AI Implementation and Business Process Automation
We help Ukrainian companies get rid of repetitive routine, bring their data together, and put AI assistants to work alongside the team. We always start with one specific process. We agree on what counts as a result. Then we embed the solution into the systems you already use.
3years of AI implementation experience
in business processes
- Process analysis
- Pilot launch
- API integration
- Team training
Workflow architecture
- 1. Incoming data flow
Customer inquiry · Emailed PDFs · Internal policies · Catalog
- 2. AI assistant / AgentRAG + Tools
Semantic search · Validation against business rules · Hallucination check
- 3. Result and execution
Create a lead in CRM · Draft for the accountant · Team notification
Which processes can be automated
The biggest gains are where experienced specialists spend hours on mechanical work. The kind you can easily describe with rules.
Manual data transfer
Requests from email, Telegram, and forms get copied into CRM or spreadsheets by hand. The result: contact errors and a customer waiting too long for the first call.
Repeated questions
Up to 70% of support time goes to the same answers: schedule, delivery, availability, policies. And there is no time left for complex deals.
Document processing
Details from invoices, completion certificates, and waybills from dozens of counterparties are rewritten into accounting or ERP software. Manually. Every day.
Content preparation
Descriptions for hundreds of products. Or one press release that needs to be adapted for every channel without losing a consistent style.
Neural2B services
AI Agents for Business
Autonomous software assistants. They understand the context of the task, use API tools, and perform operations according to the rules you agreed upon.
AI Chatbots for Sales and Support
Intelligent dialogue systems trained on your regulations and catalogs. They respond 24/7 and do not lead clients into dead ends.
Business Process Audit for AI Implementation
We analyze repetitive operations, check data quality, and create a step-by-step automation roadmap.
Corporate Knowledge Base and RAG
Retrieval-Augmented Generation. Thousands of pages of internal instructions are transformed into an AI assistant that responds with references to the exact point.
Document Processing Automation
Intelligent recognition (OCR + LLM) of scans, PDFs, and photos of primary documents. The system checks mandatory fields itself.
Integration of CRM, Websites, and Systems via API
We build a reliable infrastructure through which different services exchange data. With retries and loss protection.
Marketing and Content Automation
Content generation and rewriting under control. Brand style (Tone of Voice) is preserved, facts are verified.
Sales Automation and CRM
We automate the entire lead journey — from the first click to a closed deal. Reception, classification, tasks, conversion control.
Website, online store, and CRM development
Websites, online stores, and CRM systems built around your process. With integrations, analytics, and room for an AI assistant from the start.
Solutions for your industry
Every industry has its own business rules, document formats, and constraints. We take that into account.
AI for B2B and Service Companies
We respond faster to initial inquiries, tackle complex technical tasks, and help managers prepare commercial proposals tailored to specific clients.
AI for Education and Corporate Training
An AI assistant for students based on course materials, automated onboarding of new employees, and integration with corporate LMS platforms.
AI for Logistics and Freight Transportation
Requests from emails are recognized automatically, loading and unloading addresses are verified, and the dispatcher receives a ready draft of the route.
AI for Online Stores
We automatically consult buyers, organize catalog specifications, and synchronize orders with HubSpot, Shopify, OpenCart, and UPS.
How implementation works
A step-by-step engineering approach. No vague promises or endless discussions.
Task description
We identify a specific process where your team is losing time or customers are waiting too long for a response.
Audit and data sampling
We look at what data you have and what shape it is in. We check the API of your existing systems and the processing rules.
Plan and architecture
We write the technical solution outline. We define what AI can do and what it cannot, and where human confirmation is mandatory.
Pilot prototype
We build a working solution and run it on a test set of requests. That shows how accurate it is and whether the logic is right.
Validation check
We measure speed and the share of correct responses. Then we decide whether the system is truly ready to launch.
Launch and training
We connect it to your working tools, hand over the instructions, and keep a close eye on things during the first weeks.
Examples of AI use cases
AI Assistant for Bookings and Orders
Restaurants and cafes often lose guests in the evening and during peak hours. The administrator is busy with the hall or billing, and no one answers calls and messages on Telegram/Instagram — 'is there a table?', 'what's on the menu?' — the guest goes to the neighbors.
Automation of Logistics Request Processing
A logistics company receives hundreds of emails daily in various formats: 'calculate the route', 'pick up the cargo'. Dispatchers manually copy cities, warehouse addresses, dimensions, and sender's phone numbers. Responses are delayed. Clients turn to competitors.
