AI & Software Engineering
The practice that turns AI ambition into working systems — from strategy to production, and every step after.
What this practice is, in two minutes.
Most organisations know they need AI. Very few know where to start, which tools to pick, or how to make the results stick. Our practice exists to answer those three questions — first with consulting, then with engineering, and finally with the operation of the AI systems we build, so they keep delivering value long after the first release.
We work across four connected layers. AI consulting identifies where your business will actually benefit from AI and proves it quickly. Custom AI engineering builds the assistants, agents, and retrieval systems grounded in your own data. Software engineering connects AI into the systems your business already runs on. Data foundations — warehouses, lakehouses, pipelines, governance — make everything above them possible. Once those systems are live, we stay with them: monitoring, optimising, and governing your AI and agentic systems as a managed service, so the value compounds instead of decaying.
In plain terms, most of what we deliver is automation — work that humans used to do by hand, now done faster, more consistently, and at scale by AI assistants, AI agents, and the software around them. The technical vocabulary changes; the underlying business outcome rarely does.
Our vendor approach is deliberately four-tiered. Open source for foundations — practically free, lowers upfront investment, perfect for proving the case. Pay-as-you-grow platform partners — cloud AI services where you pay for what you use. Established enterprise vendors — the mainstream choice when scale, support, and compliance matter. Premium top-of-market options — when the use case justifies the investment. We will recommend what fits your maturity and your budget, not what fits our commercial incentives. Vendors change fast; the right answer today is not the right answer in eighteen months. Our job is to know the difference, and to make sure our clients are never stranded on a single bet.
Proof before production. Always. We start small, prove the case, and scale what works.
Nobody believes in AI on our word alone, and nobody should. Trust and appetite for AI are built one proven outcome at a time. That is why we design our first engagements to be small — small scope, small cost, short timeline, real results. If the pilot works, we scale it. If it does not, we have saved you from a much more expensive mistake. This approach means we can work with any serious organisation — the largest banks and ministries we have served for twenty-five years, and the mid-sized or smaller company taking its first steps with AI. Willingness matters more than size. We will find a way to make it affordable.
We are a true system integrator. We have worked with infrastructure, networks, cybersecurity, cloud, and enterprise systems across almost every major industry in our region for a quarter of a century. We are candid about what that means for AI: the infrastructure expertise lives primarily in our sibling practice, and end-to-end delivery — cloud, hybrid, or fully on-premises — is available to you through a single relationship with us. This practice is about the AI itself: the consulting, the engineering, the data, the software, and the ongoing operation. The integrator heritage is the reason it all holds together.
We do not aim to lock clients in. We aim to earn the right to stay by delivering.
At a glance
AI Consulting Practice
A structured methodology for identifying high-ROI AI opportunities, prototyping them in weeks, and putting them into daily use.
Proof before production
Every engagement starts small — small scope, small cost, short timeline, real results. If the pilot works, we scale. If it does not, we have saved you a more expensive mistake.
Four-tier vendor choice
Open source, pay-as-you-grow platforms, established enterprise vendors, and premium options. The right tool for your maturity, your scale, and your budget.
Managed AI operations
We do not just build and leave. We monitor, govern, and optimise your AI and agentic systems as a managed service, so value compounds over time.
Any client, any size
The largest banks and public institutions in the region for a quarter of a century, and the mid-sized or smaller organisation taking its first AI steps. Willingness matters more than size.
End-to-end through one partner
The AI itself sits here. Infrastructure, cybersecurity, and OT sit in our sibling practices. Cloud, hybrid, or fully on-premises delivery, through one relationship.
Why most AI projects fail — and why ours reach production.
Five layers, one connected practice.
Most clients start with one capability and grow into the others as confidence and ambition build. The layers are designed to reinforce each other.
AI Consulting
Our AI Consulting Practice is the entry point for most of our AI engagements. It exists to answer a simple question: where, in your organisation, will AI create the most value in the next three to six months, and how do we prove it quickly?
