Is this just a toy or hype?

We're just as skeptical about AI as you are. That's why we only take on a task where the solution returns time or money — and turn it down when it won't pay off. Our best proof is our own product, Apros CRM: its AI module distributes leads in production every day, not in a slide deck. This isn't an experiment on your budget.

How is an AI agent different from a regular chatbot?

A regular chatbot follows a rigid script: press a button, get a canned reply. An AI agent understands free-form queries, checks your database and performs an action — qualifies a lead, books an appointment, hands it off to a manager. Put simply: a chatbot answers, an AI agent works for you. We build the second kind.

Which business processes can be automated with AI?

Five directions usually pay off most: customer replies and lead qualification (chatbots and assistants), document processing (extracting data from invoices and bills), search over a catalog or knowledge base, lead scoring and routing in a CRM, and integrating AI into existing systems. They share one trait: a lot of repetitive manual work at scale. We define the exact list for your business during the breakdown call.

Will AI make up answers (hallucinate)?

That's a real risk in poorly built solutions, and we close it with a RAG architecture: AI answers only based on your data, not "off the top of its head". Important: AI doesn't "learn" from your data or change the base model — it checks your database every time before answering. If a question falls outside the knowledge base, it hands off to a human. For the first two weeks after launch, we review conversations together and fix anything that isn't right.

We don't have data for AI — is that a problem?

Depends on the direction. For document processing and search, you already have the data — your invoices, catalog, FAQ, correspondence. There's nothing extra to collect. For predictive analytics, clean data may genuinely be lacking — and if so, we'll say that honestly at the breakdown call instead of selling you a solution that won't work.

What about data privacy?

Requests are processed through the OpenAI or Anthropic API under enterprise terms: data isn't used to train public models. Your database is stored on your infrastructure or our private server — your choice. Storage terms, access rights and an NDA are fixed in the contract before work starts. This applies to every direction, from chatbots to document processing.

Can you integrate AI into our CRM or 1C?

Yes — and it's not "rewriting everything from scratch". AI connects as a separate service on top of what already works: a webhook into your CRM, a 1C integration, a website widget, or a Telegram bot. The website and system stay as they are. If the system doesn't exist yet, we build basic automation first, then add AI.

Will AI replace my employees?

No. AI removes the routine: repeat questions, lead qualification, retyping documents, request routing. People do what they should — sell, handle complex cases, build relationships with customers. If an employee spends the whole day answering "how much does it cost" and re-entering invoices, AI gives them that time back for real work.

How long does implementation take and how much does it cost?

A prototype takes 2 weeks from the start; a full launch with a populated database and testing takes 4–6 weeks. Cost depends on scope and direction: number of scenarios, integrations, channels. We calculate the exact range at a 30-minute breakdown of your process. Monthly support and model hosting are a separate line item, also fixed before we start.

AI for business

AI assistants and chatbots for business

A chatbot that qualifies a lead at three in the morning. AI that extracts data from invoices instead of a manager. Search that understands a customer from half a sentence. We take on a task only where AI actually returns time and money — and we say honestly where it doesn't. Stack: Node.js + OpenAI / Anthropic, integration with your CRM.

Directions

AI directions — chosen for your task

Chatbots and AI assistants
Answer customers and qualify leads 24/7 — on the website, in Telegram, in your CRM. A voice or phone bot is a separate scenario for the same task. This is what "AI that works for you" really means: not just answering FAQs, but handing the manager a lead that's already sorted.
When it's NOT worth it: if you get 5 inquiries a week, a bot won't pay for its own development — we'll say so directly.
AI document processing and search
Extracts data from invoices, bills and forms — without manual entry into 1C. And finds what's needed from a description: a customer or manager searches the catalog or knowledge base in plain words, not exact matches. The clearest ROI among our directions: the data already exists, you just need AI to learn to read it.
When it's NOT worth it: if you handle a few documents a week and the format changes every time, entering them by hand is cheaper.
AI in your CRM: lead scoring
Sorts requests by urgency and routes them to the right manager automatically — a hot lead doesn't sit in the general pile. This is exactly how our own CRM works: the AI module distributes leads in production every day. Not "AI in the name for marketing" — real automation we use ourselves.
When it's NOT worth it: if you get few requests and they all go to one manager, there's nothing to route.
AI integrations and automation
We add AI to your website, CRM or 1C — without rebuilding from scratch. AI connects as one more service on top of what already works. "We need to rewrite everything" is the most common myth; it's almost never true.
When it's NOT worth it: if the system to connect to doesn't exist yet — first basic automation, then AI.

