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Business automation

Removing manual work
from business processes

We systematize company processes and remove manual work: we implement task trackers and CRM, connect systems to each other and, where it pays off, add AI bots and assistants.

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Automation principle

We automate the process rather than adding another chat

We start with the operations where the team is losing time, customers or quality. We pick a scenario with a measurable impact and only then choose the model and interface.

AI automation means embedding language models into specific processes: handling requests, searching documents and answering questions about your data. Unlike "yet another chatbot", the system is connected to the CRM, knowledge base and channels, follows company rules and hands complex cases over to a person.

Here is how the pieces fit together: LLM (a model that understands and generates text) works through API (OpenAI, Anthropic or enterprise access) and answers from your data via RAG — grounded in company documents rather than the model's guesswork. We orchestrate scenarios with n8n and integrations with Twenty CRM or amoCRM, Plane and Jira, WhatsApp Business API, Kaspi Pay and eGov.

Pilot
2–4 weeks
Stack
LLM API · n8n · RAG
Languages
RU / KZ
Format
Online · Astana / Almaty
People in Kazakhstan use AI for chatbots and automation at 1.8× the global average (Anthropic, 2025), but most companies have not yet built a system around it. The window to get ahead of competitors is still open.
In plain words

What it is and why it matters

What it is
We bring order to company operations: set up task tracking and a CRM (customer and deal database), connect your tools and add AI assistants where the effect is measurable.
Who it is for
An IT company where leads get lost in messengers, tasks scatter across chats and managers cannot see team workload or deal status without asking around.
What you get
One place for leads and deals with nothing lost, visible tasks and deadlines, connected systems and, where needed, an AI scenario tested in a pilot with one team.
Where to start
Tell us where leads, tasks and conversations live today and what hurts most. In a free basic diagnostic we will choose the first process for a pilot.

Task trackers: Jira and Plane

We implement work management systems around the team's real process: from audit and status model to migration and training.

  • Jira Cloud, Plane Cloud or self-hosted on servers in Kazakhstan;
  • workflow, roles, task templates and integrations;
  • migration from Jira, Trello and spreadsheets;
  • a pilot with one team, then scaling.

Result: tasks, deadlines and ownership visible to the manager without chasing people in chats.

CRM implementation: Twenty and amoCRM

We implement Twenty CRM on servers in Kazakhstan or amoCRM for messenger-driven sales, and connect the CRM with the task tracker and AI assistants.

  • pipeline, stages, fields and permissions;
  • client import from spreadsheets and old CRMs;
  • integrations with the website, WhatsApp, Plane and Jira;
  • AI lead qualification and conversation summaries.

Result: one pipeline where leads are not lost and a won deal becomes a project right away.

AI website bots

We help visitors choose a service or product, answer from a verified knowledge base and pass qualified requests to a manager.

  • consultation and qualification scenarios;
  • answers based on company documents;
  • capturing contact details and request context;
  • handover to a person without losing the conversation.

Result: a working website assistant with measurable conversion and quality control.

Employee assistants

We cut the time spent searching for information and preparing routine work materials, without opening access to all company data.

  • search across policies and documents;
  • drafts of emails, instructions and proposals;
  • role-based access and links to sources;
  • request log and answer review.

Result: less manual searching and faster turnaround on repetitive knowledge work.

Chat with Data

We give managers and teams conversational access to reports and metrics without building every query by hand.

  • connecting permitted data sources;
  • business glossary of metrics and query validation;
  • answers with tables and cited sources;
  • restrictions on sensitive data.

Result: quick answers to operational questions with controlled calculation logic.

Support automation

We speed up request handling while keeping the option to hand complex and sensitive cases to a staff member.

  • request classification and routing;
  • conversation summaries and agent prompts;
  • SLA and repeat-contact monitoring;
  • analytics on contact reasons and answer quality.

Result: less load on the first line and clear visibility into customer service quality.

Fractional AI Manager

We take on the role of head of AI without a full-time hire: we identify where automation really pays off, prioritize scenarios and own the implementation.

  • AI strategy and a scenario map tied to business goals;
  • priorities, pilots and return on investment (ROI);
  • selection of tools, models and vendors;
  • team training, quality and security control.

Result: systematic AI development in the company without bloating headcount or running chaotic experiments.

