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Business automation and AI integration

Routine moves into code and models: AI assistants on your data, chatbots, process automation, AI features in your product and CRM. People stay where a decision is needed.

05 services

Subsections

In short

Automation moves routine into code and models: an AI assistant answers customers from your knowledge base, a chatbot takes bookings and payments in a messenger, data moves between the site, the CRM and your accounting system by itself, and documents and requests are sorted without manual work. We build it on our own code and pick the model for the task — Claude, GPT or a local one when data cannot leave your perimeter. At the steps that matter a person approves the result, your data is not used to train models, and keys and accounts are registered to you. The price is counted in hours and fixed before the start; model usage is paid to the provider directly, and the cost of every request is visible.

What we build

Services in detail

Service What’s included Who it’s for Link
AI Assistants
  • Assistant trained on your own documents, prices and policies (RAG)
  • Answers in your tone of voice, cites the source, admits when it doesn't know
  • Escalation to a human with full context
  • Multilingual out of the box
  • Voice channel if needed
  • Analytics on what people actually ask — and where your content fails them
  • Support teams answering the same 30 questions a hundred times a day
  • Companies with a large knowledge base nobody reads
  • Sales teams losing leads to slow first replies
 Go deeper about AI Assistants
Chatbots
  • Scripted flows for booking, orders, applications and payments
  • Telegram, WhatsApp, website widget — one logic, every channel
  • Lead qualification before a human joins
  • CRM and calendar sync
  • Payments inside the chat
  • Telegram Mini App as the interface when a chat window isn't enough
  • Local services living on bookings and calls
  • Online stores drowning in "where is my order"
  • Anyone whose sales still run through a manager copying messages by hand
 Go deeper about Chatbots
Process Automation
  • Agentic workflows that plan a task and run it end to end, not just move data
  • Incoming requests sorted, enriched and routed automatically
  • Documents parsed into structured data — invoices, contracts, specs
  • Scheduled reports and alerts
  • Systems connected through APIs and MCP
  • Human approval kept at the steps that matter
  • Companies where a person retypes the same data between two systems
  • Teams whose reporting is a manual Excel ritual every Monday
  • Businesses growing headcount just to keep up with paperwork
 Go deeper about Process Automation
AI Features Inside Your Product
  • Semantic search that understands intent, not keywords
  • Generated descriptions, summaries and replies
  • Classification, tagging and moderation
  • Personalised recommendations
  • Model routing to keep costs predictable
  • Guardrails, evaluation and logging so quality is measured, not assumed
  • Private or on-prem models when data can't leave
  • SaaS products competing against rivals who already shipped AI
  • Marketplaces and catalogues where search is the main funnel
  • Products sitting on data they've never turned into a feature
 Go deeper about AI Features Inside Your Product
CRM: Integration & Custom Development
  • Integration: your CRM wired into the site, forms, telephony, payments and messengers
  • Clean pipelines, automation, custom modules and reports
  • Custom build: a CRM written for your actual process when the boxed one fights you
  • Your entities, your roles, your logic, your data, no per-seat fees
  • Companies whose CRM is a database nobody updates
  • Sales teams keeping the real pipeline in a spreadsheet next to the CRM
  • Businesses with a process no boxed system will ever fit
 Go deeper about CRM: Integration & Custom Development

What automation gives a business

Not “AI for the sake of AI”, but hours returned to the team and requests that are no longer lost.

  1. 01

    Less manual work

    Data is not retyped between systems: transfers, reconciliation and reports run by themselves.

  2. 02

    An answer in seconds

    Customers get an answer at any hour, and a request does not wait until a manager is free.

  3. 03

    No lost requests

    Every enquiry lands in the CRM with its source and history.

  4. 04

    Fewer mistakes

    Code does not get tired or mix up rows; doubtful cases are handed to a person.

  5. 05

    The team does what matters

    People spend their time on customers and decisions, not on copying.

  6. 06

    Growth without new hires

    The flow of requests grows, the load on the team does not.

  7. 07

    Everything is visible

    A log shows every operation: what came in, what went out and what the other side answered.

  8. 08

    Everything is yours

    Code, model keys, bots and data belong to you — a change of contractor stops nothing.

Script, chatbot or AI: what your task needs

Not every task needs AI. The best result usually comes from a combination: rules where they are known, a model where meaning has to be understood.

  1. 01

    Scripts and integrations

    When the rules are known in advance: an order comes in — pass it to the CRM; every morning — build a report. Works the same way every time and costs the least.

    price list importssyncreportsalerts

  2. 02

    Scripted chatbot

    When the conversation fits into buttons: a booking, an order, a delivery status, a payment. Predictable, fast and with no model costs.

    bookingsorder statuspayment in chatTelegramWhatsApp

  3. 03

    AI model

    When the input is free text: a question in someone’s own words, an email, a contract, a scanned invoice. The model understands meaning, not keywords, and works on your data.

    knowledge base answersdocument parsingrequest sortingsearch by meaning

  4. 04

    Agentic workflow

    When a task has to be not only understood but carried through: find data in several systems, prepare a document, send it for approval. The model plans the steps, a person approves the important ones.

    several systemsMCPapproval

What is worth automating — and what is not

The rule is simple: frequent, repetitive and costly in hours — to code; rare and dependent on experience — to people.

