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.
Why Choose Nomtek?
We help companies release AI solutions faster and validate ideas sooner. Prioritizing outcomes, we propel your product to success with dedicated, goal-oriented teams of 2-30 specialists.
Mobile native products, cross-platform apps, AI solutions, and AR/VR products shipped.
Senior designers and developers supported by dedicated product managers.
Active users of a single app over the years.
Average team member experience
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.
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.
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Our Custom AI Development process
Our process is built on experience, precision, and a commitment to delivering results at every stage of development. We've chiseled out a process that is proven with low risk thanks to rapid iteration.
01
Discover & Prioritize
We explore your workflows and goals. Together, we select one use case with the clearest ROI, addressing your real business pain, not just a tech curiosity.
Impact: Focuses effort where it matters most, reducing wasted cycles.
02
Design for Fit
We map the ideal user flow, integration points, and system interactions. This ensures the AI solution aligns with how your team works.
Impact: Boosts adoption and effectiveness. AI that makes sense to your users.
03
Build & Integrate Smart
We fine‑tune models on your own data, then embed them into your infrastructure, on cloud or on‑prem, as fits your security and control needs.
Impact: Delivers AI that’s accurate, safe, and smoothly integrated.
04
Measure & Validate
We deploy a working model with real data and workflows. We track performance by success metrics such as speed gains, error reduction, or decision quality.
Impact: You see real value first, before committing fully.
05
Tighten for Scale
If the prototype delivers, we upgrade it for production: add monitoring, compliance logs, retraining pipelines, and documentation.
Impact: Your custom AI becomes robust, auditable, and ready to scale.
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
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.
Case studies
Go beyond the obvious. Co-create with teams who value impactful experiences and products.
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Automating mortgage underwriting with multi-agent AI
Friday Harbor
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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
What to Watch Out for When Moving from PoC to a Production-Ready AI Solution
Launching a production-grade AI system isn’t just about scaling up a prototype — it’s about building something stable, compliant, and valuable in real-world conditions. Many projects stall at this stage due to gaps in infrastructure or hidden operational costs. Here’s what to look out for:
Why a Prototype is only the beginning of Proper Custom AI integration?
A working demo gives a false sense of readiness. Once the PoC ends, the hard part begins — preparing data pipelines, deploying models, and connecting AI to your systems. We treat the PoC as a starting point. Our teams extend early models into tested and documented solutions, ready for production and daily use.
Why infrastructure considerations are critical for AI adoption?
Custom AI projects can fail when they’re built without regard for your actual tech stack or operational flow. We build directly inside your ecosystem — whether it’s cloud-native, hybrid, or on-prem. Deployment, monitoring, and updates are all designed with your system and teams in mind.
What are the total long-term costs of AI implementation and maintenance?
AI that’s expensive to run or hard to update becomes a burden, not a benefit. We focus on efficient models — fine-tuned for accuracy, light on compute. With MLOps-ready pipelines, retraining and monitoring are already built in, so you avoid expensive rebuilds down the line.
