Custom AI Development

Why Custom-Tuned Models?

Off-the-shelf models are broad by design. But your business isn’t. We fine-tune language models on your own data — from MVPs, pilots, or internal sources — to deliver systems with:

Higher Accuracy on Your Tasks

Custom models trained on your own data outperform large, generic models on domain-specific problems. You get outputs that match your language, structure, and logic, not just general guesses.

Lower Cost, Better Speed

Smaller, purpose-built models use fewer resources. They’re cheaper to run, respond faster, and are easier to deploy, especially in environments with limited compute.

Trusted Data

We use your internal, vetted data — not scraped sources. That means more control over outputs, fewer hallucinations, and clearer traceability.

No Lock-In, Full Control

Custom models give you freedom. You’re not tied to one vendor’s API or pricing. You choose where and how the model runs, and you stay compliant with data and industry rules.

What We Do

We build AI around your infrastructure and data — not templates. From first experiments to stable deployment, everything is structured to deliver real value in production, not in slides.

Model Fine-Tuning & Custom Training

We fine-tune large language models when it makes sense, and we train compact models to fit speed and cost requirements. The process involves cleaning raw inputs, shaping the data, adjusting parameters, and running targeted experiments. It’s practical data science, built to perform under real-world conditions.

Compact, Efficient Deployments

If a model doesn’t need size, we don’t inflate it. Smaller setups reduce inference time, cut infrastructure load, and open up deployment on devices that can’t handle heavier models. The result is faster response and broader reach without unnecessary complexity.

Frictionless Integration

Integration isn’t a postscript. It’s how the AI becomes useful. We plug into your stack — where your data lives and where your teams work. We integrate AI into your product, CRM, internal tools, or APIs — making it a part of your real operations, not just a demo.

MLOps-Ready Engineering

Models drift. Priorities shift. We set things up so you can respond. Monitoring is built in. Retraining is planned, not patched. You stay in control as the system evolves, without starting from scratch.

Our Custom AI Development Tech Stack

Databricks

Weights & Biases

Azure ML

Amazon Sage Maker

MLflow

Hugging Face Transformers

TensorFlow

Cost-Efficient Architecture

Custom-tuned models can be lighter, faster, and cheaper to run — allowing you to maintain performance without incurring the high operating costs of large generic models.

lower total cost of ownership

Deploying large, generic AI models can quickly become expensive and inflexible, especially at scale. By contrast, custom-tuned smaller language models deliver real-world results with a much lower total cost of ownership.

Less Computing Power

Compact models require less computing power, which means lower cloud or on-premise infrastructure costs—without sacrificing quality on your core tasks.

Better User Experiences

Leaner models respond faster, enabling real-time AI features and better user experiences, even on standard hardware or edge devices.

Rapid Model Updates

Fine-tuned models are more transparent, easier to monitor, and can be quickly updated or re-trained as your business evolves.

On-Device AI

Smaller models are easy to deploy in a wide variety of environments, from the cloud to your own servers—or even on devices with limited resources.

Custom AI That Drives Results

Off-the-shelf AI can’t deliver the precision your operations demand. Our custom AI models integrate directly with your data and systems to solve high-impact problems, reducing manual work, speeding up decisions, and unlocking insight where it matters most. Explore use cases that show what focused, goal-driven AI can do for your business.

See All Use Cases

First-Party Data Personalized Product Recommendations

Email Classification for Logistics and Supply Chain

Domain-Specific Embedding Models for Internal Search

Contract Analysis with Custom Entity Extraction

Fine-Tuned Chat Assistants for Customer Support

Legal Document Summarization with Regulatory Context

See all use cases

Built for the Real World

AI products don’t live in isolation — they evolve with your business. Our custom solutions are architected for long-term adaptability and operational scale.

Domain-Specific Reasoning

Generic outputs don’t work in regulated or specialized environments. Our models are trained to reflect how your business actually operates — not just on paper, but in day-to-day use. They follow your workflows, adapt to exceptions, and apply your terminology with precision. That includes industry-specific logic, internal rules, and edge cases that off-the-shelf models miss.

Secure & Compliant by Design

Whether you’re in finance, healthcare, or logistics — we align with industry regulations and ensure traceable AI behavior. As an ISO 27001-certified company, we follow rigorous standards for information security, so your data, workflows, and AI outputs remain protected and auditable throughout the entire development lifecycle.

What You Get

You don’t just get a working model — you get everything needed to put it to use. From deployment and integration to visibility into performance and a clear path for updates, the focus is on making AI part of your system, not a separate experiment.

A production-grade model (fine-tuned or built)
Deployed via API or integrated into your app
Monitoring dashboard + retraining pipeline
Option for full end-user interface (e.g. chatbot, tool)
Ongoing support or handoff with documentation

Case studies

Go beyond the obvious. Co-create with teams who value impactful experiences and products.

View All Projects

app screens from Friday Harbor AI underwriting analysis

Automating mortgage underwriting with multi-agent AI

Friday Harbor

screens from Monster AI Job Search feature

Helping candidates perform better at job interviews using AI

Monster AI

Helping students learn and understand basic math using artificial intelligence

Fibo — AI Math Tutor

Leveraging AI to build a content summarization app for better knowledge retention

taim

Creating a personalized audio listening experience with content curated by artificial intelligence

Audioburst

Why Choose nomtek?