A typical engagement begins with a discovery workshop — one or two days with your management team — to map current processes, identify friction points, and produce a prioritised list of AI opportunities ranked by business value and implementation effort. We then design a pilot that can be deployed in four to eight weeks using public, ready-to-use AI tools, and we measure its effect with hard numbers: time saved, error rates, cycle time, customer satisfaction, deal conversion.
The consulting practice is deliberately vendor-neutral. We work across the full set of frontier model providers — OpenAI, Anthropic, Google Gemini, Perplexity, Mistral — alongside the established enterprise platforms from Microsoft, Google, and IBM. We will recommend the model that fits the use case, the data sensitivity, and the budget — not the model that fits a single ecosystem. We do not sell hours; we sell outcomes.
Our methodology works the same way for the largest enterprise and for the mid-sized or smaller company taking its first AI steps. We adjust the language, the entry point, and the pilot scope to match where you are today. The discipline — discover, prove, build, adopt, operate — stays the same.
Typical first-engagement use cases
- Contract analysis, redlining, and clause extraction for legal teams
- Proposal generation and RFP response assistance for sales teams
- Customer support assistants grounded in internal knowledge bases
- Meeting summarisation and action-item extraction across Teams, Zoom, and Google Meet
- Document review and intelligent data extraction from unstructured inputs
- Internal research and knowledge retrieval across SharePoint, Confluence, and document management systems
- Financial reporting and narrative generation for finance teams
Custom AI Engineering
Consulting opens doors. Engineering keeps them open. When a client sees value from a first AI engagement, the conversation moves naturally to custom work — retrieval systems grounded in proprietary knowledge, AI agents that take autonomous action inside business workflows, computer vision pipelines for physical environments, and fine-tuned models for regulated or sensitive domains.
Our AI engineering team builds the following, with production rigour, not demo-grade shortcuts:
AI Assistants. Conversational interfaces grounded in your data. We design retrieval architectures using vector databases, hybrid search, and re-ranking; integrate with enterprise identity systems; and enforce access controls so users only see what they are authorised to see. We work across Azure OpenAI and Azure AI Foundry, Google Vertex AI, IBM watsonx, and open-source models deployed via NVIDIA NIM, vLLM, Ollama, or similar runtimes — choosing the model family that fits the use case, the data sensitivity, and the budget.
AI Agents. Systems that go beyond responding to requests and take action on them — automating multi-step business processes by triggering workflows, querying APIs, writing to systems of record, and coordinating across applications. This is what most clients mean when they say they want "AI automation": agents replacing manual work that used to require people clicking through multiple systems. Agent engineering demands rigorous attention to tool use, memory, evaluation, guardrails, and human-in-the-loop escalation where full autonomy is not yet appropriate.
Retrieval-Augmented Generation (RAG). The default pattern for grounding AI in proprietary content. We design and operate end-to-end RAG systems: document ingestion, chunking strategies, embedding model selection, vector storage, retrieval and re-ranking, response generation, evaluation harnesses, feedback loops, and continuous improvement. We build for production, not for screenshots.
Computer Vision and Visual AI. A capability we are deliberately investing in, with strong use cases in sectors we have served for a long time — manufacturing, public infrastructure, smart cities, security, energy, and logistics. Typical applications include automated quality inspection on production lines, drone-based inspection of critical infrastructure, perimeter and crowd security, asset tracking, and traffic and incident monitoring. Computer vision is delivered jointly with our Advanced OT Solutions practice: they own the physical-world layer (cameras, drones, sensors, edge compute); we own the model layer, inference pipelines, and integration into business systems.
Conversational BI and analytics agents. Asking your data questions in plain language and getting accurate answers, charts, and follow-ups. Numerical hallucination is the real risk — a confident wrong number is worse than no answer. Our approach pairs the conversational layer with a properly engineered semantic layer (see capability 04 below), uses constrained NL-to-SQL with validation rather than free-form generation, and grounds every response in the underlying query so the user can see how the number was produced. We work across Microsoft Fabric and Power BI Copilot, Google Looker with Gemini, and IBM watsonx with Cognos. We are honest about maturity: a strong pilot capability becoming production-ready in narrow scopes, not a turnkey replacement for a BI team.
