How accurate will the assistant be?

We do not promise a number before we have seen your data. In Discover we build a small evaluation set from real cases and measure the first prototype against it. That gives you an honest baseline, and every later change is tested against the same set. Where accuracy is not good enough, a person stays in the loop.

Do we need a data warehouse before we start with AI?

Usually you need something smaller: a clean, documented copy of the data the use case depends on. That is often a few tables in PostgreSQL with a pipeline that keeps them fresh. We build the warehouse only when several teams need shared reporting, and we tell you which case you are in during Discover.

What does an AI project cost to run each month?

It depends on volume, and we design it to stay predictable. LLM calls are metered, so we measure cost per request in Build, cache what repeats, and use smaller models where they are good enough. You get a monthly usage report per feature. Hosting follows the same cost control we apply to any cloud project.

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Service · AI & Data

AI solutions for business, built on your own data

LLM assistants, document automation, data pipelines and dashboards that answer questions from your own data. Built by senior engineers in Sarajevo, with humans in the loop and results you can measure.

Start a project How we work

Most AI projects fail for a boring reason: the data was not ready, nobody defined what a correct answer looks like, and the demo never met a real user. We start from the other end. First the data, cleaned and connected. Then a clear measure of what good looks like. Then a model, often a small one, with a person checking the output wherever a mistake would cost money.

Elevate Solutions builds AI solutions for business from Sarajevo for companies in Bosnia, Germany and the EU, with senior engineers only. We ship AI in our own products too: DesAIgner turns a one-sentence idea into planned screens, flows and assets, with code export to HTML/CSS and React + Tailwind, and Figma import. We know where LLMs are reliable and where they are not, and we design around both.

What we build

We build three kinds of things, usually in this order: the data foundation, the analytics on top of it, and the AI that only works once the first two exist. Python for pipelines and model work, LLM APIs rather than training from scratch, PostgreSQL as the system of record. Every assistant we ship has an evaluation set, a confidence threshold and a human path for the cases it should not decide alone.

LLM assistants over your documents, tickets and knowledge base, with sources cited in every answer
Document automation: extracting and validating invoices, orders, contracts and forms before a person reviews them
Data pipelines that collect, clean and join data from ERP, CRM, web and spreadsheets into one warehouse
Dashboards and analytics on the company's own data, with definitions the whole business agrees on
Evaluation and monitoring for models in production, so quality is measured, not assumed
Integration of AI features into the software you already run

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Triage the inbox

The model files what it is sure about. You decide the rest.

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How we work

AI and data projects follow the same four phases as everything we do, with one extra rule: data quality comes first. Discover takes 1 to 2 weeks: workshops to pick the use case with a measurable payoff, a look at the real data, a written brief and a fixed-price estimate for the next phase. Design covers the architecture, the data model, the evaluation criteria and the roadmap, validated with you before we build. Build runs in sprints with a weekly demo on your own data, direct communication with the engineers and no black boxes. Elevate starts at launch: we monitor accuracy, cost per request and drift, and keep improving on a roadmap we review with you. The team stays after go-live.

Discover. Workshops with your team to define scope, risks and what success means. You get a written brief and a fixed-price estimate for the next phase.

What you get

You get a system that answers from your data and shows its work, plus the pipelines and dashboards that make the data trustworthy in the first place. Models, prompts, evaluation sets and data live in your accounts. You can replace us, or the model vendor, without starting over.

A written use-case brief with the expected payoff and the evaluation criteria agreed up front
Pipeline code, warehouse schema and dashboards in your repository and your cloud
An evaluation set and a quality report before launch, re-run on every change
Human review paths for low-confidence outputs, built into the workflow
Cost and usage monitoring per feature, so AI spend stays predictable
Support and maintenance retainers for ongoing tuning, if you want them

Who this is for

This service fits companies that have data but no answers, or that spend people-hours on reading and re-typing documents. It is a poor fit for teams looking for an AI badge without a use case, and we will tell you that in the first week.

Companies in Bosnia, Germany and the EU with data spread across ERP, CRM and spreadsheets
Finance, operations and legal teams processing documents by hand
Businesses whose management reports take days to assemble and still disagree
Product teams adding an assistant or search to an existing application

Technologies we use

Python
LLM APIs
PostgreSQL
data pipelines
dbt
pgvector
Azure
Docker

Frequently asked questions

It can be, if the architecture is set up for it. We use providers that do not train on your data, keep the data in your own cloud region, and send the model only what each request needs. Sensitive fields can be masked before they leave your systems. We write this down in Design, before anything is built.

Service

Tell us which question your data should answer.

Send a short brief and a sample of the documents or data involved. A senior engineer replies within one business day.

Start a project

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