A few calls and a written recommendation
We look at the workflow, the data you already have, and what a reasonable first step would cost in time. You get the recommendation in writing, including the case where the answer is to wait.
AI consulting and cloud consulting · Tbilisi
AI and cloud solutions for real work
We look at what your team actually does, where time is lost, and whether a model, a pipeline, or a cloud move would genuinely help. If the honest answer is not yet, we say so.
First engagement
A short assessment: a few calls, a written recommendation, and a scoped small first phase measured in weeks.
Request an assessmentWhere it actually pays off
Where AI fits in a normal business
Most useful AI work is not dramatic. It is document intake that used to take an hour and now takes minutes. It is support tickets routed to the right person without a human reading each one first. It is internal search that actually finds the answer instead of three near-misses.
Those are the projects we take on. Not company-wide AI strategies, but specific features with a clear before-and-after in how the work gets done.
Document intake. Incoming paperwork summarized and filed, with a person reviewing the summary rather than the whole stack.
Ticket routing. Support requests sent to the right desk on arrival, so nobody triages by hand every morning.
Internal search. One place to ask a question about your own documents and get a sourced answer back.
Practical first AI projects
We usually recommend a small first project, measured in weeks rather than months, so you can see whether the approach works before committing to a larger build. These are the four starting points that come up most often with clients.
Summarizing documents
Long incoming files reduced to a short brief a person can check in a minute.
Typical scope: 2–4 weeks
Drafting replies
A first draft written for a human to edit and send, never sent automatically.
Typical scope: 2–4 weeks
Extracting structured data
Pulling fields out of unstructured text into a table your team already uses.
Typical scope: 3–6 weeks
Searching internal knowledge
Questions answered from your own documents, with the source shown alongside.
Most requested
Choosing a stack
Models and platforms we work with
We work with OpenAI's models, Anthropic's Claude, and open-source models like Llama, depending on cost, privacy, and accuracy needs. For some clients, running a model on their own hardware matters more than getting the strongest output. For others, API cost decides it.
Retrieval before fine-tuning
We build with retrieval-augmented generation when a model needs access to your internal documents. For typical business cases this is more useful than fine-tuning: the answers stay grounded in material you control, and when a document changes you update the source instead of retraining a model.
Fine-tuning still has a place, usually when the task needs a consistent tone or a format the base model keeps getting wrong. We will tell you which of the two your case actually needs.
What decides the choice
- Privacy. If documents cannot leave your environment, a locally run open-source model is the starting point.
- Accuracy. Hosted models generally give the better output on messy, real-world text.
- Cost. Per-request pricing adds up fast on high-volume features; a local model trades that for setup and upkeep.
Platforms we cover
- Google Cloud — a primary platform for us
- AWS — the other primary platform
- Azure — supported where you already run it
Cloud consulting and infrastructure
Cloud work covers setup, migration, cost optimization, and security. Migrations from on-premise are usually phased rather than big-bang, because a hard cutover is riskier than it looks once real traffic is involved.
Cost reviews are the fastest place to find something. Savings often turn up in the first month, mostly from resources nobody switched off and instances sized for a peak that never arrived.
Phased migration
Move one workload at a time, prove it in production, then take the next. Rollback stays possible for every step.
Cost and access review
We read the billing export, find idle capacity, and tighten access rules while we are in there.
BI and data work
BI work starts with the data itself. If your reporting is unreliable, the first job is fixing the pipeline, not adding another dashboard on top of it. A chart that nobody trusts is worse than no chart, because it still gets used.
Warehouses and pipelines
We build data warehouses, ETL pipelines, and the models between them. Common tools include BigQuery, Snowflake, and dbt. The point of the pipeline is that numbers land in the same place, the same way, every day.
Dashboards people open
Dashboard layers like Looker Studio or Metabase sit on top of that pipeline. We keep the count small and the definitions shared, so two teams stop reporting the same number two different ways.
Reporting you can act on
The goal is reporting your team trusts enough to make decisions with. That means clear owners for each metric, a known refresh time, and a place to ask when a number looks wrong.
Digital transformation without the buzzwords
Most projects sold under that phrase are really about replacing manual steps with systems. That is less exciting than the phrase suggests, and far more useful. We approach it as a sequence rather than a programme.
- Step 1 Find the workflow that costs the most time each week. Not the one that sounds most modern.
- Step 2 Automate or rebuild that one workflow, and put a person at the decision points that matter.
- Step 3 Move to the next one once the first has settled. Changing everything at once rarely works, and it leaves no way to tell what helped.
What we don't do
We don't sell AI as a layer that fixes a broken process. If your data is disorganized, a model will produce faster wrong answers, and you will pay for the speed. When the data is the real problem, we fix the data first and say so before any model gets built.
We also don't promise specific outcomes from AI features. Model behaviour depends on your data, your users, and the task itself. What we can do is show you how we would measure success, and put that in writing before the build starts — the metric, the test set, and the point at which we would call the approach a failure.
In writing, before build: the success metric, who reviews outputs, what data the feature may read, and the result that would make us recommend stopping.
How engagements usually start
Most AI and cloud work starts with a short assessment: a few calls and a written recommendation. If the recommendation is to build, we scope the build. If it is to wait, or to do something simpler first, we tell you that too. From there, projects usually run in phases, with a small first phase proving the approach before anything expands.
A small first phase, defined before build
If the recommendation is to build, we scope that build: what ships in the first phase, what is deliberately left out, and how we will judge whether it worked.
A working feature, not a strategy deck
The first phase produces something your team can use, on real data, so the decision to continue is based on what the feature did rather than on how the pitch sounded.
The next phase is a separate decision
If the first phase worked, we widen the scope. If it did not, we either change the approach or stop. Either outcome is a usable result, and it arrives early enough to be cheap.
Questions we get before the first call
If your situation is not covered here, describe it in a message and we will answer directly rather than sending a brochure.
Discuss an AI use caseDo we need our data organized before starting AI work?
Usually yes, for anything involving internal documents or reporting. If the data is messy, we start with cleanup, which is often the most valuable part of the project anyway. It is also the part that makes every later feature cheaper to build.
Can AI features run entirely on our own infrastructure?
Yes, with open-source models. The tradeoff is usually more setup and lower output quality than a hosted model, but for some privacy requirements that is the right call. We will price both routes so you can see the difference before choosing.
What cloud platforms do you work with?
Primarily Google Cloud and AWS, with support for Azure where clients already use it. If your team has existing platform knowledge, we work inside it rather than moving you for the sake of a cleaner diagram.
How do you measure whether an AI feature is working?
We agree the metric before build: time saved on a task, share of outputs accepted without edits, or the rate at which a person has to correct the model. We also agree a sample of real cases to test against, so the judgement does not rest on a demo.
Nevotop Digital · Tbilisi
Start with the assessment, not the roadmap
Tell us what the workflow looks like today and where the time goes. We will come back with a written view on whether AI or cloud work would help, what a first phase would involve, and what we would measure. Reach us Monday to Friday, 9:00 AM – 6:00 PM, at kanogml1201@gmail.com or +1 (815) 934-4003.