Fine-tuning and custom models. For use cases where public models are insufficient — regulated industries, specialised domains, sovereign data requirements — we fine-tune open and frontier models on client data. We have ML engineers and ML architects on staff, and we invest in this capability because in the long run, fine-tuning and prompt engineering will determine who wins in enterprise AI.
MLOps and LLMOps. Production AI systems need the same discipline as any other production software: version control for models and data, automated evaluation, drift detection, cost monitoring, prompt management, regression testing, and rollback.
Software Engineering
AI is not a standalone discipline. It is a layer inside software systems, and software is the connective tissue of every serious enterprise. Our software engineering capability exists because AI that cannot connect to the systems your business already runs on creates no business value.
We build custom applications, modernise legacy systems, design and implement integrations, and operate software in production. Our teams are fluent in modern web and mobile stacks, API-first architectures, event-driven systems, containers, and cloud-native deployment. We work across .NET, Java, TypeScript, Python, and Go, and we adopt AI-native engineering practices internally — our engineers use AI-assisted development tools every working day.
We treat software engineering as an integration discipline. Most of our custom development engagements are not greenfield product builds; they are systems that extend, replace, or connect to existing enterprise software — ERP, CRM, ticketing, document management, industry-specific line-of-business systems.
- Custom business application development — web, mobile, backend
- Legacy system modernisation and replatforming
- API design, system integration, and middleware
- Event-driven and microservices architectures
- Cloud-native development on Azure, Google Cloud, and AWS
- AI-native development practices — in-IDE AI assistants, automated code review, generative testing
- DevOps, CI/CD, observability, and site reliability engineering
- Open-source contribution — we build on open source and give back where it makes sense
Data Foundations
Every meaningful AI system is built on data. Our data practice closes the loop between the AI above and the infrastructure that serves it, and it is often where our engagements begin when the AI use case is clear but the data underneath it is not.
Data warehouses, lakehouses, and open alternatives. Established commercial platforms when scale, support, or integration demand it — Microsoft Fabric, Google BigQuery, IBM watsonx.data, and similar. Open-source foundations when the priority is lowering upfront investment while proving the case — Apache Iceberg, Apache Hudi, Trino, Apache Spark, DuckDB, and the broader open analytics stack. We will recommend the combination that fits your maturity, your data volumes, and your budget, not the combination that fits a single vendor roadmap.
Business intelligence and analytics. Dashboards, reports, KPI frameworks, and the semantic layer underneath them. We work across Microsoft Power BI, Google Looker, IBM Cognos, and open-source platforms including Apache Superset and Metabase. The semantic layer is what we focus on most — clean dimensions, agreed definitions, validated measures, lineage back to source. It is the difference between an organisation where every team disagrees on the numbers and one where everyone works from the same truth. It is also the foundation that makes conversational BI in the AI layer above actually work.
Data integration and pipelines. Real-time and batch ETL and ELT, change-data-capture, event streaming, and data quality monitoring. Commercial tooling when you need it (Azure Data Factory, Google Dataflow, IBM watsonx.data integration, Fivetran), and open-source alternatives when you do not (Apache Airflow, dbt, Apache Kafka, Debezium).
Data governance. Policies, catalogues, lineage, access control, quality rules, and GDPR and sector-specific compliance. Governance is not paperwork. It is the layer that decides whether your AI can be trusted with decisions that matter.
Data spaces. We participate in the European data spaces ecosystem through partnership with the GATE Institute in Sofia and engagement with the International Data Spaces Association. For clients operating in regulated sectors or participating in EU-funded programmes, data spaces architectures — based on IDS and Gaia-X — are becoming the standard pattern for sovereign, governed data sharing.
Managed AI Operations
Building an AI system is the beginning, not the end. Most of the value — and most of the risk — lives in the months and years after launch. Models drift. Data changes. New regulations arrive. Costs creep. Users find gaps that were invisible on day one. Prompt injection attempts appear. Without active operation, the system that delivered value in month one quietly decays into the system that nobody trusts in month twelve.
Our managed AI operations offering exists so this does not happen. We stay with the systems we build — or take over systems others have built — and run them as a managed service with a clear service-level agreement, a defined change-management process, and transparent reporting on performance, cost, compliance, and security posture.
