Custom AI Model Development Services

Smaller models, lower costs, and protected IP

Break free from high-token dependencies with custom Small Language Models (SLMs). We build fine-tuned, task-specific models for agentic workflows that lower inference costs, guarantee data sovereignty, and remain your proprietary IP forever.

Get in touch
We’ll explore your technology goals and challenges
You’ll get expert insights on the best path forward
We’ll outline next steps to bring your solution to life
FRONTIER MODELS ARE NOT THE ONLY SOLUTION

Reasons to fine-tune your own AI model

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Sorting and routing

Every ticket, document, or request that needs a fast, consistent call on where it goes. This high-volume, low-ambiguity work is the most expensive and inefficient thing you can hand a public frontier model.

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Reading and extracting

Pull the exact fields and facts out of contracts, claims, invoices, or forms, every time, without the data leaving your perimeter. Fine-tuned on your document types rather than prompted at a generalist.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Summarizing at volume

Turn long calls, chat logs, or case files into actionable summaries. Custom models drastically lower inference costs, allowing you to run millions of generations a month at a predictable unit cost without eating into product margins.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Powering agent workflows

Handle the small, repetitive steps inside an agentic workflow so the massive general model isn’t stuck doing them. Autonomous agents multiply cheap calls, which is exactly the wrong profile for pay-per-token frontier pricing.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Adopt an AI safety posture that is right for you

Develop systems around existing software and enterprise workflows that demand absolute data sovereignty. For organizations where third-party APIs or models hosted in China might not be acceptable from a risk perspective, we build integrated ERP, CRM, and internal tools that keep data strictly in-house.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Protect your IP as you scale AI

Design scalable foundations built for predictable unit costs as volume grows. We engineer architectures that support your required deployment environment—from secure cloud platforms to air-gapped, on-premise infrastructure—ensuring the final custom AI remains your proprietary IP forever.

We'll find the workloads either blocked by a data boundary or draining your budget at volume.
You'll get a modeled ROI on your own data, your own traffic, and your own costs.
We'll outline the model, the training approach, and where it runs—inside your perimeter or ours.
Case Studies

Our client impact in action

AI document system cuts processing costs by 50%

A logistics provider’s legacy document system cost the firm more than $1 million annually, couldn't scale, and suffered significant downtime. FullStack built a scalable AI solution that reduced processing times by 75% and cut costs in half, all while maintaining high accuracy and reliability.

AI call auditor automates 99% of reviews

A regulatory compliance firm partnered with FullStack to build an AI system that reviews calls for potential SEC violations. The tool scores accuracy and confidence, reducing human review to just 1% of transcripts and saving an estimated 5,500 labor hours and $232,000 annually.

Candidate matching on our own platform

FullStack is building a specialized model for Connect, our own vetted-engineer platform, handling candidate-to-role matching and skills extraction—a high-volume task on proprietary data. We’re validating custom models on that proprietary dataset because they offer more flexibility for this matching task than pre-trained models. We'll publish the accuracy, cost per task, and latency against the frontier baseline.

testimonials

What our clients are saying

FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
FullStack’s deep understanding of BenjaminWest’s needs, coupled with consistent updates, made the collaboration seamless and the outcome outstanding.
Joe Eikelberner, COO
BenjaminWest
FullStack acted as true partners and advisors. The expertise around AI and the level of developers, engineers—whatever role it was that came to the table—was just phenomenal.
Marisa Kopec, CEO
Lux Research
Speed is only the byproduct; the real value is better software and better use of our people.
Raj Tatta, VP of Engineering
Paciolan
FullStack turned our vision for The Launchpad into reality. Their intuitive design approach delivered an app that provides IT buyers a seamless and hassle-free experience, effortlessly connecting them with the ideal tech vendors.
Tonya Turrell, Founder & CEO
Technology Match
FullStack completely transformed our company's app, breathing new life into how we service our customer base. Their innovative and collaborative team delivered an application experience that we're proud to have in the market!
Jay Williams, Software Manager
Green Mountain Power
AI built for you

We partner with you from use-case to production

Our custom AI development process opens with a fixed-fee Opportunity & Data-Readiness Assessment: candidate use cases, a data quality read, a cost baseline, and a modeled ROI.
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A tested business case in 2–3 weeks*

You get a prioritized use case and the arithmetic behind it before any AI development work is scoped.