15+ years in product development

Deep experience turning complex ideas into stable, scalable products.

Experts in AI + mobile + system integration

We build AI that works where it matters — in apps, systems, and daily operations.

Trusted by Monster, Siemens, Randstad

We’ve delivered real results for global brands across industries.

Your AI Product Starts Here

Frequently Asked Questions

Dedicated team

Unpack your idea with dedicated feasibility agents. A team of three experts will work on all angles to check technical feasibility and prepare an initial roadmap. Each iteration lasts one week.

High transparency

The whole process is highly transparent — you’re in the loop at every moment. If the first week reveals additional areas for exploration, we continue iterating. This way we generate enough valuable data to speed up your decision-making.

Rich deliverables

Deliverables — Every tech feasibility sprint ends with a report, roadmap, or prototype, depending on the duration of the workshop and our findings.

Custom Process

Throughout the workshop, we integrate custom processes crafted using collected insights and internal expertise. A tailored approach is optimized for confident execution in the later stages.

How do you control access to sensitive data during training and deployment?

We follow strict access control policies during AI development. Only authorized team members can access sensitive inputs, and all access is logged and auditable. When required, we deploy AI systems inside customer-managed environments to reduce external exposure. This is especially important for companies working with personal or regulated data, where full transparency in handling is non-negotiable.

What frameworks and infrastructure do you use for training?

We use industry-standard AI tools like PyTorch, TensorFlow, Hugging Face Transformers, MLflow, and cloud infrastructure from AWS, Azure, and Google Cloud. These platforms support robust training workflows, whether you’re working with natural language processing, computer vision, or structured data. We select the stack based on your tech environment and the requirements of the model.

Can you help with AI model compliance and audits?

We provide full documentation for model training, data sources, and configuration decisions. If your team needs to meet audit requirements — internal or external — we can deliver ISO-aligned records, artifact logs, and documentation that fits into your review process. Our workflows are built with compliance in mind, not retrofitted later. For teams creating custom AI models in regulated industries, auditability isn’t optional — it’s a baseline requirement.

How long does it take to train and deploy a custom model?

Timelines vary depending on the size of the model, the quality of training data, and how deeply it needs to connect with your existing systems. A well-defined project using pre-trained components can be ready in weeks. More complex models or projects involving new architectures may take longer. We scope and estimate upfront, so you always know what to expect.

What is custom AI development?

It means building AI systems that fit your specific data, infrastructure, and use cases — not applying general-purpose models to problems they weren’t designed for. Unlike generic AI models, which may fall short in accuracy or control, custom development gives you models that align with how your business actually operates. It also allows for better integration with your existing tools and workflows.

How to create a custom generative AI model?

Start by defining the task — classification, summarization, content generation, etc. Then you move into model creation: gathering data, choosing architecture, and setting up training pipelines. Most projects don’t need to train from scratch. Fine-tuning pre-trained models like GPT using your data can achieve high performance at lower cost. Creating generative models from the ground up is possible, but typically only justified in very large-scale AI projects.

Can you build your own AI model?

Yes, but building from scratch is usually reserved for advanced use cases. It requires deep machine learning expertise, high-quality data, and significant resources. In most business contexts, it’s more effective to adapt or fine-tune existing models. That way, you stay focused on results, not just infrastructure.

Can you take over an existing AI PoC or MVP built by another vendor?

Absolutely. We often step in when early-stage AI projects need to be taken further. We evaluate what works, replace what doesn’t, and bring the model into production. This includes integrating it with your systems, improving stability, and making sure it meets the standards of enterprise-grade AI technology.

What kind of data do we need to provide for model fine-tuning and training?

We work with structured logs, documents, chat records, form data, and other internal datasets: anything that reflects your operations. Strong outcomes depend on clear, high-quality inputs. That’s why we help with data preparation from the start, including formatting, validation, and alignment with your goals. These steps are essential when creating AI solutions that reflect how your business actually functions.

How do you handle security and compliance during custom AI development?

We follow ISO 27001-certified processes to protect your data at every stage. From data collection through deployment, access is restricted, encrypted, and logged. AI systems are developed in isolated environments, and all training and testing artifacts are project-specific. For clients operating in regulated sectors, we also support compliance with GDPR, HIPAA, and local data residency laws. Security is not a bolt-on. It’s built into every step of custom AI model creation.

Do you train AI models on our data?

We use your data exclusively for your project. During model training, we isolate datasets and environments to avoid any risk of data leakage. When needed, we can run the entire pipeline within your infrastructure. This approach is critical for enterprises with strict security policies, especially in regulated industries. Creating custom AI models based on your own data gives you stronger performance on domain-specific tasks, without compromising data control.

Where is the AI model hosted?

That’s up to you. We can deploy models in your cloud, on-prem environment, or a managed hosting setup — depending on your policies and preferences. For enterprises concerned with governance or access control, keeping custom AI models within internal infrastructure ensures full oversight. We support a range of deployment targets, including secure environments with restricted outbound access.