Security sits alongside operation as a first-class concern. AI systems have a new and fast-evolving threat surface: prompt injection, jailbreak attempts, sensitive-data leakage through model outputs, model extraction, data poisoning, and the anomalous usage patterns that indicate either misuse or compromise. Our operations practice deploys continuous guardrail enforcement on model inputs and outputs, anomaly detection across usage telemetry, and automated response to policy violations — combining platform-native AI security controls from our cloud partners with open-source tooling and our own runbooks.
What we operate on your behalf:
- AI assistants and AI agents — prompt management, evaluation, guardrail tuning, user feedback loops
- Retrieval-augmented generation systems — document freshness, retrieval quality monitoring, index maintenance
- Fine-tuned and custom models — drift detection, periodic retraining, A/B evaluation
- Agentic AI systems — orchestration, tool-use monitoring, error recovery, human escalation paths
- AI security operations — input/output guardrails, prompt-injection and jailbreak detection, sensitive-data leakage prevention, anomaly detection on usage telemetry, red-teaming schedules
- Cost optimisation across cloud AI services, token spend, and inference infrastructure
- AI governance operations — audit logs, compliance evidence, policy enforcement, incident response
- Continuous improvement — identifying the next use case from the usage patterns of the current one
You can engage us to co-operate systems alongside your team, to operate them entirely as a managed service, or to advise on the operations your team will run itself. All three models are in production with our clients today.
Multi-vendor by design.
We do not build on a single vendor's platform and call it neutrality. We work across four tiers — open-source foundations, pay-as-you-grow cloud AI platforms, established enterprise vendors, and top-of-market premium options — and we certify our people across all of them. Clients get recommendations based on their specific maturity, scale, and budget — not on our commercial incentives.
The partners below are public. Several additional partnerships across the AI and data ecosystem are in the process of being formalised, and will appear here as they go live.
Where most of our engagements begin. Open-source foundations let us prove the case without heavy upfront investment, and keep serving as components of production systems at any scale. Model families including Llama, Mistral, and Qwen, deployed via NVIDIA NIM, vLLM, and Ollama. Open data and analytics stacks — Apache Iceberg, Apache Spark, Trino, DuckDB, Apache Airflow, dbt, Apache Kafka. We contribute back where our work aligns with the community. For most clients, the natural next step from open source is one of the pay-as-you-grow platform partners below.
The most natural next step from open-source foundations for many of our clients — pay-as-you-grow consumption with no large upfront commitment. Building certified expertise on Google Cloud AI: the Gemini family of models, Vertex AI, Google AI Studio, and the Google Cloud Professional Machine Learning Engineer track. We run active proof-of-concept projects on Gemini and choose Google when the client wants to move quickly without an existing enterprise-platform commitment elsewhere.
A vendor we have worked with for many years and where our certified team is largest. Microsoft Solutions Partner across Data & AI, with active delivery experience on Azure OpenAI, Azure AI Foundry, Microsoft Fabric, Microsoft 365 Copilot, Copilot Studio, and Azure AI Search. The right starting point for clients with an existing Microsoft estate or a strong Microsoft preference.
The natural choice when on-premises deployment is the requirement, although IBM also offers a public cloud option. IBM watsonx for regulated-industry AI, IBM Granite open models for enterprise use cases, IBM watsonx.data for unified structured and unstructured analytics, IBM watsonx.data integration for data pipelines, and IBM's governance tooling for AI risk management. Strong fit for public sector, financial services, defence, and critical national infrastructure where data must stay inside controlled environments.
Partner across both NVIDIA hardware and NVIDIA software. The hardware layer is the familiar one: GPU compute for training and inference, and edge AI on NVIDIA Jetson and IGX platforms. Equally important is NVIDIA's software stack — Nemotron open models, NIM microservices for production model serving, NeMo and NeMo Guardrails for building and safeguarding custom AI, TensorRT-LLM and Triton for high-performance inference, and frameworks like RAPIDS for GPU-accelerated data science. An unusual combination for an enterprise hardware vendor: much of what they produce is open and freely available. We take advantage of that on our clients' behalf.
