AI / ML Integration

We embed intelligence into your product — from language models and computer vision to predictive models and intelligent automation pipelines that run reliably in production.

AI That Actually Works in Your Business

There is a wide gap between a demo that impresses and an AI system that performs consistently in a real production environment. Most companies have seen both sides of that gap.
At MakDev, we do not treat AI as a feature to bolt on. We integrate machine learning and large language models into the workflows, data systems, and user experiences where they create genuine value. That means doing the engineering work properly: connecting models to your actual data, testing for edge cases, monitoring for drift, and building systems that stay accurate over time.
Whether you are starting from scratch or trying to make sense of an AI proof-of-concept that stalled, we can help you move from experimentation to production.

An end-to-end AI and ML system integrating LLMs, RAG, and custom models into your business workflows.

Businesses that want to turn data into intelligent automation, decision-making, and scalable AI products.

We design, train, and deploy AI systems with structured data pipelines, prompt logic, and continuous monitoring.

Reliable AI performance, smarter operations, and measurable impact on efficiency, growth, and decision-making.

AI Engineering & Intelligent Systems

What We Offer

LLM Integration and Prompt Engineering

API orchestration, prompt versioning, and output validation using OpenAI, Anthropic, Llama, and more.

Plugging into an LLM API is straightforward. Engineering it to behave reliably inside a business workflow is a different challenge entirely.
We integrate large language models into your existing systems and build the prompt architecture that controls how they respond. This includes chain-of-thought reasoning structures, output validation layers to catch hallucinations before they reach users, and prompt versioning so changes are tracked and reversible. The result is an LLM that reflects your business logic and stays within the guardrails you set, rather than one that gives confident but incorrect answers when things get unusual.
Use cases we support: internal knowledge assistants, customer-facing chat, document summarisation, automated classification, content generation pipelines, and more.

RAG Systems and Vector Databases

Semantic search, data embedding, vector indexing, and document chunking strategies using Pinecone, Weaviate, Milvus, and others.

A general-purpose language model does not know your business. It does not know your product documentation, your internal policies, your historical data, or the specific context your users expect answers about. Retrieval-Augmented Generation (RAG) fixes that.
We build RAG pipelines that connect your AI to your actual business data. That means embedding your documentation, indexing it in a vector database, and configuring the retrieval logic so the model pulls the right context before generating a response. The AI does not guess. It reads your data and answers based on it.
We handle the full pipeline: data preparation, chunking strategy, embedding model selection, vector store setup, and the integration layer that ties it to your product. We also build the evaluation processes that tell you when retrieval quality is degrading before your users notice.

Custom ML Model Development

Model fine-tuning, hyperparameter optimisation, and custom training pipelines.

Off-the-shelf models are trained on general data. If your business problem is specific enough that a general model does not perform well, a custom or fine-tuned model is the right approach.
We develop and fine-tune machine learning models for the use cases that generic solutions cannot handle well. That could mean a predictive model for sales forecasting trained on your historical pipeline data, a computer vision model built for quality inspection in a manufacturing environment, a fraud detection model tuned to your transaction patterns, or a recommendation engine trained on your product catalogue and user behaviour.
We define success criteria upfront, build transparent training pipelines, and deliver models with documented performance benchmarks so you know exactly what you are deploying and what to expect from it.

MLOps and Model Lifecycle Management

Model deployment, performance monitoring, CI/CD for ML, and governance.

Deploying a model is not the end of the work. Models degrade. Data changes. User behaviour shifts. An AI system that performed well at launch can quietly become unreliable over time if nobody is watching.
We implement MLOps infrastructure to keep your models stable, observable, and accurate in production. That includes automated monitoring for data drift and performance degradation, retraining pipelines that trigger when quality drops below defined thresholds, CI/CD workflows that let you update models without disrupting production, and audit trails for governance and compliance purposes.
We treat your ML models the same way we treat production software: something that needs to be maintained, tested, and improved on an ongoing basis.

Phase by Phase

Our Process

Every AI and ML engagement follows a consistent process that prioritises working systems over impressive prototypes.

