Most AI projects die in the gap between demo and dependable.

We close it. Assessment, prototype, production system - built with your team, not handed over the wall. Then we hand you the keys and teach your people to drive.

Book a working session See how we work

Four stages. You can enter at any one of them.

Select a stage

Stage 01 - Assess

An honest read on what AI can and can't do for you.

Two to four weeks inside your product and processes. We map where intelligence actually pays, model the ROI, design the architecture - cloud-native, hybrid or on-prem - and tell you plainly which ideas to drop.

    Six ways in. One map.

    Every route connects to the same thing - your product. Pick the one that matches where you actually are; most engagements start with one and grow into the others.

      Deep expertise - generative AI & knowledge

      RAG done properly: advanced retrieval across single and multi-index solutions, hybrid Graph RAG with natural-language-to-SQL, semantic search, and evaluation you can trust.

      • RAG
      • Knowledge graphs
      • LLM integration
      • Graph RAG
      • NL→SQL
      • Semantic search
      • Vector DBs
      • Embeddings

      Deep expertise - autonomous systems

      Agents that reason, learn and act: single and multi-agent architectures, orchestration and coordination design, and tool boundaries that keep autonomy useful and safe.

      • AI agents
      • Multi-agent
      • Orchestration
      • Coordination
      • Agentic networks
      • Decision systems
      • Tool design
      • Autonomous workflows

      The product shelf. Three, so far.

      Every engagement teaches lessons; the ones that kept repeating became products. Pick one off the shelf - the dossier stays short here, because each product runs on its own site with its own demo.

      Lesson № 01 - the hard part isn't the model; it's the gap between the machine finishing and the human deciding.

      We built the platform we kept wishing existed.

      Lyhnis is one execution layer for workflows, agents and the people who sign off on them. Author on a canvas or in Python, pause for a human - four minutes or four weeks - and resume exactly where you stopped, with the whole run signed.

      • Python + visual authoringCanvas, JSON or code - three renderings of one graph.
      • MCP in both directionsAgents call your workflows; workflows call agents.
      • Durable human-in-the-loopA pause is state, not a sleeping thread.
      • Audit by constructionInsert-only, signed, exportable.
      Visit Lyhnis Book a demo

      Lesson № 02 - the model was rarely the bottleneck; the representation was.

      The EU AI Act, as a graph an agent can reason over.

      ACTLAS turns dense regulation - the EU AI Act and GDPR today, NIS2 and DORA next - into a typed knowledge graph, then lets an agent reason over it: classify a system, derive the obligations that attach, and hand back the exact path it walked. It assembles the evidence and cites the source; it never concludes "compliant".

      • Typed legal knowledge graphEvery cross-reference a directed, trust-labelled edge.
      • Agentic reasoning, shownFeature → regime → obligation, with the path handed back.
      • Spec linterMap a system onto the law; see the obligation coverage gaps.
      • Provenance-firstVerbatim law vs. editorial layer - evidence, never a verdict.
      Visit ACTLAS Book a demo

      Lesson № 03 - the data was always there; nothing could ask it properly.

      The intelligence layer your recruitment stack is missing.

      Personode sits on top of the ATS, CV database or hiring workflow you already run and turns it into a living candidate graph - one that reads a CV the way your best recruiter does: how strong each skill is, for how long, how recently, and the career story between the lines.

      • A living candidate graphTyped relations carry level, years and recency - not keywords.
      • Answers that show their workGraph + semantic ranking, with the full agent trace attached.
      • Team fit from behaviourWorking-style clusters you can place a new CV into.
      • An engine, not another systemYour ATS stays the record - on-premises, cloud, or both.
      Visit Personode Book a demo

      Teach a team properly and you work yourself out of a job.

      Which is rather the point. Hands-on training written out of delivery work rather than a curriculum.

      AI Upskilling

      upskilling.adoptintelligence.com

      Expert-led, hands-on modules for developers, architects and enterprises. Frameworks and stack flex to your reality.

      • M1RAG Systems - Azure & open-source knowledge systems
      • M2AI Agents - design & orchestrate autonomous systems
      • M3AI Tooling - the SDLC, accelerated
      • M4AI for Marketing Teams - campaigns, content and analysis that ship
      • M5AI in Sales - qualification, outreach and pipeline hygiene
      • 1:1Tailored programmes - individuals and enterprise teams
      Explore training programmes

      The person you'll actually be working with.

      Dr. Miodrag Cekikj, Founder and AI Innovation Lead at Adopt Intelligence

      Dr. Miodrag Cekikj

      Founder & AI Innovation Lead

      PhD in Applied AI

      LinkedIn

      “I've spent years watching brilliant products die in the gap between what AI can do and what users actually need. That gap isn't technical - it's vision.”

      We don't just advise, we build with you. Every product has untapped intelligence waiting to be unlocked. My job is to co-create it, ship it, and help you own the market before someone else does.

      On stage

      Conference keynotes, enterprise workshops and technical communities across Europe - Data Science Conference, AI Tech Summit, WeTalkData, What The Stack and more.

      Tell us what's stuck.

      One conversation, no deck. If we're not the right partner we'll say so and point you at who is.

      hai@adoptintelligence.com