, filling fields automatically, flagging mismatches for review | Manual data entry — and the errors it produces — falls away sharply |
| Warehouse and logistics | Stock monitoring, demand forecasting, automatic alerts when a critical reserve is reached | Stock-outs and overstock shrink at the same time |
| Telegram and social channels | Understanding an order written in plain language, confirming it, calculating the total and pushing it into your system | Order intake runs 24/7, and operators only handle the exceptions |
| Document flow | OCR over scans and PDFs, classifying each document by type, routing it to the right owner | The manual work of finding and forwarding documents disappears |
| Analytics and reporting | Sales forecasting, anomaly detection, automatic written summaries and dashboards | Management stops waiting for a report and gets an answer to the actual question |
If order intake on Telegram is what you need as a standalone product, see Telegram bot development. If there is no CRM in place at all, the sensible first step is CRM development.
The four projects below are live systems from the Innosoft portfolio. In each one, the AI or automation layer was attached to an existing business flow rather than sold as a separate product built in isolation.
Restaurants, cafes, hotels · order → accounting → courier
The AI agent reads an order written in plain language on Telegram — "50kg chicken, 20kg beef for tomorrow" — confirms it and calculates the total in under three seconds. The integration is the important part: the order is passed into the client's accounting system and linked to courier delivery, so every order is tracked as one chain from intake to hand-off.
Food delivery · delivered in 1 month
Chefs post their orders in Telegram groups; the AI agent picks them up, confirms them, and transfers them into the central accounting system through the REGOS API. Suppliers are notified in real time when warehouse stock runs low. This is the clearest example of adding AI to an accounting system without replacing it.
HomeShop & Million Market · 8-role SaaS
One platform combining a customer-facing store, an 8-role admin panel (admin, manager, operator, warehouse, courier, seller, ad manager), Telegram autoposting and advertising analytics. Orders are routed automatically through operator → warehouse → courier, and the seller's balance is credited on delivery. Payme, Click and Uzum payments are integrated.
Online store · delivered in 1.5 months
Products, orders, customers and reporting brought together in a single admin panel. A CRM organised this way is a ready foundation for AI integration: because the data sits in one place and is properly structured, adding lead scoring or sales forecasting later is a short project rather than a rebuild.
Price depends on how many systems are connected, how they connect, and how complex the AI task is. These are our current packages. After the audit you receive a fixed sum and a fixed timeline in a written proposal.
One system, one process
Businesses running a CRM plus a knowledge base
Companies where 1C/ERP, warehouse and CRM work together
We never open with a large integration. First the audit and a single entry point, then a pilot tested on real data — and only then the full build. That sequence is what stops a budget from being spent on an assumption.
We inventory the systems you run, draw how data actually moves between them, and count the repetitive tasks still done by hand. Whether an API, a webhook or database access exists is confirmed at this stage, not assumed later.
AI does not belong everywhere. The process that burns the most manual hours and produces the most errors goes first, and it gets a written success criterion before a single line of code is committed.
The chosen process runs on real data at limited volume. We measure accuracy and response time. If the pilot misses its criterion, the approach changes — and the large budget was never spent.
The AI layer is wired into your CRM, 1C, warehouse or Telegram through APIs and webhooks. Failure states, retries and operator fallback are written explicitly rather than left to chance.
The team learns the new flow: what changed, where the AI decides on its own, and when a human steps in. Written instructions and video walkthroughs are handed over.
After launch we watch the numbers and keep tuning prompts and model settings. Once the first process runs reliably, the next one joins the integration.
The answer to "is my system compatible?" is simple: if there is at least one channel for getting data out or writing data back, it is compatible. In practice we use one of the six routes below.
The most common route: two-way data exchange through your system's documented API, authenticated with a token and scoped to what the AI actually needs.
When an event happens — a new order, a status change — your system notifies us. Real time, without polling the API every few seconds.
We work with 1C, REGOS and comparable accounting platforms through their APIs or exchange files, keeping documents and orders synchronised both ways.
Order intake, confirmations and notifications through groups and private chats — in Uzbekistan this is consistently the fastest channel to get live.
When there is no API: a read-only replica or a restricted-privilege connection. Your production database load is left untouched.
For closed or legacy systems: scheduled exports (CSV, XML, Excel) are processed by the AI and the results are written back on the same schedule.
Not every business needs AI integration. The table below compares the three options honestly — pick the column that describes your situation.
| Criterion | AI integration | New system / agent from scratch | Keep doing it manually |
|---|---|---|---|
| Starting point | The system you already run stays in place | A new product is built from zero | Everything stays manual |
| Price | 10–35 mln UZS | 5–25 mln UZS for an agent, more for a full platform | 0 UZS, but manual workload keeps growing |
| Timeline | 20–45 days | 10–45 days, often longer | — |
| Change for your staff | Minimal — it runs inside the same interface | A new system has to be learned | None |
| Historical data | Your existing history is used immediately | Requires migration or starts empty | Stays scattered across tools |
| When it is the right call | You have a system, it is decent, but too much of it is manual | There is no system, or the one you have is obsolete | The process volume is genuinely small |
The third column can be the correct answer too: at a handful of orders a day, automation will not pay for itself. We say so during the audit rather than after the invoice.
The questions clients ask us most often about integrating artificial intelligence, answered directly.
