Your data is a powerful, and likely untapped asset. Transform it into a competitive advantage with Supercharger, Straker's Custom Model Creation service.
LLMs are powerful but they lack specialized knowledge required for high-stakes, nuanced tasks. They weren't trained on your data, terminology, or your specific quality standards, this leads to generic outputs, costly manual corrections, and missed opportunities.
Straker's small language models are different. They're designed for one thing: to be expertly trained for specific, high-value verticals. And now, we're offering our world-class AI team to build a custom model exclusively for you.
Our team of AI experts will partner with you to build, train, and evaluate a proprietary SLM that turns your data into a strategic asset.
Custom models are trained on your unique datasets – be it translation memory, support tickets, or internal documentation – to create an AI that understands your world.
Custom models deliver higher accuracy and quality. For translation, that’s less time spent on post-editing, reducing costs and accelerating your time-to-market. For applications like chatbots, it means more accurate and helpful responses.
Automate processes and scale your global operations without a linear increase in costs. Your custom AI model is your “always-on” linguist and expert, dedicated to your brand’s success.
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Drastically reduce post-editing time and improve translation quality by training a model that knows your preferred terminology and style.
Deliver more accurate, helpful, and context-aware responses by training a model on your past support conversations and knowledge bases.
Create investor-focused summaries of financial documents or analyze patents with a model trained on your specific formatting and language.
Automate the evaluation of content quality with a model trained to your specific standards
In April 2025, the Tokyo Stock Exchange (TSE) mandated that investor relations content from TSE listed companies be published in both Japanese and English. Customized and trained for this specific application, our Tiri-J custom model was built and now drives SwiftBridge, a super-fast Japanese to English translation platform that connects companies with overseas investors.
Straker’s Supercharger Custom AI Model Creation service transforms your data into a unique, revenue-driving asset. Instead of treating AI as a cost center, you gain a competitive edge built on specialization, efficiency, and scale.
Your data is your greatest asset. With Supercharger you harness Straker’s SLMs to transform it into a proprietary AI model that delivers higher quality, greater consistency, and a lasting competitive edge.
Think beyond what Large Language Models offer. Supercharger gives you:
Your translation memory, documentation, or support tickets become the foundation of a proprietary AI model no competitor can copy. This gives you a long-term competitive moat.
Custom models slash the need for costly human input. Combined with our Tiri inference framework – you reduce latency, maximize throughput, and keep hosting costs down.
Straker's Tiri SLMs are specialists, not generalists. They deliver consistent terminology, adhere to brand style, and meet the quality bar for regulated industries like legal or financial services.
Your custom model acts as an always-on expert. It enables you to scale operations globally without scaling costs – automating routine tasks and freeing your teams to focus on growth.
A custom AI model accelerates launches, sharpens quality, and gives you the confidence to scale globally. With Supercharger, your AI is no longer an overhead – it’s a driver of competitive advantage and measurable business results.
Any business that deals with high volumes of specialized content, require strict quality consistency, or possess vast proprietary datasets can benefit from the Supercharger service. Businesses like:
• Global Enterprises (e.g. major international brands in Manufacturing, Fashion, Travel)
• Large Customer Support Operations (e.g., Telecoms, E-commerce Platforms)
• Highly Regulated or Specialized Content Producers (e.g., Financial Services or Legal Firms)
Getting started with a custom model is simple. Straker partners with you to define goals, prepare your data, select and train the right model, and then deploy it securely into your workflows. From day one, you get a tailored AI engine built on your own data – with continuous refinement to keep it sharp and future-ready.
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We begin by understanding your goals, your data, and what success looks like.
Our team prepares and curates your data so the model learns the right terminology and style from the start.
Based on your needs we'll select the model that best fits your domain to deliver precision performance.
The model is tested against real-world scenarios and refined until it meets the highest standard.
Your model is hosted securely and connected to your systems through simple, scalable APIs.
As your needs evolve, we monitor, update, and retrain your model to keep it performing at its peak.
Includes expert consultation, model training, and standard evaluation reporting.
Up 10 hours of expert consultation
Training on up to 2 million tokens
Standard evaluation reporting
Includes extended consultation, advanced hyperparameter tuning, and detailed evaluation reporting for more complex tasks.
Everything in Base plus training on up to 5 million tokens
Advanced hyperparameter tuning
Detailed evaluation report
A detailed benchmark report comparing up to 3 foundational models on your data to inform the best choice
Contact our AI solutions team to learn more about Supercharger and how your data can power a custom model to accelerate business growth.
Request a discovery call
What information do I need to supply to get a comprehensive quote?
We've put together a guide which will help you gather the information we need. Download it here.
What size model fits within the $10k budget?
A $10,000 budget is sufficient for a model of moderate size (several hundred thousand data segments), given our efficient training processes. The exact model size will depend on data volume and complexity.
How much data do we need to prepare?
The ideal amount of data is highly dependent on the complexity of your domain and desired model performance. In general, more data typically leads to better results, but a well-curated smaller dataset can also yield excellent performance. Recent internal work has focused on this balancing act; a few million well-chosen segments may be sufficient.
We work in a specialized domain, how does the training process handle industry-specific terminology?
The training process effectively handles industry-specific terminology. We incorporate your specialized vocabulary through several methods: providing domain-specific training data, using prompt engineering techniques to guide the model's understanding of your terminology and fine tuning the model to achieve optimal performance on your data.
Is there flexibility to improve the model over time as we learn more about what works?
Yes, there's flexibility to continuously improve the model. We incorporate continuous retraining and updates into our service agreements to ensure the model remains accurate and performs at its peak.
Does 400 GPU hours cover the full process including optimization?
The 400 GPU hours is an estimate. The actual requirement will vary considerably depending on the selected model, data size, and training methodologies. We typically optimize the training process to minimize compute costs, but unforeseen issues can lead to resource adjustments.
We're trying to plan our infrastructure, generally what is needed for hosting?
Hosting requirements vary considerably based on anticipated usage levels. We'll work with you to design a scalable infrastructure to meet your specific needs and budget constraints, and you will likely require an extensive consultation with our engineering team.
What are the ongoing inference costs at different usage levels?
Ongoing inference costs depend heavily on the volume of requests and model complexity. We can provide more precise estimates after assessing your projected usage patterns.
What kind of response times are typical once deployed?
Typical response times depend on various factors, including model size, network latency, and request complexity. We strive to provide fast and efficient responses tailored to your application's needs.
We use standard APIs in our tech stack, what's the integration process like?
We also work with standard APIs, making integration straightforward.
How does the support relationship work after the initial training?
We offer ongoing support beyond the initial training phase, including model monitoring, maintenance, and updates. The specific details of our support offering are discussed and defined during the project planning stage.