We help enterprises adopt AI faster and smarter.

We have helped many businesses find where AI adds real value, then scale it across the whole organization.

The IH-Tech mark as a glass sculpture: four interlocking modules, two in cobalt and two in frosted glass.

Remote-first from Sarajevo, on our clients’ working hours.

  • European Union
  • United States and Canada
  • Middle East

Expertise

Five roles we get called into

Every role is filled by AI experts. We keep the team small on purpose, with no bench and no juniors billed as experts.

Talk to us about a role
01

Technical advisory

We take the senior seat on decisions that are expensive to get wrong: feasibility, architecture, and sequencing. Your team leaves with an answer it can act on.

  • Technical due diligence and codebase assessment
  • Architecture and feasibility review before commitment
  • Phased engagement design and presales for AI-enabled delivery
02

Embedded teams

AI experts join your program and work in your backlog, to your standards. They have done the hard version before, and they are accountable for shipped work.

  • AI experts inside your existing delivery team
  • Modernization and cloud work carried with your team, on Azure or AWS
03

Team enablement

The aim is for your engineers to do this work without us. We run the workshops, set the standards, and put in the evaluation practice that makes AI-assisted delivery measurable.

  • AI SDLC workshops and hands-on training
  • Claude Code and coding-agent adoption programs
  • AI proficiency standards and evaluation practice
04

Agentic products

We design agent systems with separate planning, implementation, and review roles, connected to the systems a business already runs. Discovery runs in days.

  • Subagent systems, orchestration workflows, and MCP integrations
  • Fast agentic discovery, then proof of concept through to production
  • Proofs of concept on current agent frameworks and tools
05

Platforms and tooling

The biggest gain is often a tool nobody has built yet: migration tooling, evaluation platforms, and internal assistants connected to systems a company already pays for.

  • GenAI migration CLIs and code-transformation tooling
  • Evaluation platforms for model and agent quality
  • Internal AI platforms, analytics assistants, and CRM integrations
  • Metadata-driven no-code engines and low-code platform architecture

Approach

Four stages, from first conversation to production

  1. 01

    Assess

    Before any roadmap, we read the code, the pipelines, and the tickets with the discipline of a due diligence review. The plan rests on what is in the codebase.

  2. 02

    Prove

    Agentic discovery runs against your real domain. The proof of concept uses your data and your constraints, and it is fast enough that being wrong is cheap.

  3. 03

    Evaluate

    We measure before we scale. An independent reviewer checks agent output against an agreed standard, because a common failure in AI delivery is confident output that nobody checked.

  4. 04

    Hand over

    We take the work to production with your team able to run it. Standards, tooling, and training stay with you. A small agency that becomes load-bearing is a risk to its client, and we would rather be hired again than depended on.

Production readiness

From a vibe-coded prototype to a production system

AI tools get a prototype working fast. Then the team hits the same wall: nobody can change it safely. There are no tests and no module boundaries, secrets sit in the client, nobody chose the dependencies, and a coding agent will make all of it worse at speed. That is when we step in.

We keep the codebase you have and make it safe to change. That is almost always the fastest path to production.

Get it shipped
  1. Security before launch

    Secrets out of the client, authentication and authorization that hold, input validated at the boundary, and a dependency tree somebody has reviewed.

  2. Made safe to change

    Real module boundaries, typed contracts, and dead paths deleted. That is the difference between a codebase a team can extend and one it is afraid of.

  3. Tests where they pay

    Coverage goes on the paths that lose money or data. Each test is written failing first, so it is proven to work.

  4. Operable in production

    Environments, migrations, observability, a rollback that has been rehearsed, and a bill you can predict.

  5. AI practice that keeps it clean

    Agent standards, review gates, and evaluations, so the next feature built at speed does not quietly undo this work.

Stack

What we work in

Agents and SDKs

  • Claude Agent SDK
  • Claude Code
  • MCP
  • Subagent systems
  • Agent orchestration
  • Tool and function calling

Models and retrieval

  • Frontier models
  • Open-source models
  • Local and self-hosted inference
  • RAG
  • Embeddings and vector search
  • Evaluation harnesses

Platform

  • .NET
  • TypeScript
  • Next.js
  • Microservices
  • Temporal

Cloud and delivery

  • Azure
  • AWS
  • Azure DevOps
  • GitHub Actions
  • Terraform

Most of the stack comes from the client. These are the tools that come up most often.

Regions

Remote-first, across three regions

Our clients are in the European Union, the United States and Canada, and the Middle East. We work remotely from Sarajevo, on each client’s working hours.

  • European Union

    Full working-day overlap from Central European Time

  • United States and Canada

    Morning-to-afternoon overlap across Eastern and Central

  • Middle East

    Near-complete working-day overlap with the Gulf

Insights

Field guides for engineering leaders

Each guide takes one decision a technical leader has to make, gives the answer we would give in the room, and names the conditions that change it.

All field guides

FG 00219 min read · Updated 16 Aug 2026

Redesigning the engineering ladder for AI-native teams

A custody-based leveling and hiring framework: levels defined by what an engineer can be accountable for, a standardized assessment cockpit, and a protected formation budget that keeps the junior-to-senior pipeline alive.

Read the guide

FG 00114 min read · Updated 16 Aug 2026

Modernizing legacy systems with AI agents

A control-loop strategy for AI-agent modernization: structure first, plans as reviewed prompts, machine-generated parity evidence, and a clear fit test.

Read the guide

Contact

Tell us what is stuck.

Due diligence on an acquisition target, an AI program stalled at proof of concept, a modernization your team is carrying without senior cover: these are the conversations where we help most. We are a small team and we turn down work we would do badly. Write to us and we will tell you plainly whether we are the right fit.

Let’s talk info@ihtech.ba

Sarajevo, Bosnia and Herzegovina