Problem Definition
We start by establishing whether AI is the right tool for the problem at hand. Not every challenge benefits from machine learning, and we will tell you that honestly if it applies. For problems that do, we define success metrics, data requirements, and integration scope before any development begins.
Data Assessment
AI quality is determined largely by data quality. We audit your available data, identify gaps, and design the preparation pipeline needed to make it usable for training or retrieval.
Architecture and Integration Design
We design the system architecture covering model selection, infrastructure choices, API integration points, and the connection to your existing software stack.
Development and Training
Model development, fine-tuning, prompt engineering, RAG pipeline construction, or custom training, depending on the approach agreed in Phase 3. We build iteratively with regular reviews.
Evaluation and Testing
e test rigorously against real-world edge cases, not just benchmark datasets. Output validation, hallucination testing, latency profiling, and integration testing all happen before production deployment.
Deployment and Monitoring
We deploy with the monitoring infrastructure in place from day one. You go live knowing that performance is being tracked and that there is a defined process for handling issues.

Engineering Depth, Not Just API Wrappers

There is a significant difference between a team that can call an AI API and a team that can engineer reliable AI systems. We work at the level of prompt architecture, model evaluation, vector retrieval optimisation, and MLOps infrastructure. If the work requires genuine ML engineering, we have the people to do it.

Security and Compliance Built In

AI systems handle sensitive data. We build with that in mind from the start. MakDev is ISO certified, GDPR compliant, and HIPAA compliant. For clients in finance, healthcare, and other regulated sectors, that is not a checkbox. It shapes the architecture decisions we make throughout the engagement.

Integrated with Your Existing Stack

We do not build AI in isolation. The systems we deliver connect to your existing software, data infrastructure, and user-facing products. That integration work is a significant part of what we do and something we treat with the same rigour as the AI layer itself.

Cost-Effective Without Compromise

Our teams are based in Central and Eastern Europe, which gives US and Western European clients access to senior AI and ML engineers at 40 to 60 percent of the cost of equivalent teams in those markets. The technical standards are the same. The overhead is not.

Technologies

AI & ML Technology Stack for Scalable, Production-Ready Systems

We work across the major AI and ML toolchains, selecting the right technologies for each project’s requirements rather than defaulting to a fixed stack.

Frequently Asked Questions

How do you handle hallucinations in LLM outputs?

We engineer multiple layers of control to reduce hallucination risk. These include RAG pipelines that ground responses in verified source data, output validation logic that checks responses against defined constraints, confidence scoring where appropriate, and human-in-the-loop review stages for high-stakes outputs. No AI system can guarantee zero hallucinations, but a properly engineered one can catch and contain them reliably.

Do we need a large dataset to build a custom ML model?

Not always. The data requirements depend on the problem. Fine-tuning an existing model often requires far less data than training from scratch. We assess your available data early in the process and design around what you actually have, rather than what an ideal scenario would require.

Can you integrate AI into software that already exists?

Yes. Most of our AI and ML work involves integrating with existing systems rather than building from scratch. We work with your current architecture and existing data infrastructure, and we design integration points that minimise disruption to what is already running.

How do you ensure AI systems remain compliant with data regulations?

Compliance is built into the architecture, not added at the end. We design data flows, access controls, and audit trails to meet GDPR, HIPAA, and other applicable standards. For regulated industries, we document our compliance approach as part of the technical deliverables.

How do you measure whether an AI integration is working?

We define success metrics at the start of every engagement, before any development begins. These typically include accuracy benchmarks, latency targets, business outcome indicators such as time saved or error rates reduced, and ongoing monitoring thresholds that trigger alerts or retraining when performance drops.

What is the difference between RAG and fine-tuning, and which do I need?

RAG connects an existing model to your data at query time, which is effective when you need the AI to reference specific documents, policies, or real-time information. Fine-tuning modifies the model’s weights based on your data, which is better suited to changing how the model responds, its tone, its reasoning style, or its handling of domain-specific language. Many projects benefit from both. We will recommend the right approach based on your use case.

Related Insights

3 min March 11, 2026

Custom Software Development & Scalable Infrastructure

AWS, GCP, Azure, Custom Software Developement

Read →

Ready to Build Your Integration?

If you have a use case in mind, or if you are trying to understand what AI could realistically do for your business, let’s have a direct conversation. We will tell you what is feasible, what is not, and what it takes to build something that actually performs in production.

Book an Appointment