All directions

All AI directions — pick your task

Each direction has its own page with examples and an honest line for "when it's NOT worth it".

Bots and assistants

Data and content

In your systems

Real scenarios

What this looks like in your industry — task, AI action, result

Sales and support

E-commerce. Customers ask dozens of times a day about stock, sizes and delivery times. The bot answers from the catalog and qualifies the inquiry — the manager sees a sorted request, not a raw email.
Medical center. The front desk is swamped, calls queue up. The bot books appointments on Telegram around the clock — the receptionist is freed up, night bookings aren't lost.
Services and real estate. First contact with a customer happens in seconds, not an hour: the bot clarifies details per your script and hands the manager an already-warm lead.

Documents and data

Wholesale and logistics. A manager manually enters invoices into 1C. AI extracts line items from a PDF or photo — hours of manual work per day go back into actual work, not retyping.
Real estate. Client forms and documents are scattered across email and folders. AI structures them into a database — matching a property to a request happens faster.

Search and routing

Online course school. Students ask what's already in the materials. AI search over the course database answers on its own — fewer repeat support requests.
Any sales department. All leads pile up together, managers sort them manually. AI scores urgency and routes them (as in our own CRM) — hot leads don't sit around.

How it's built

Why this works, not "AI for show": Node.js + OpenAI / Anthropic

Custom code, not builders

Control over answers. RAG: the solution checks your data first, then answers. It's not "trained" on your data — it looks up the fact in it every time before saying a word.
Connects to what already exists. We integrate with your CRM, 1C, website, Telegram. Nothing gets rebuilt from scratch — AI connects as one more service.
Confidentiality. Customer data doesn't go toward training public models. Access, storage and an NDA are in the contract before we start.
This isn't "AI" for marketing. Our own Apros CRM runs on the same stack, with AI lead processing in production every day. We take on a project only where the solution pays for itself.

Will it pay off?

Not sure if you need AI? Let's find out together in 30 minutes

30 minutesOne-on-one online
Flexible formatVideo or phone call
Solution-focusedPractical answers

How we work

Five steps to launching an AI solution

Task breakdown
A 30-minute call: where you have routine work, what data already exists, which CRM and channels you use. We determine whether AI is needed at all and which direction to launch first. Deliverable: a list of scenarios + a stack recommendation + a rough budget and timeline range.
Prototype in 2 weeks
We build an MVP on your real data: connect the database, set up the first scenario, deploy to a test environment. You see a working solution, not a slide deck. Deliverable: a working prototype + the first test runs on your data.
Data from your sources
We load the catalog, documents, FAQ, scripts — whatever AI will work with. We test on live queries and fix anywhere the solution behaves incorrectly. Deliverable: the knowledge base in the system + a test log + a list of scenarios where AI hands off to a human.
Launch and monitoring
Production deployment: connection to the website, Telegram, CRM or 1C. First 2 weeks — we review performance together daily and fine-tune. Deliverable: a solution in production + connected integrations + configured analytics.
Support and growth
AI isn't set up once and forgotten: data and prices change, scenarios grow. We support and develop it on a monthly retainer or hourly. Deliverable: an up-to-date knowledge base + regular quality reviews.