Beyond the formal partnerships above, we deliver on the major frontier model platforms — OpenAI, Anthropic, Perplexity, Mistral, and others — through direct client subscriptions. Model selection is driven by use case, output quality, latency, data sensitivity, and budget, not by which provider gives us the best margin.
Additional partnerships across lakehouse platforms, enterprise language-model providers, data integration vendors, and niche AI tooling are in various stages of formalisation. The list here is a floor, not a ceiling — if the technology you want to use is not listed, ask us.
End-to-end through one relationship.
This practice is about the AI itself — the consulting, the engineering, the data, the software, and the ongoing operation. Two sibling practices sit alongside it, and together we cover every layer a real AI programme needs. A single client relationship. One commercial conversation. One integrated delivery team.
That matters because the gap between a pilot that worked in a cloud sandbox and a production system the business depends on is almost always somewhere outside the AI model. It is in the network fabric, the identity system, the sovereignty constraint, the factory-floor camera, the edge device in a remote location, or the compliance boundary that rules out a public API call. We can deliver across all of it — cloud, hybrid, or fully on-premises — through one partner.
IT Infrastructure & Cybersecurity
Our sibling practice delivers the compute, networking, storage, identity, and security layer that production AI depends on — across every deployment mode. Public cloud, private cloud, hybrid, and fully on-premises; we are not a cloud-only shop and we are not an on-prem-only shop. GPU infrastructure, high-speed interconnects, sovereign deployment options, confidential computing, zero-trust identity, and the cybersecurity discipline that AI systems now require — from prompt-injection resistance to model supply chain risk. When the AI programme calls for any of this, we bring it in from day one.
Advanced OT Solutions
Our OT practice delivers the physical-world layer — industrial automation, operational technology, sensors, cameras, drones, and edge compute on the factory floor or in the field. When AI meets the physical world — computer vision on a production line, predictive maintenance on rotating equipment, drone-based inspection of infrastructure, perimeter security, real-time location services across a logistics facility — the two practices deliver together as one team.
Managed Services & Operations
Our group-wide operations capability runs the systems we build — across AI, software, infrastructure, and OT — to defined service levels, with a single point of accountability. If you want one number to call when something breaks, and one integrated team watching the whole stack, this is where it lives.
Compliance and AI governance.
AI governance is no longer optional. The EU AI Act is entering enforcement phases. NIS2 has expanded the scope of security obligations. GDPR continues to shape how personal data flows through AI systems. Sector-specific regulation is tightening across financial services, healthcare, and the public sector.
We work closely with our Governance, Risk and Compliance team to help clients navigate this landscape. The areas we advise on most often:
EU AI Act readiness
Classification of AI systems by risk tier, risk management processes, documentation, conformity assessment, and post-market monitoring.
NIS2 compliance
For AI systems that fall within essential or important entity scope. Mapping operational obligations to existing security and incident response practice.
Data governance for AI
Training data provenance, inference data retention, consent management, data minimisation, and GDPR alignment for AI workloads.
AI transparency & explainability
For high-risk and regulated use cases. Documentation, model cards, decision logs, and the evidence trail an auditor will actually ask for.
AI security posture
Prompt injection resistance, model access control, audit logging, red-teaming schedules, and alignment with NIST AI Risk Management Framework.
Internal AI usage policies
Acceptable use frameworks, staff guidance, role-based access controls, and the change-management work that makes policies stick.
We are a technology partner, not a law firm, and we do not provide legal opinions. We translate regulatory requirements into technical architecture, operational practice, and the documentation that an auditor or regulator will actually ask to see.
One thing we want to be honest about up front. AI readiness, NIS2 readiness, and regulatory alignment are not products we can hand over in a box. They are processes, procedures, and operational decisions that only you can own — because they describe how your organisation actually works, and only your organisation can change that. Our role is to guide you, advise you, design the technical architecture that supports compliance, and stand alongside you through the work. We are deliberate about this. There are providers in our region who will sell you a certificate in two weeks with no real organisational change behind it; we will not. We believe the certificate is worth what the change behind it is worth, and our methodology is built on that conviction.