  • Repeated customer questions

    Automate

    Answers from the knowledge base in seconds; difficult cases go to a manager.

  • Moving data between systems

    Automate

    The site, the CRM and accounting exchange data by themselves, with no retyping.

  • Reports and alerts

    Automate

    Built on a schedule and delivered where people actually read them.

  • Sorting incoming requests and emails

    Automate

    Topic, urgency and the person responsible are set automatically.

  • Parsing invoices and contracts

    With a human check

    The model extracts the data, a person checks amounts and dates.

  • Replies to complaints

    With a human check

    The model drafts, a person sends.

  • Negotiations and large deals

    Leave to people

    Here relationships and experience decide; automation helps by preparing the data.

  • Rare one-off tasks

    Leave to people

    If a task happens once a quarter, automating it will not pay off.

By hand, no-code services or own code: a comparison

Three ways to run the same process — so it is clear what you pay for in each case.

CriterionBy handNo-code (Zapier, Make, n8n)Own code
Launch already running days, from ready blocks longer: written for the task
Monthly cost salary for hours of routine a subscription that grows with the number of operations hosting and model usage
Complex logic depends on the person within the platform’s blocks no limits
Failures noticed late handled with the platform’s tools retries, an exchange log and alerts
Where the data goes in spreadsheets and inboxes through the platform (n8n can be self-hosted) stays in your systems
Who owns it the knowledge sits in people’s heads the scenarios live on the platform code, keys and access are yours
When it makes sense the process has not settled yet to test an idea and simple links when the process is part of how the business works

AI under control: data, quality and costs

A model is a powerful tool, but not an employee who can be left alone. Here is what keeps it within bounds.

  1. 01

    A human in the loop

    At the steps that matter the model proposes and a person approves: money, contracts and replies to complaints do not go out unchecked.

  2. 02

    Your data does not train models

    The work goes through the API, where the provider’s terms forbid training models on your data.

  3. 03

    Local models when needed

    When data must not leave your perimeter, the model runs on your own server.

  4. 04

    Access rights and a log

    An assistant sees only what it is allowed to; every request is logged, and access can be withdrawn at any moment.

  5. 05

    Answers with a source

    An assistant refers to the document the answer came from and admits when it does not know — instead of making things up.

  6. 06

    Predictable costs

    Simple requests go to a cheap, fast model, complex ones to a strong model. The keys are yours and the cost of every request is visible.

  7. 07

    Quality is measured

    A set of test questions is run after every change, so quality is measured, not assumed.

How the work goes

From one process to a result you can measure. Every stage can be seen and judged along the way, not at the end.

  1. 01Looking into the process

    We look at how the task is done today: who does what, where time is lost, which systems take part and what it costs in hours.

    You getA list of what is worth automating and what is better left to people

    Needed from youA story of the process, examples of requests, documents or chats, and a word with the people who do this work

  2. 02Specification and estimate

    We write down what the automation does, where a person checks the result, how the effect is measured and what it costs.

    You getA specification and a fixed estimate split into stages

    Needed from youApproval of scope and dates, one person who makes the decisions

  3. 03Prototype on your data

    The idea is checked on real examples before the main build: does the assistant answer correctly, are the documents parsed right.

    You getA working prototype and quality measurements on your examples

    Needed from youA set of real questions, documents or requests for the check

  4. 04Build and connection

    We write the code, connect the CRM, accounting, messengers and models, and set up access rights and the log.

    You getAccess to the test version as the work goes

    Needed from youAccess to your systems and model keys registered to you

  5. 05Launch and handover

    The automation goes into work, the team is shown how to use it and where to look in the log.

    You getInstructions and a log where every operation and its cost is visible

    Needed from youFeedback from the staff in the first weeks

  6. 06Warranty and what comes next

    A month of fixing anything that turns out broken — free: that is not support, that is finishing the job. Ongoing work is a separate agreement.

How much automation costs

There is no price list: every task is estimated in hours. Model usage is a separate line, and you see it yourself.

  1. Price — by hours and scope

    Fixed price for a fixed scope, split into stages. If the scope changes mid-way, we agree the difference separately — you always know the number before the work, not after. The price is built on the cost of an hour of our work: it reflects the skill and the speed with which the task gets done here. So an estimate is a count of hours, and the scope is always fixed together with the price. Projects with an undefined scope that surfaces along the way we do not take: working time is a resource that cannot be stretched, only shared between tasks.

  2. Payment by stages

    Thirty per cent to start, the rest by stages as they are ready — usually three payments: advance, interim and on completion. Big projects with a long timeline are billed monthly. The terms are agreed before the work starts and written into the contract.

