CUSTOM AI DEVELOPMENT SERVICES
We are a custom AI development company specializing in bespoke enterprise AI solutions. Our team designs and implements tailored machine learning and automation systems that streamline routine processes and sharpen your business intelligence – built around your goals, your data, and your industry, so the result is something your team will actually use.
Projects Delivered
Weeks To Kickoff
Years of Expertise
We’ll get back to you within 1 business day
AI Solutions That Work in the Real World
AI Assistants & Chatbots
Our AI assistant development and custom AI chatbot development services build conversational AI on GPT, Claude, or Gemini, trained on your business logic rather than relying off-the-shelf models.
AI Agents & Workflow Automation
Our AI agent development services and workflow automation services build autonomous agents that execute multi-step tasks across your systems without manual handoffs.
LLM Integration & Fine-tuning
Our LLM integration services and LLM fine-tuning services connect large language models into your stack and adapt them to your business terminology and logic.
RAG & Knowledge Base Systems
Our custom RAG development services build AI that answers questions grounded in your internal documents, data, and policies, rather than generic training data.
AI-Powered Document Processing
Our AI document processing services deliver automated extraction, classification, and analysis of documents, receipts, and contracts through intelligent document processing built around your data.
Custom AI Solutions & Consulting
We provide AI consultancy services covering strategy, architecture, and implementation - tailored custom AI business solutions built around your goals, backed by hands-on generative AI consulting expertise.
Why Choose Diatom Enterprises
Building AI is easy. Building AI that actually works in your business — that takes experience.
We’ve been integrating intelligent systems long before “AI” became a buzzword.
Deep Technical Expertise
From LLM integration and RAG systems to AI agents and document automation — we work with the full stack of modern AI tools. No vendor lock-in, no one-size-fits-all solutions.
Real Business Experience
We've been building intelligent software since before it was called AI. That background means we understand your business, not just the technology.
Practical, Not Theoretical
We don't build demos. Every solution is designed for production: maintainable, secure, and integrated with your existing systems and workflows.
Full Engagement Flexibility
Whether you need a dedicated AI team, additional engineering capacity, or ongoing support, we match the engagement model to your project, not the other way around.
Sound like the right partner?
AI Technologies We Use
We rely on proven, modern technologies to deliver reliable and scalable AI solutions.
LLMs & AI Models
OpenAI's flagship language model family, powering ChatGPT and general-purpose text, code, and reasoning tasks.
Anthropic’s model family, built with a safety-first approach for reasoning, coding, and long-context work.
Google DeepMind's multimodal model family, integrated across Search, Workspace, and Google Cloud.
Meta's open-weight model family, built for self-hosting, fine-tuning, and flexible deployment.
AI Tools
Anthropic's agentic coding tool that plans, writes, and ships code changes across a codebase from the terminal, IDE, or desktop app.
Microsoft's AI pair programmer, offering inline code suggestions and chat-based help inside popular IDEs.
An AI-native code editor built on VS Code, with an agent mode that edits multiple files from natural-language instructions.
A data framework for connecting LLMs to your own files, databases, and APIs to build retrieval-augmented applications.
Search & RAG
An AI-powered answer engine that searches the live web and returns cited, sourced responses instead of static training data.
A fully managed vector database built for storing and querying embeddings at scale with low latency.
An open-source vector database supporting hybrid search — combining vector similarity with keyword filters.
A PostgreSQL extension that adds vector similarity search directly into your existing SQL database.
AI Platforms
The company behind GPT and ChatGPT, offering an API platform for building custom applications on its models.
The company behind Claude, focused on building reliable, interpretable, safety-first frontier AI systems.
Microsoft's enterprise offering of OpenAI's models, hosted on Azure with added compliance and governance controls.
Google Cloud's unified ML platform for building, training, and deploying models — including Gemini — with MLOps tooling.
Infrastructure
A containerization platform that packages applications and their dependencies so they run consistently across environments.
Amazon's cloud platform, offering the compute, storage, and managed AI/ML services behind many production systems.
A modern Python web framework for building fast, well-documented APIs — a common choice for serving AI models and agents.
The dominant programming language for AI and machine learning, backed by libraries like PyTorch and Hugging Face.
AI Development Case Studies
The People Behind
A team of skilled engineers, architects, and delivery managers, dedicated to making every engagement a success.
Frequently Asked Questions
Everything you need to know before starting a custom AI project.
How much does custom AI development cost?
Pricing depends on scope – a single chatbot integration looks very different from a multi-agent workflow system – but most custom AI projects range from a few thousand euros for a focused proof of concept to well into six figures for enterprise-grade deployments. One cost factor that’s often overlooked is ongoing token usage: every request to an LLM has a per-token cost, so we design with token efficiency in mind from the start – choosing the right model size for each task, caching where it makes sense, and avoiding unnecessary calls – so your running costs stay predictable as usage scales, not just the upfront build.
How long does it take to build a custom AI solution?
A well-scoped proof of concept typically takes 1-2 weeks to kick off, with a working prototype in 3-6 weeks depending on complexity. Full production systems – think multi-step agents, RAG pipelines, or deep integrations with your existing tools – usually take 2-4 months from kickoff to deployment, including testing and refinement.
What industries benefit from custom AI development?
Nearly every industry has use cases, but we see the strongest impact in professional services, healthcare, finance, legal, e-commerce, and logistics – anywhere with repetitive, document-heavy, or data-rich processes. For regulated industries like healthcare and finance, custom development also means we can build compliance requirements (like GDPR) directly into the system’s architecture, rather than retrofitting them afterward.
What's the difference between off-the-shelf and custom AI?
Off-the-shelf AI tools are built for general use cases and average users – they’re fast to set up but rarely fit your exact workflows, data, or edge cases. Custom AI is built around your business logic, your data, and your compliance needs from day one, which means better accuracy, tighter integration with your existing systems, and full control over how your data is handled and stored.
Do I need my own data to start an AI project?
Not necessarily – some projects, like general-purpose assistants, can start with minimal data and improve over time. But for anything involving your internal documents, customer data, or proprietary knowledge (like RAG or knowledge base systems), having that data organized and accessible speeds things up considerably. If you’re an EU-based business or handle EU customer data, we also make sure data handling – storage, processing, retention – is GDPR-compliant from the outset, whether that means EU-based hosting, data minimization, or clear data processing agreements with any third-party model providers we use.