Compliance is not something we deliver in a box. The processes, the procedures, the decisions — these belong to you, and they must. Our job is to guide, advise, and stand alongside you. The heavy lifting will always be yours.
We know the environments we build for.
We are honest about this. We are not claiming deep business-process expertise in banking, telecommunications, or any other vertical. That level of process fluency lives with our clients — and it always will.
What we do claim — and what twenty-five years of serving them earns us the right to claim — is a working understanding of how these organisations actually operate. Their regulatory reality. Their security posture. Their tolerance for change. The systems they already depend on. The politics of procurement, the patience of their operations teams, the realities of their budgets. You cannot succeed at delivering infrastructure and cybersecurity for a quarter of a century without learning how your clients' organisations work. We have learned.
That matters for AI, because the AI programmes that fail are almost always the ones that ignored these realities. The ones that succeed are the ones where the technology partner knew, from day one, what the client's environment would and would not tolerate.
One commitment we want to be explicit about: clients will always be more fluent in their own business than we are, and that is correct — they should be. But we treat every engagement as an opportunity to go deeper. AI and data work pulls us closer to the heart of how a business actually runs than infrastructure work ever did, and we welcome that. The depth of our understanding will grow with every engagement; we expect it to, we plan for it, and we are perfectly comfortable with the fact that knowing more is always part of the job.
Where we have been working for the past twenty-five years.
We have delivered end-to-end across banking and financial services; central government institutions including defence, interior affairs, education, agriculture, transport, culture, tourism, sport, and labour; e-government platforms; healthcare; pharmaceuticals and chemicals; oil and gas; telecommunications; manufacturing; transport, logistics, and air traffic management; utilities and energy; retail and distribution; and media organisations including national radio and television broadcasters. Within each of these sectors we have worked with some of the largest institutions in our region, and we continue to. The client types we know best are the ones where uptime matters, regulators are watching, data sensitivity is real, and operational discipline is non-negotiable. Those are the environments where AI — done properly — creates the most durable value.
Smaller and mid-sized organisations
Our traditional clients have been among the largest institutions in the region. That is changing. The economics of AI — especially when built on open-source foundations and pay-as-you-grow platforms — make meaningful AI programmes accessible to organisations of almost any size. If you are a smaller or mid-sized organisation and you have been hesitant to approach a firm like ours, don't be. We will start small, prove the case on your data, and scale what works.
How we work.
Every engagement follows a recognisable pattern, adapted to the scope and scale of the work.
Discover
A structured workshop with your team to understand the business, identify opportunities, and agree on what success looks like.
Prove
A four-to-eight-week pilot using real data to demonstrate real value, with measurable outcomes defined upfront.
Build
The production system — engineered to scale, secured to standard, integrated with the systems you already run.
Adopt
Training, change management, and structured adoption support. This is where most AI projects die. Ours do not.
Operate
Monitoring, maintenance, model updates, cost management, and continuous improvement. AI systems drift. Ours are watched.
Our engagement model favours depth of integration over vendor lock-in. We aim to be the partner you choose to stay with because we keep delivering — not because you cannot leave.
Common questions, direct answers.
Direct answers to the questions clients most often ask before they engage. If yours is not here, ask us — we will answer it the same way.
What does your AI & Software Engineering practice actually do?
Our AI & Software Engineering practice covers five connected layers: AI consulting, custom AI engineering, software engineering, data foundations, and managed AI operations. We help organisations identify where AI will create value, prove it quickly with a pilot, build the production system, and operate it on an ongoing basis. We work with multiple vendors — Microsoft, Google, IBM, NVIDIA, and open-source ecosystems — and choose the right combination for each client's maturity, scale, and budget.
How do you typically start an AI engagement?
Most AI engagements begin with a one-to-two-day discovery workshop with the client's management team. We map current processes, identify friction points, and produce a prioritised list of AI opportunities ranked by business value and implementation effort. From that list we agree on a single use case to pilot, typically deployed in four to eight weeks using public, ready-to-use AI tools. We measure the pilot against outcomes defined upfront. If the pilot proves value, we scale it. If it does not, we have saved the client a much more expensive mistake. This approach is called proof before production.