Comparison of approaches

Off-the-shelf chatbot or process-specific AI automation

An off-the-shelf chatbot answers from a generic script and does not know your business context. Process-specific AI automation runs on your data, is integrated with the CRM and channels, and is accountable for a metric. Below is when an off-the-shelf solution is enough and when you need a managed setup.

CriterionOff-the-shelf chatbotProcess-specific AI automation
Source of answersGeneral scriptCompany knowledge base (RAG) with source links
IntegrationsLimited or noneCRM, WhatsApp API, Kaspi Pay, eGov, 2GIS
LanguagesUsually RURU + KZ at native level
Accountable forJust getting it launchedA metric: conversion, response time, SLA
Error controlLowRestricted sources + escalation to a human
When it's enoughStandard FAQs, low volumeSales, support, data work, regulatory topics

Risks and limitations

What to consider when implementing AI in Kazakhstan and the CIS

AI only delivers results on a managed process. The main risks are model "hallucinations", data leaks, weak Kazakh language support and dependence on a single provider. We address them before production: we restrict sources, separate access rights and build in escalation to a human.

Model hallucinations

Without grounding in documents, an LLM makes up answers. The fix is RAG, a test set of questions and rules for escalating to an operator.

Data and compliance

Personal data and trade secrets require role-based access, logging and a decision on where the model is hosted.

Kazakh language

Mainstream models are weaker in Kazakh. We check quality with native speakers, not with machine translation.

Provider dependency

API prices and access change. We design the system so the model can be swapped without rewriting it.

Implementation approach

From one scenario to a working system

We don't start with a big AI transformation. In a short cycle we test one process, measure the result and scale only proven value.

01

Process

Find the losses

We map the current flow, the cost of manual work and the success criterion.

02

Pilot

Launch the scenario

We build the knowledge base, integrations and a secure working environment.

03

Scale

Manage quality

We add analytics, new channels and the next processes.

For IT companies

Business automation for IT companies in Kazakhstan

For IT companies and development teams, business automation starts with systematization: one CRM, a task tracker and integrations between them, with AI added on top of data that already works. Sales, development and support then operate in a single loop.

SaaS: from trial to payment

For SaaS we connect product sign-ups, the CRM and billing: a new trial lands in Twenty CRM as a deal with its source, plan and trial end date. The manager sees who activated and who got stuck in onboarding, and gets a task at the right moment. Before the call an AI step prepares a summary of account activity, so the conversation starts with facts rather than generic questions.

IT studios: from deal to project

For studios and outsourcing teams the main loss is the gap between sales and delivery. We set up a chain: a won deal in the CRM creates a project in Plane or Jira with a task template, estimate and owner, and delivery status flows back to the client record. The head of the studio sees team workload before signing the next contract.

Mobile apps: reviews and bugs

For mobile app teams we collect feedback from the app stores, support and messengers into one queue. AI classifies each message as a bug, question or request and creates a tracker task with the app version and device when the user mentions them. Duplicates are merged, and the product manager sees which problems repeat most often.

A growing development team

As a development team grows, tasks scatter across chats and the head loses sight of workload. We introduce one tracker with a clear status scheme, link it to Git and the CRM, and build dashboards for deadlines and bottlenecks. Only then do we add an AI assistant that finds answers in internal policies and project documentation.

How it works

How IT business systematization works in practice

IT business systematization is the order in which a company moves sales, tasks and communication into connected systems: CRM and task tracker first, then integrations, then AI. Each step delivers value on its own and becomes the foundation for the next one.

Process map before choosing tools

We start not with choosing a CRM or a model but with a map: where leads come from, where client information lives, how a task reaches a developer and how the result gets back to the client. For every handoff we note who does it manually and how long it takes. The output is a process diagram with loss points and a list of scenarios ranked by impact and complexity. The typical mistake is automating everything at once. We pick one or two handoffs where manual work repeats every day and start the pilot there. Using the map, we agree on the success criterion for the first pilot with the owner.

CRM and tracker as the foundation

AI and integrations are useless without clean data, so the CRM and task tracker become the foundation. For IT companies the main option is Twenty CRM: open source, custom objects that match the product model, an API and webhooks. If sales run through WhatsApp and other messengers, we choose amoCRM. Tasks move into Jira or Plane with a scheme of 4–6 statuses. We decide based on the real process, data storage requirements and who will maintain the system after launch, not on how popular a tool is. If the company already works in one of these systems, we first check whether it can be put in order without migrating.