FAQ

What businesses ask about implementing AI

Ask about AI

We're just as skeptical about AI as you are. That's why we only take on a task where the solution returns time or money — and turn it down when it won't pay off. Our best proof is our own product, Apros CRM: its AI module distributes leads in production every day, not in a slide deck. This isn't an experiment on your budget.
A regular chatbot follows a rigid script: press a button, get a canned reply. An AI agent understands free-form queries, checks your database and performs an action — qualifies a lead, books an appointment, hands it off to a manager. Put simply: a chatbot answers, an AI agent works for you. We build the second kind.
Five directions usually pay off most: customer replies and lead qualification (chatbots and assistants), document processing (extracting data from invoices and bills), search over a catalog or knowledge base, lead scoring and routing in a CRM, and integrating AI into existing systems. They share one trait: a lot of repetitive manual work at scale. We define the exact list for your business during the breakdown call.
That's a real risk in poorly built solutions, and we close it with a RAG architecture: AI answers only based on your data, not "off the top of its head". Important: AI doesn't "learn" from your data or change the base model — it checks your database every time before answering. If a question falls outside the knowledge base, it hands off to a human. For the first two weeks after launch, we review conversations together and fix anything that isn't right.
Depends on the direction. For document processing and search, you already have the data — your invoices, catalog, FAQ, correspondence. There's nothing extra to collect. For predictive analytics, clean data may genuinely be lacking — and if so, we'll say that honestly at the breakdown call instead of selling you a solution that won't work.
Requests are processed through the OpenAI or Anthropic API under enterprise terms: data isn't used to train public models. Your database is stored on your infrastructure or our private server — your choice. Storage terms, access rights and an NDA are fixed in the contract before work starts. This applies to every direction, from chatbots to document processing.
Yes — and it's not "rewriting everything from scratch". AI connects as a separate service on top of what already works: a webhook into your CRM, a 1C integration, a website widget, or a Telegram bot. The website and system stay as they are. If the system doesn't exist yet, we build basic automation first, then add AI.
No. AI removes the routine: repeat questions, lead qualification, retyping documents, request routing. People do what they should — sell, handle complex cases, build relationships with customers. If an employee spends the whole day answering "how much does it cost" and re-entering invoices, AI gives them that time back for real work.
A prototype takes 2 weeks from the start; a full launch with a populated database and testing takes 4–6 weeks. Cost depends on scope and direction: number of scenarios, integrations, channels. We calculate the exact range at a 30-minute breakdown of your process. Monthly support and model hosting are a separate line item, also fixed before we start.

Breakdown

Let's break down your scenarios with real examples

30 minutesOne-on-one online
Flexible formatVideo or phone call
Solution-focusedPractical answers

Learn more

AI for business — when it pays off, and when it doesn't

Apricode is a Ukrainian development team from Kharkiv, in the web since 2016, working remotely worldwide. We build AI solutions for business: chatbots and AI assistants, document processing, AI search and AI integration into CRMs. After 10 years in the web, we've learned the main thing about AI for business: it isn't about ChatGPT in a browser tab, and it isn't about "automating everything". It's about specific tasks — answering a customer at night, extracting data from a hundred invoices, routing leads. And it's about honesty — saying where AI pays off and where it becomes a trendy toy.

Chatbots and AI assistants: when they pay off

A chatbot or AI assistant pays off wherever there's a high volume of repetitive requests. An online store with 500+ SKUs where customers ask about sizes and delivery daily, a medical center with an overloaded front desk, an online course school — all good fits. A niche business with five customers a month — not really; a bot won't close anything significant there. We cover the depth — lead qualification, RAG search over the knowledge base, channels including Telegram — on dedicated pages. Here's the one thing that matters: if the volume doesn't justify implementation, we won't take the project, and we'll explain why.More on AI agents →

Document processing and AI search: the clearest ROI

The clearest payoff shows up where someone manually retypes data every day. AI document processing extracts line items from invoices, bills and forms — the manager stops copying PDFs into 1C and gets those hours back. AI search over a catalog or knowledge base understands free-form queries: a customer describes what they're looking for and finds the product or answer instead of "nothing found". Neither direction requires collecting new data — your documents and catalog already exist. When it's NOT worth it: if you get a handful of documents a week and the format changes each time, entering them by hand is cheaper, and we'll say so.

AI in CRM and automation: we connect, we don't rewrite

Our strongest proof is our own product. Apros CRM has a built-in AI module that scores leads by urgency and distributes them among managers automatically. We use this every day. We bring the same approach to your CRM: a hot lead doesn't sit in the general pile. And if AI needs to be added to your website, 1C or an existing system without rebuilding from scratch — that's the automation and integrations direction: AI connects as one more service on top of what already works. "We need to rewrite everything" is almost always a myth.Automation and integrations →

Where to start implementing AI

Implementing AI for business doesn't start with picking a model — it starts with an honest breakdown: do you have a task that AI would solve better than a person. If yes, we build a custom solution on Node.js and an LLM API, connect it to your infrastructure, launch it in 4–6 weeks and support it going forward. If not, we'll say so directly, and you'll save the budget. 30 minutes and you'll know whether it's worth it:book a breakdown call

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