Do you only work with large enterprises?
No. Our traditional clients have included some of the largest banks, telecommunications providers, and central government institutions in South-East Europe, but we work with organisations of any size that are willing to start small and prove the value of AI on their own data. The economics of AI — particularly when built on open-source foundations and pay-as-you-grow cloud platforms — make meaningful AI programmes accessible to mid-sized and smaller organisations. We will find a way to make the engagement affordable.
Which AI vendors and platforms do you work with?
We are deliberately multi-vendor and we work with a wider set of providers than the ones we hold formal partnerships with. Our four formal technology partnerships are with Google, IBM, Microsoft, and NVIDIA. Beyond those, we work directly with the major frontier model providers — OpenAI, Anthropic, Perplexity, Mistral, and others — through direct client subscriptions. We also have deep expertise on open-source foundations including Llama, Mistral, Qwen, vLLM, Ollama, Apache Iceberg, Apache Spark, Trino, and Apache Airflow. The right combination is chosen based on the client's specific requirements — use case, output quality, data sensitivity, and budget — not on our commercial incentives.
Can you deploy AI on-premises or in a sovereign environment?
Yes. We deliver AI in public cloud, private cloud, hybrid, and fully on-premises configurations. For clients who cannot send data to a public cloud — typically in financial services, public sector, defence, or critical national infrastructure — we deploy AI on-premises using platforms including Azure Local, IBM watsonx, and NVIDIA AI Enterprise on bare metal or virtualised infrastructure. The infrastructure layer is delivered by our sibling IT Infrastructure & Cybersecurity practice, so the client deals with one partner across the full stack.
What is "managed AI operations" and why does it matter?
Managed AI operations is the ongoing operation of AI systems after they go live. AI systems decay without active operation: models drift, data changes, costs creep, regulations evolve, and new attack surfaces emerge such as prompt injection and sensitive-data leakage. Our managed AI operations service runs AI assistants, AI agents, RAG systems, and agentic systems on the client's behalf — monitoring performance, enforcing security guardrails, detecting anomalies in usage telemetry, controlling cost, and continuously improving the system. Three engagement models are available: co-operation alongside the client's team, fully managed service, or advisory-only.
Is this just a fancy way of saying automation?
Yes, in large part. The technical vocabulary of modern AI — agents, assistants, retrieval-augmented generation, agentic systems — describes new ways of doing what business has always wanted to do: automate work that humans previously had to do by hand. An AI agent that reads incoming customer emails, classifies them, drafts a reply, and routes the case to the right person is doing automation. So is a vision system that inspects parts coming off a production line, an assistant that drafts contracts from templates, or a system that reconciles invoices against purchase orders. The difference from previous waves of automation is that AI handles unstructured inputs — natural language, images, mixed documents — that older rule-based automation could never approach. If you have heard us talk about agentic systems and you mean automation, we are talking about the same thing. We will use whichever vocabulary makes the conversation clearer.
Do you do computer vision, and what kinds of use cases?
Yes, and we are investing in it heavily because the strongest use cases sit in sectors we have served for a long time. Computer vision means AI systems that interpret images and video — from cameras, drones, industrial sensors, and edge devices. Typical applications include automated quality inspection on manufacturing production lines, drone-based inspection of bridges, power lines, pipelines, and other critical infrastructure, perimeter and crowd security in public venues, asset tracking and real-time location services across logistics facilities, traffic and incident monitoring in city environments, and safety monitoring on industrial and energy sites. We deliver computer vision jointly with our Advanced OT Solutions practice: they own the physical-world layer (cameras, drones, sensors, edge compute), and the AI & Software Engineering practice owns the model layer, the inference pipelines, and the integration into business systems. The combination matters because computer vision is rarely just a software problem — it always touches hardware, networks, and the operational reality of the environment it monitors.
What about business intelligence — and is conversational BI ready for production?