Integrations: one source of truth

We build integrations on n8n and through service APIs: website and forms, WhatsApp Business API, Kaspi Pay, the CRM and the tracker. For each link we define upfront which system owns the data, what happens on error and who gets notified. Without this rule two systems start overwriting each other and the team goes back to spreadsheets. We also describe concrete scenarios: a won deal creates a project, a Kaspi payment changes the client status, a WhatsApp message lands in the client record with its full history. Every chain is logged, so a failure is visible at once rather than a week later from a client complaint.

AI inside the process, not beside it

We add AI as a step in an existing chain: lead qualification before handoff to a manager, a conversation summary in the CRM record, classification of a support request, a draft reply with a link to the source document. The model works via API and RAG over the company knowledge base, and the result is written where the team already works. Before launch we build a test set of real questions in Russian and Kazakh and check the answers with native speakers. Complex and sensitive cases always go to a human. After launch we regularly review low-confidence answers and extend the knowledge base.

Data in Kazakhstan and owner decisions

If the company wants its data stored in Kazakhstan, we choose self-hosted Twenty and Plane on servers in the country for the CRM and tracker. For AI steps we decide which data may go to an external API and which must be anonymized. In small IT companies the owner makes the decisions, so we give them one dashboard: lead flow, deals without a next step, team workload and deadlines. If the owner does not use the system, the team quickly goes back to chats. Interfaces and message templates are set up in Russian and Kazakh when people use the system in both languages.

Training and a system owner

Systematization rests not on tools but on the people who use them. For each system we appoint an owner on the client side who is responsible for rules, access rights and data quality. We run role-based training, separately for sales, development and support, and leave short guides with examples from the company's real tasks. In the first weeks after launch we handle the team's questions and adjust whatever gets in the way. A typical mistake is handing over a configured system without working rules: a month later everyone uses it differently and the data diverges again.

Step by step

How business automation works: five steps

Each step ends with a concrete artifact and a decision, so the owner of an IT company sees progress, not just meeting notes.

Diagnostic session

We meet the owner and the heads of sales, development and support. We look at where leads, tasks and conversations live today, which systems are already in use and what hurts most. We review the real spreadsheets, chats and old CRM rather than a slide about the process. The output is a list of problems with an estimate of manual work and a preliminary choice of the first scenario.

Process map and scenario choice

We draw an end-to-end diagram: lead, deal, project, task, release, support request. We mark manual handoffs and losses and assess the impact and complexity of each scenario. Together with the owner we choose the first pilot and its success criterion, for example time from lead to first reply. The artifact is a process map, a prioritized scenario list and an agreed pilot plan with owners.

Systems and integrations setup

We deploy or reconfigure the CRM and tracker, migrate data and build integrations on n8n and through APIs. For each link we document the data source and behavior on error. On the client side the process owner takes part, plus a developer or administrator if there is one. The output is a working pilot environment, test scenarios and an integration log showing what fired and when.

Pilot with one team

One team or one pipeline works in the new process for 2–4 weeks. Every week we review where the system gets in the way, which fields stay empty and which integrations fail, and adjust right away. If the pilot includes an AI step, we check its answers on the test set and on real requests. The step ends with a measurement of the success criterion and a decision: scale, change or stop the scenario.

Rollout and handover

We connect the remaining teams, the next scenarios and a dashboard for the owner. We train users by role and hand over the working rules and integration documentation to the system owner on the client side. We agree on the support format: the team works independently, regular check-ups, or a Fractional AI Manager role. The artifact is a documented system the company can develop without us. A month later we check how the system is used and adjust the rules.

Engagement formats

Business automation formats in Kazakhstan

We work in pilots: first one process in 2–4 weeks, then rollout to other teams and scenarios. We choose the format based on process maturity and the systems the company already has.

