AI solutions for business: chatbots, OCR, voice assistants, enterprise search

Artificial intelligence

AI solutions for business

DIgitise your COMpany

We embed AI into real processes — where it counts money, not where it does tricks. From a chatbot built on your knowledge base to OCR document processing. Every project starts with a free 30-minute session.

Discuss a project Download price list

What we do

Six AI-powered products

We work with current models: Claude, GPT-4, open LLMs and Russian ones (GigaChat, YandexGPT). We pick the model to fit the task — sometimes open-source is better, sometimes a commercial cloud model. Client data never goes into training third-party models — we put that in the contract.

Chatbot

Chatbot on your knowledge base

Answers client and staff questions from your documents and regulations. Cites sources, says "I don't know" when the answer isn't in the base.

Voice

Voice assistants

Call transcription and summaries, voice bots for the first line of support, speech-to-text in the deal card.

OCR

Document processing (OCR)

Recognising and structuring invoices, contracts, acts. Data flows straight into CRM or 1C instead of being typed by hand.

Vision

Computer vision

Image and video analysis: quality control, object counting, defect detection on the production line.

Bitrix24

AI inside Bitrix24

Call summaries, auto-replies to leads, lead qualification and deal-closing prediction — through the Bitrix24 REST API.

Enterprise search (RAG)

A single search across all company documents and drives. The employee asks in plain language and gets an answer with source links.

A pilot (MVP) starts from $1,700. The production price depends on data volume, load and the number of integrations. External model API costs (OpenAI, Anthropic, GigaChat, YandexGPT) and infrastructure are paid by the client, billed on usage.

How we work with AI

Four principles

Pilot

Pilot first, production later

We don't believe in big AI rollouts done blind. First a 30-day pilot: we show how it works on your data and what result it gives. If it doesn't land — we move on, no obligations.

ROI

We do the maths before the start

In the free session we estimate the potential effect in money — how much time the assistant saves, how many errors OCR removes. If the ROI doesn't add up — we'll honestly say "don't do it".

Data

Your data stays with you

For sensitive data — an on-premise LLM on your servers or Russian models (GigaChat, YandexGPT) with a subscription through us. No leaks into someone else's models.

Integrations

AI in real processes

We connect it to your systems: Bitrix24, 1C, ERP, email, messengers. AI works inside the employee's usual interfaces, not in a separate chat.

From idea to a working solution

Project stages

1

Free session (30 minutes)

We listen to the task, look at the processes, assess whether AI applies. Often it becomes clear the task can be solved without AI — then we say so, we don't sell.
2

30-day pilot

We build an MVP on your data: the chatbot answers from your knowledge base, OCR processes your documents, search looks through your drive. From $1,700 for the pilot.
3

Production version

If the pilot succeeds — we scale: integrate into Bitrix24/1C/messengers, add quality monitoring, prepare the production deployment. Price set by data volume and load.
4

Support

A subscription with reserved hours: re-training the model on fresh data, monitoring answer quality, handling errors. From $400/mo.

Frequently asked

Questions about AI

How much does AI implementation cost?

A pilot — from $1,700, built in 30 days. At this stage you see whether the solution works on your data and what effect it gives. The production version — from $4,900, the price depends on data volume, the number of integrations and load.

External model API costs (OpenAI, Anthropic, GigaChat, YandexGPT) are separate, billed on usage. For a mid-sized business chatbot that's usually $70-400/mo.

Which models do you work with?

We pick to fit the task:

Commercial cloud: Claude (Anthropic), GPT-4 (OpenAI) — the highest quality, but a paid API.

Russian: GigaChat (Sber), YandexGPT — for cases where Russian servers are required.

Open-source on-premise: Llama, Qwen, T-Pro — for on-premise deployment on your infrastructure, when data can't go outside.

In the free session we'll discuss which option fits your scenario and budget.

We have sensitive data — can it be on-premise?

Yes. We deploy open-source models (Llama, Qwen, Mistral) on your servers — data never leaves the perimeter. Downsides: quality is slightly below Claude/GPT-4, and it needs compute (at least one A100-class GPU or equivalent).

An alternative — Russian cloud models (GigaChat, YandexGPT). Servers in Russia, a contract with the provider, personal-data processing under the law.

What if the AI starts making mistakes or "hallucinating"?

That's a normal risk for any AI system. We minimise it three ways:

RAG (search over the base): the model answers only from your documents, without "free" reasoning. If the answer isn't in the base, it honestly says "I don't know".

Source citation: every answer shows which document the AI relied on — an operator can verify in a second.

Quality monitoring: in the support subscription we review a sample of dialogues weekly and re-train on problem cases.

For critical decisions (medicine, law) we keep a human expert in the loop: AI drafts, a person checks and confirms.

How is the ROI of an AI project calculated?

We calculate it in the free session before work starts. Typical formulas:

Support chatbot: savings = (number of requests × average cost per operator minute) minus the subscription cost. ROI usually within 6-12 months.

Document OCR: savings = (number of documents × manual entry time × employee rate) minus implementation cost. Pays off in 3-6 months at volumes from 500 documents/mo.

Enterprise search: savings = (time spent searching × number of employees × rate) minus pilot cost. Harder to measure, we do it through staff interviews.

If the numbers don't add up in the session — we'll honestly say AI isn't cost-effective for this task.

We already have Bitrix24 — how do we embed AI?

Through the Bitrix24 REST API and marketplace apps. Typical scenarios:

AI call summaries: conversation → transcript → short summary in the deal card.

Auto-replies to leads: AI answers typical client questions in chat before a manager sees the request.

Lead qualification: from the request text, AI tags "hot/warm/cold" and routes to managers.

Conversation analysis: AI reads the deal history and predicts the probability of closing.

More on the Bitrix24 implementation page.

Discuss an AI project

Free AI session

Tell us briefly about the task — in a 30-minute meeting we'll assess whether AI applies, sketch a scenario and the ROI. No obligations, no proposal just for show.

We'll reply within 1 business day. The session is over Zoom/Telegram — your choice of channel.