Business intelligence sits in our data foundations layer, not as a separate practice. We deliver dashboards, reports, KPI frameworks, and the semantic layer underneath them across Microsoft Power BI, Google Looker, IBM Cognos, and open-source platforms like Apache Superset and Metabase. The semantic layer — clean dimensions, agreed definitions, validated measures — is what we focus on most, because it is the difference between teams disagreeing about the numbers and teams working from the same truth.
Conversational BI — asking your data questions in plain language and getting answers, charts, and follow-ups — is genuinely exciting and we are actively delivering pilots on it. We are also honest about its current state. Numerical hallucination is a real risk: a confident wrong answer on a financial number is worse than no answer at all. The capability is becoming production-ready in narrow, well-modelled scopes — not as a turnkey replacement for a BI team. Our approach is to ground every conversational answer in a properly engineered semantic layer, use constrained NL-to-SQL with validation rather than free-form generation, and always show the user how the number was produced. We work with conversational BI features inside Microsoft Fabric, Power BI Copilot, Google Looker with Gemini, and IBM watsonx with Cognos, and we are watching the open-source approaches closely.
How do you handle AI security and the EU AI Act?
AI security is treated as a first-class concern, not an afterthought. We deploy continuous guardrails on model inputs and outputs, monitor usage telemetry for anomalies that indicate misuse or compromise, prevent sensitive-data leakage through model outputs, and maintain audit logs and red-teaming schedules for regulated environments. For EU AI Act compliance we work with our Governance, Risk and Compliance team to classify AI systems by risk tier, run conformity assessments, document the AI system lifecycle, and operate post-market monitoring. We do similar work for NIS2 and GDPR, and we align our security practices with the NIST AI Risk Management Framework. We are a technology partner, not a law firm — we translate regulatory requirements into technical architecture and operational practice.
In which countries do you operate?
We operate across seven countries in South-East Europe: Bulgaria, Croatia, Serbia, Romania, Slovenia, Bosnia and Herzegovina, and North Macedonia. We deliver to multi-country clients from a single integrated team, with one consistent engineering culture across all markets. Our headquarters is in Sofia, Bulgaria.
What is the difference between AI consulting and AI engineering at Telelink Business Services?
AI consulting identifies where AI will create value in the client's organisation and proves it quickly using public, ready-to-use AI tools — typically Microsoft 365 Copilot, Google Gemini, or similar. AI engineering builds custom AI systems grounded in the client's own data — AI assistants, AI agents, retrieval-augmented generation systems, computer vision pipelines, and fine-tuned models. Most clients begin with consulting and grow into engineering as the use case matures. The two capabilities sit in the same practice and are delivered by the same team.
How do I get started?
Email [email protected] or use the contact form on this page to request a thirty-minute conversation with one of our practice leads. We will give you a clear read on what is realistic for your organisation, where to start, and what it will take. There is no obligation, no pressure, and no charge for the conversation.
Reasons to work with us.
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01
Consultant and integrator. Most AI firms in the region are either consulting shops that cannot build production systems, or engineering shops that cannot have a business conversation. We do both, at the same scale.
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02
Genuinely multi-vendor. Public partnerships with Google, IBM, Microsoft, and NVIDIA, a growing ecosystem of formalising partners, and deep expertise on open-source foundations. No single vendor's commercial incentives shape our recommendations.
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03
End-to-end through one relationship. This practice owns AI, software, data, and managed AI operations. Our sibling practices own infrastructure, cybersecurity, and OT. Together we cover every layer a real AI programme needs — cloud, hybrid, or fully on-premises — and the client only ever deals with one partner.
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04
Regional reach, consistent delivery. Operations in seven South-East European countries, a single engineering culture, and the ability to serve multi-country clients from one partner.
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05
Publicly traded, long-standing. A quarter of a century in business. Transparent governance. The kind of partner still here to support the system we built five years from now.
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06
We practise what we preach. Our own organisation uses AI heavily in engineering, sales, and back-office functions. The lessons we bring to clients are lessons we have lived.
Practice leadership.
Start with a thirty-minute conversation.
Tell us where you are today and where you want to be in twelve to eighteen months. Thirty minutes with one of our practice leads will give you a clear read on what is realistic, where to start, and what it will take.
Request a meeting →