FormatWhat is includedTimelineOutcome
Process diagnosticProcess map, loss points, scenarios ranked by impact and complexity, CRM and tracker recommendationBefore the pilot startsPriority scenario list and pilot plan
Single-process pilotSetting up the CRM, the tracker or one AI scenario, integrations, test set, team training2–4 weeksA working process with a measurable success criterion
CRM and task tracker rolloutTwenty or amoCRM, Jira or Plane, data migration, deal-to-project link, working rulesBased on the diagnostic resultsOne pipeline and tasks linked to clients
Fractional AI ManagerAI scenario map, priorities, pilots, quality and security controlOngoing, by agreementSystematic AI development without a full-time hire

We choose the right format during a free basic diagnostic, after reviewing the company's processes and systems.

Glossary

Terms in plain words

LLM
Large language model — software that understands and writes text. It powers AI bots and assistants.
API
A programming interface that lets one system exchange data with another without human involvement, for example a website with a CRM.
RAG
An approach where the model answers from company documents and cites the source instead of making up an answer.
n8n
A tool for linking services into automated chains: a website lead lands in the CRM, a won deal creates a project in the tracker.
Model hallucinations
Plausible but invented AI answers. They are reduced by limiting sources, test questions and handing complex cases to a person.
Self-hosted
Running a system on your own servers, for example in Kazakhstan, so customer and task data stays inside the country.

Frequently asked questions

Safe AI implementation

Do we need a separate AI model API?

A production scenario usually requires an API for the chosen model or enterprise access. The specific provider depends on your data, quality, cost and hosting requirements.

How do you reduce the risk of wrong answers?

We restrict sources, add links to documents, a test set of questions, rules for escalating to a human and regular quality checks.

Where is the best place to start with automation?

With one frequent process that has a clear cost of manual work and a measurable success criterion, rather than automating the whole company at once.

How much does implementation cost and when does it pay off?

A pilot for one process has a fixed price based on its scope. We calculate payback against a metric: hours saved, higher conversion or lower cost per request. We prepare a commercial proposal after the diagnostic.

Will AI replace employees?

No. AI takes over the routine (search, drafts, qualification), while decisions and complex cases stay with people. The goal is to free up the team's time for higher-value work, not to cut headcount blindly.

Where should we start: AI or a CRM and task tracker?

In most cases, with the CRM and task tracker. AI works on company data, and without a single pipeline and transparent tasks it has nothing to rely on. We first put things in order in Twenty or amoCRM and in Jira or Plane, connect the systems with integrations, and then add AI lead qualification, conversation summaries and assistants; if processes and data are already in order, you can start straight with an AI pilot.

Which should we choose: Twenty CRM or amoCRM?

Twenty is an open-source CRM that can be deployed on servers in Kazakhstan, flexibly adapted to your data model and connected to your product via API; it is our main focus, especially for IT companies and SaaS. amoCRM is convenient when sales happen mostly through messengers and you need a ready-made cloud system. The choice depends on your sales process, data storage requirements and the integrations you need.

Which should we choose: Jira or Plane?

Jira is a mature standard for IT teams with a wide integration ecosystem and flexible workflows. Plane is an open-source tracker with a simpler interface that can be used in the cloud or deployed on your own servers in Kazakhstan. We make a recommendation after a process audit, taking into account team size, data requirements, budget and CRM integrations.

Can the CRM and task tracker be hosted on servers in Kazakhstan?

Yes. Twenty CRM and Plane can be self-hosted on servers in Kazakhstan so that customer and task data stay in the country. We set up the deployment, access roles and integrations. Cloud versions are also an option if your data residency requirements allow it.

Can data be migrated from an old CRM, Trello or spreadsheets?

Yes, migration is a standard part of implementation. We transfer customers, deals and tasks from spreadsheets, Trello, Jira or a previous CRM. Before the transfer we remove duplicates and map fields and statuses, and we check the result on a test sample before the team switches over.

What integrations can be set up between the CRM, tracker and other services?

We connect the CRM to the website and forms, WhatsApp Business API, Kaspi Pay, eGov, and to Plane or Jira — for example, a won deal automatically becomes a project in the tracker. Workflows are built in n8n and through service APIs. AI steps such as lead qualification or conversation summaries are built into the same workflows.

How long does it take to implement a CRM or task tracker?

It depends on the number of teams, the volume of data to migrate and the number of integrations. We start with a pilot on one team or one funnel, validate the statuses, fields and permissions, and then roll out to the rest. This way the team starts working in the new system sooner and setup errors are caught at a small scale.

Glass process channels converge at the center of AI automation