  3. Model usage

    Models are paid to the provider by actual use, straight from your account. Simple requests go to a cheap model, complex ones to a strong model, and the cost of every request is visible.

  4. Deadlines

    Each stage has its own date. Delays on your side move the dates — that is stated up front so nobody is surprised: waiting for texts is the most common reason projects slip.

  5. The estimate is free

    We look into the task and advise for free, without obligation. The exact sum comes after the process review and the specification.

What we build automation with

A tool — and what it is for. Everything is chosen for the task: a simple bot does not need a heavy stack.

Models

  • Claude and GPT — picked for the task and the budget
  • Local models — when data cannot leave the perimeter
  • Routing between models — a cheap one for simple requests, a strong one for complex ones

Knowledge and data

  • RAG — answers from your documents, prices and rules, with a link to the source
  • PostgreSQL with vector search — meaning-based search in the same database
  • MCP — Claude or ChatGPT gets access to company data with rights and a log

Channels

  • Telegram, WhatsApp and a website widget — one logic, every channel
  • Telegram Mini App — when a chat window is not enough

Exchange with systems

  • REST API and webhooks with signature checks — CRM, accounting, payments
  • Retries with a growing delay — an integration survives another service going down
  • An exchange log — you can see what was sent and what came back
  • Scheduled scripts — imports, reports, regular emails

Code

  • Go — services where response speed and load matter
  • PHP — admin panels and integrations with the site
  • Keys in environment variables — never in the code or the repository

Common automation mistakes

  1. Automating chaos

    If the process is not set up, automation only speeds up the mess. Order first, code second.

  2. Expecting AI to know your business

    A model does not know your prices and rules until it is connected to your data — otherwise it confidently makes things up.

  3. Removing people from key steps

    Where money, contracts or a customer’s mood are at stake, a person approves the decision.

  4. Not measuring

    Without numbers “before” and “after” there is no way to tell whether the automation paid off.

  5. Starting with a giant project

    One process carried through to a result is worth more than ten started at once.

  6. Keys and accounts on the contractor

    Model keys, bots and access must belong to you — otherwise everything stops when you change contractors.

  7. Pasting company data into public chats

    Staff paste spreadsheets into a chatbot by hand and the data leaves your control. It is safer to connect the assistant to the data with access rights.

Glossary: the terms in plain words

Words you will hear while automation and AI are being built — briefly and without jargon.

Language model (LLM)
The “brain” behind ChatGPT and Claude: a program that understands and writes text.
AI assistant
A model connected to your data that answers questions and performs tasks.
Chatbot
A program that talks in a messenger or on a site — by a script, by a model or both.
RAG
A way for the model to answer from your documents: it first finds the relevant pieces, then writes the answer from them.
Prompt
The instruction a model works from: role, rules, tone and answer format.
Hallucination
When a model confidently states something that is not true. Treated with data, sources and checks.
Token
A piece of text — a word or part of one. Model usage is counted in tokens.
MCP
A standard way to give Claude, ChatGPT and other assistants access to your systems — with rights and a log.
Agent
A model that does not only answer but acts: plans the steps and performs them in your systems.
API
The way programs exchange data with each other: the site with the CRM, the bot with payments.
Webhook
An instant notice from one system to another: “payment received”, “new deal”.
Human in the loop
A step where the model proposes and a person approves before anything goes further.

Questions about automation and AI

How much does automation cost?

The price is counted in hours for your task and fixed together with the scope before the start. Model usage is paid to the provider separately, from your account, by actual use. The estimate is free.

How long does it take?

Roughly: a simple bot takes a couple of days, Telegram bots and Mini Apps 2–8 weeks, integrations and AI features 1–4 weeks each. The exact dates are fixed before the start.

Will AI replace our staff?

It takes over the routine: repeated answers, transfers, sorting. People stay where decisions, experience and relationships matter — and get time for them.

Is our data safe?

The work goes through the API, where the provider’s terms forbid training on your data. The assistant sees only what it is allowed to, every request is logged, and when data must not leave the perimeter, a local model is used.

What if the AI makes a mistake?

The assistant answers from your documents, refers to the source and hands the conversation to a person when it does not know. At the steps that matter a person approves the result, and quality is checked on test questions after every change.

Which models do you use?

Claude, GPT and local models — picked for the task and the budget. A cheap, fast model handles simple requests, a strong one handles complex questions.

Will it work with our CRM and accounting system?

Yes, if the system has an API or an export. The exchange goes both ways and is logged, so failures are visible at once.

Can we start small?

That is the best way: one process, a prototype on your data, measurements — and only then the next step.

Does the assistant speak other languages?

Yes, multilingual support works out of the box: the assistant answers in the language it is asked in.

Who will own the bots and keys?

You. Bots, model keys, accounts and code are registered to you and handed over at launch — nothing is held hostage.

Do you sign an NDA?

NDA on request before the launch. Under an NDA the project is not shown anywhere at all. In any case we pass no materials or information about your project to third parties and never reuse them in similar projects for others.

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