Corporate Onboarding Assistant
In the first 3–4 weeks, newcomers primarily seek information. How to apply for leave? Where are the passwords for services? Who approves invoices? What is the regulation for responding to clients? Mentors and HR are overwhelmed with the same questions.
We integrate with your systems
No need to replace the software your team already knows. We connect solutions to the tools your team already uses — via official APIs and webhooks.
Technologies we work with
We choose the stack to fit the task, not the other way around. Here is what we build with.
- Programming languages
- PythonTypeScriptJavaScriptPHPGoRustSQL
- Frameworks
- Next.jsReactVueHTMXNode.jsDjangoFastAPITailwind CSS
- Databases
- PostgreSQLMySQL
- Vector and graph databases / RAG
- pgvectorQdrantPineconeNeo4j
- Language models
- OpenAI GPTAnthropic ClaudeGoogle GeminiMistralLlamaQwenDeepSeekOllama (local models)Whisper (speech → text)OCR + LLM
- Automation
- n8nREST APIWebhooksOAuth2Telegram Bot API
- Data validation
- ZodJSON Schema
What the cost depends on
The estimate depends on the scope of work. We do not do made-up pricing tiers.
Quality of input data
If you have structured policies, spreadsheets, and a clean request history, development moves noticeably faster. With chaos spread across disconnected files, it takes longer. No way around that.
Number of integrations
A simple chatbot without system access costs less. An autonomous agent that exchanges data both ways with three different databases and CRM costs more. The logic is pretty clear.
Control requirements
How deeply the scenarios need to be checked, whether you need an interface where an employee approves actions, and what the validation test set should look like.
Potential automation impact calculator
Calculate how much your team's working time costs — time you could take back from routine tasks and invest in business growth. No need to leave contact details — run as many calculations as you want.
How many people regularly perform the operations you want to automate.
The full monthly cost of a workplace: salary, taxes, the workplace itself, licenses.
What part of the workday gets eaten up by routine: data entry, searching policies, handling email.
The part of routine work that makes sense to hand over to the system without losing quality control. Usually 40–60%.
A realism factor. Freed-up time never turns into money at 100%: some of it goes to pauses, some to more complex tasks.
Modeling results
Please note: Freed-up time does not necessarily mean a smaller payroll. In practice, most often the same team simply manages to process more orders. Without hiring more people.
All figures here are a model based on your assumptions. This is not a commercial guarantee of profit.
Data and quality control
How we protect your trade secrets and stop the model from doing anything nobody approved.
Isolated environment
Your internal documents do not end up in open training datasets for language models. At all.
Source of truth principle
The AI assistant answers only based on approved materials. And it adds a link to the document with every answer.
Human-in-the-loop
Critical financial and legal operations do not go through until the responsible manager personally clicks the button.
Full action audit
Every request, every response, and every external tool call is logged in the system event journal.
Answers to questions
A straight talk about the limits of the technology, security, and starting conditions.
Where should you start with AI implementation?▾
Start with one process that repeats all the time. Estimate how often you do it, how much time it takes, and where errors happen. Then in the consultation we will decide together whether AI is even needed here. Sometimes regular automation is enough, without language models.
Do you need to change CRM?▾
Not necessarily. First, we look at what your current system's API can do, what access is available, and what the limits are. Only after the technical analysis do we suggest how to integrate: via webhooks, n8n, or a direct script.
How is an AI agent different from a chatbot?▾
A chatbot mostly just talks to a person. An AI agent has a set of allowed tools and performs actions on its own. For example, it creates a request in CRM, checks whether an item is in stock, or generates a document.
Can AI make mistakes?▾
Yes, it can. That is exactly why we validate the data, limit the context (RAG), and hand unusual cases over to a person (Human-in-the-loop). And critical financial operations do not go through at all without employee confirmation.
Where will our company data be stored?▾
That depends on your project requirements. It can be in the cloud, on-premise, or hybrid. Before the work starts, we clearly define which data can be sent to external models and which data stays inside the closed environment.
How much does implementation cost?▾
The cost depends on the process, the quality of your data, the number of integrations, and how strict the validation needs to be. We calculate development, infrastructure, model API costs, and support separately. You will get an exact estimate after the task audit — naming numbers earlier would be dishonest.
Let’s start with your process
Describe the process that’s eating up your team’s time. We’ll suggest what can be automated, what data is needed, and where to start.