AI solutions and IT consulting · Kazakhstan
AI and IT consulting for real workflows
We work on the consulting side of applied intelligence: where AI fits, how your systems connect, and how to make technology decisions you will not regret in two years.
What we take on
Where AI actually helps
AI earns its place when it is attached to a specific task that is currently slow, manual, or inconsistent. The test is simple: if you cannot name the task it replaces or speeds up, the project is not ready.
Task before model
Applied intelligence in practice
Applied intelligence means using AI where it changes an outcome, not where it looks impressive. In practice that is often a narrow model doing one job well, integrated into a system your team already uses. The engineering work sits in the integration, the error handling, and the fallback when the model is uncertain — that is the part most vendors under-scope.
Route incoming requests
New enquiries get read, categorised and sent to the right queue, with anything unclear handed to a person instead of guessed at.
Summarise long records
Case files and contract histories condensed into a short brief, so a specialist starts from context instead of from page one.
Flag anomalies
Unusual entries in a dataset surface for review, with the threshold and the reasoning kept visible so nobody has to trust a black box.
Systems integration
Most businesses do not have one system. They have a CRM, an ERP, a billing tool, a support desk, and a spreadsheet that holds everything together. Making those talk to each other is where a lot of value sits, and it is unglamorous work.
We build integrations that move data reliably between tools, handle failures without losing records, and keep a clear audit trail. Every connection ships with retries, a dead-letter path and a log someone can actually read at 2am. It is the kind of project nobody notices when it works, which is exactly the point.
- Failure handling
- Retries and a dead-letter queue, so a bad record is parked for review instead of vanishing mid-sync.
- Audit trail
- A written record of what moved, when, and from where, kept where your finance and compliance people can reach it.
- Ownership of the pipeline
- Documented architecture, credentials in your accounts, and a handover your team can run without us.
- Scope honesty
- If a tool has no usable API, we say so before the build rather than discovering it halfway through.
Data governance
Before you automate anything on top of your data, you need to know where it lives, who can access it, and how long it is kept. Data governance is the discipline behind those answers. For AI projects it matters more, because models depend on data quality and raise questions about consent and retention.
We help clients map what they have, set practical rules, and avoid the situation where a promising tool cannot be deployed because nobody can say where the training data came from. The output is usually a short, plain-language record: sources, owners, retention, and what is allowed to leave your environment.
What we map first
- Where each dataset actually lives, including the spreadsheets nobody mentions in meetings
- Who inside and outside the company can reach it today
- How long records are kept and what triggers deletion
- Which data a model may use, and which must stay out of scope
IT consulting for decisions
Not every engagement ends in a build. Sometimes the useful output is a decision: build or buy, replace or extend, move to the cloud or stay put. We work through those questions with your team, produce a recommendation with the reasoning attached, and leave you able to defend it internally.
Build or buy
We compare what a vendor package really covers against what you would have to maintain yourself — licence, integration, staff time and the cost of changing your mind later. The recommendation comes with the trade-offs spelled out, including the option to do nothing this year.
Typical output: a written recommendation your finance and IT leads can both sign, plus a short list of what would change the answer.
Engagement shape
Short and specific, usually a few working sessions with your team rather than a long discovery phase.
Ask for a scope note
Working alongside your team
We are used to working with internal IT teams, not around them. That means sharing architecture decisions, documenting what we build, and handing over enough context that your team can maintain it. When a project needs skills your team does not have yet, we can cover the gap while training happens.
The goal is always for you to end up less dependent on us, not more. If that sounds like a strange thing for a vendor to say, it is also the reason clients come back with the next project.
Shared decisions
Architecture calls happen with your engineers in the room, not in a document that arrives afterwards.
Written handover
Setup notes, environment details and known limits, kept in your repository rather than ours.
Skill cover
We hold the specialist work while your team learns it, then step back as the internal owner takes over.
No black boxes
You keep the accounts, the repositories and the models. Nothing important lives only on our side.
What a realistic AI project looks like
A typical project starts with a short assessment of the task, the data, and the constraints. If it passes, we build a small working version, test it against real inputs, and measure whether it actually helps. Only then does it get integrated into production. That order matters — building a full system before validating the core task is the most common way AI projects waste money.
If the assessment says the task is not ready, that is a result too. We would rather hand you a clear no than a build that nobody uses.
Regional context
Kazakhstan's market has moved past the hype stage on AI. Businesses are asking practical questions about data, integration and cost rather than chasing the label. That suits how we work.
We are based around this market, and the projects that succeed here tend to be the ones with a clear task, clean-enough data, and a team willing to change a process rather than simply buy a tool. Our IT consulting in Almaty and the wider region is shaped by that reality: fewer slides, more working systems.
Studio hours: Monday to Friday, 9:00 AM – 6:00 PM
Questions clients ask
If your question is not here, write it down and bring it to the first call. We would rather answer it before a proposal than after.
How do AI consulting services differ from custom AI implementation?
Consulting covers assessment, feasibility, data readiness and risk. Implementation is the engineering work of building and integrating a working system. Some firms do both; others specialise. Ask directly who writes the code and who owns the handover.
Do we need clean data before starting an AI project?
You need data that is good enough to test a hypothesis, not perfect data. Part of the assessment is working out what you have and what it would take to make it usable. If the gap is too large, we will say so before you spend on a build.
Can you integrate AI into our existing systems?
Yes. Most of the work is integration rather than model development. We connect new capabilities to your current tools, handle failures gracefully, and keep an audit trail so the system stays maintainable after handover.
Who owns what you build?
You do. Code, configuration and documentation are handed over in your own accounts and repositories. We keep nothing that you would need to call us to reach.
Next step
Bring us the task, not the trend
Tell us which process is slow, manual or inconsistent, and what data sits behind it. We will tell you whether AI is the right answer — and what it would take to prove that before a full build.
Reach the studio
- Email kanogml1201@gmail.com
- Phone +1 (815) 934-4003
- Almaty office Furmanov Ave 110, 050000 Almaty, Kazakhstan
- Hours Monday to Friday, 9:00 AM – 6:00 PM