AI integration means adding an artificial intelligence layer to the systems you already run — your CRM, 1C, ERP, warehouse, Telegram groups, or website. Nothing is rebuilt from scratch; an existing process is upgraded. If what you actually need is a new chatbot or assistant that does not exist yet, that is a different service: see our AI agent development page.
If it exposes an API, a webhook, or even just database access, it is a fit. In practice we have worked with 1C, Bitrix24, AmoCRM, REGOS, in-house admin panels, Excel and Google Sheets bookkeeping, and plain Telegram groups. For closed systems we connect through scheduled export files or an intermediate database. We confirm all of this during the audit, at no cost.
Basic integration starts at 10 mln UZS — one system, one process, one channel (website or Telegram). Extended integration, which adds CRM connectivity, a company knowledge base, and voice queries, is 20 mln UZS. Enterprise integration across several systems with custom APIs and hardened security is 35 mln UZS. The AI analytics and forecasting module is priced separately, from 15 mln UZS.
Basic integration takes 20–25 days, extended 30–35 days, and enterprise 40–45 days. The main variable is how quickly your side provides API documentation and access to a test environment. The pilot — one process running on real data — is usually live within 7–10 days, so you see evidence long before the full rollout.
You choose the architecture. When sensitive data is involved, only the minimum fragment needed for the task is sent to the model, stripped of personal identifiers; the business logic and the database stay on your servers. In our banking deployment the agent is built so it never asks for a PIN, CVV, or OTP at all. We sign an NDA before the project starts whenever you need one.
Usually not. The AI layer runs inside the interface your team already uses — the same CRM record, the same Telegram group, the same admin panel. New buttons and auto-filled fields appear, but the workflow itself does not change. Every project includes team training and a short set of video walkthroughs.
Four things: how many systems are connected, how they connect (a documented API is cheap, a closed or legacy system is expensive), how complex the AI task is (drafting replies versus building a forecasting model), and the expected load. After the audit you receive a fixed price and timeline in a written proposal — it does not move mid-project.
That is precisely why the pilot comes before full integration: we run one process on real data and measure accuracy against a written success criterion. If the pilot misses the target, we change the approach or stop the project — before a large budget is committed. After launch, monitoring and correction are part of support.
Yes. We work with 1C, REGOS, and similar accounting platforms — the agent that pushes Telegram orders into a client's accounting system through the REGOS API is a live project of ours. The AI reads documents and orders, writes them into the right fields, and raises an alert automatically when warehouse stock drops below a threshold.
Yes. Depending on the tier you get 3, 6, or 12 months of technical support: monitoring, bug fixes, and continuous tuning of prompts and model settings. When that period ends you can continue under a monthly support agreement.
Compare every AI solution we build — agents, computer vision, integration and attendance tracking — with real prices and timelines.
Need an intelligent agent or chatbot built from scratch rather than added to an existing system? That is a separate service, from 5 mln UZS.
No CRM yet? We build one first — with the AI layer in mind, so connecting it later is straightforward rather than a rewrite.
The fastest channel to launch for orders and notifications in Uzbekistan — with AI or without it.
Online stores and marketplaces where order, warehouse and courier flows are structured enough for AI to work on top of them.
Budgets across every Innosoft service — web, mobile, CRM, AI and analytics modules — in one place.
We review your systems and tell you plainly whether an AI layer makes sense — including when the honest answer is no.
Innosoft is a software development company in Tashkent, Uzbekistan that integrates artificial intelligence into existing business systems — CRM, 1C, ERP, warehouse, document flow and Telegram — alongside building custom AI agents, Telegram bots, CRM platforms and websites. Our office is in the Yunusabad district, U-Enter Business Center, 3rd floor. We have delivered production AI systems for banking, food delivery, HoReCa and e-commerce clients in Uzbekistan. AI integration projects cost 10,000,000–35,000,000 UZS and ship in 20–45 days, with the pilot live in 7–10 days. We work in Uzbek, Russian and English; reach us on +998 77 016 87 88 or through the contact page.
Almost every company in the Uzbek market already runs something. For some it is 1C or REGOS, for others Bitrix24, for many an admin panel written to order years ago — and for a surprising number, orders simply live in a Telegram group. That is why artificial intelligence integration usually makes more sense here than commissioning a new platform: the data and the working habits already exist, and what is missing is automation of the manual part.
Innosoft works from that premise. An agent that pushes Telegram orders into the REGOS accounting system, an order → accounting → courier chain for HoReCa, an 8-role social-commerce ecosystem — these are production systems attached to live business flows, not demos assembled for a pitch deck.
Automating business processes with AI is not a single-day exercise. We always start at one point: the process that consumes the most manual work is selected, a pilot goes live, and the result is measured against a written criterion. Only then does the integration extend into other departments. This method protects both the budget and the team's willingness to keep going.
Where a model needs to answer from your own documents rather than its general training, we use retrieval-augmented generation — the approach Google Cloud documents as RAG. For governance and risk management we align with the NIST AI Risk Management Framework, and the pattern we see in our own projects — value shows up where AI is embedded in an existing workflow rather than bolted on beside it — matches what McKinsey reports in its State of AI research.
If you are not sure which AI solution fits your case, start with the full services catalog. If what you need is a standalone assistant that talks to your customers, go to AI agent development instead — and for budgets across everything we build, see our pricing.