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Full AI implementation in 8–12 weeks*

From an approved business case to a validated model running inside your systems, data quality permitting.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Proven side-by-side on your data

We validate AI models against your current AI tooling on your own eval set, run inside your perimeter, until it wins on cost, speed, and accuracy. If it doesn't, you find out during the assessment, not after a build. This side-by-side process shows where custom AI models are more flexible than pre-trained models for your workload.

A group of business professionals collaborating in a lush urban park with towering skyscrapers in the background, symbolizing innovation and efficiency. The scene represents machine learning models optimizing business operations, as technology and sustainability converge to enhance productivity and decision-making in a modern corporate environment.

Honest advice

Task-specific models win on high-volume, repeatable work, whether the constraint is a data boundary or a rising bill. For the genuinely ambiguous reasoning tasks, a frontier model is still the right tool, and we'll say so—usually we route between both.

*These are typical time estimates and actual times may differ based on project complexity and scope.
COMPREHENSIVE SOLUTIONS

Explore FullStack's model training and custom AI development services

As an AI software development company, we analyze the problem and the data, choose the base model and training approach, build and validate against success criteria on your workloads, deploy inside your environment, and operate it as your data evolves.
  • Opportunity and data-readiness assessment
  • Base model selection and training strategy
  • Fine-tuning, distillation, and adapter-based training
  • Evaluation harness and success-criteria design
  • VPC, on-premise, and air-gapped deployment
  • Inference cost and latency optimization
  • Drift monitoring and scheduled retraining

Partner with FullStack and own a model that is built for your business

Enterprise Partnerships

Built for regulated and sovereign data

For financial services, healthcare, legal, and government workloads whose sensitive data can't reach a hosted model, we deploy custom AI solutions inside your perimeter—VPC, on-premise, or fully air-gapped—and you hold the weights and the audit trail.
Mid-Market Solutions

Move your highest-volume work off frontier pricing

For teams whose inference bill is growing faster than product revenue, we find the routine workloads underneath it and build the specialist that owns each one. When high-volume workloads do not require the broader capability of frontier models, lower-cost specialists create real business value.
our blog

Featured articles

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How to Choose Your AI Backbone: Top AI Infrastructure Recommendations

Building an AI backbone? A smart AI strategy, the right technology stack, and enterprise infrastructure are key to long-term success.
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Abstract illustration showing the tokenization process in AI. Natural language text flows through a digital transformation zone and breaks into character, subword, and word tokens. Conceptual art highlights how language is processed into machine-readable units for large language models.

How Large Language Models (LLMs) Understand Text: Intro to Tokenization

Discover what text tokenization in AI is, how LLMs tokenize text, and why it matters for NLP and business-ready AI systems.
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Machine learning development team working on AI solutions

Machine Learning Development: Should You Hire In-House or Outsource?

Looking at in-house vs. outsourced machine learning development? Compare costs, build the right AI team, and explore ML outsourcing with expert AI engineers.
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Business analyst reviews performance dashboards on dual monitors, illustrating human oversight and risk-based approval of AI agent actions.

Gate Fatigue: When Human Approval Stops Meaning Anything

Gate fatigue can weaken human oversight in AI workflows. Learn how to design approval gates that hold up as agent activity scales.
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Speaker presents on AI governance, highlighting operational controls, monitoring, and responsible AI agent deployment.

How AI Agent Governance Is Moving Into Practice

AI governance is changing fast. Learn how businesses can manage AI agents, access controls, monitoring, and incident response.
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Business team meets in a conference room as a presenter discusses a data chart, illustrating cross-functional planning for enterprise AI adoption and governance.

AI Adoption Is Now an Everyone Problem: What Walmart, The New York Times, and Honeywell’s Filings Reveal

AI adoption is reshaping retail and manufacturing. See what Walmart, The New York Times, and Honeywell filings reveal.
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Frequently Asked Questions

When does a custom model make more sense than a frontier generative AI model?
When the work is high-volume, repeatable, and well-defined: sorting tickets, pulling fields from documents, summarizing calls. Frontier generative AI is built for open-ended reasoning, so using it for routine calls means paying for capability you don't need. Custom machine learning models, trained with deep learning techniques like fine-tuning and distillation, do that one job at a fraction of the cost. Pre-trained models can be useful for broad tasks, but custom AI models offer greater flexibility when one workflow needs to be optimized for cost and accuracy. For genuinely ambiguous tasks, a frontier model is still the right tool, and most setups route between both.
Can a custom model run inside our perimeter, without sending data to a hosted provider?
Yes. We deploy in your VPC, on-premise, or fully air-gapped, so sensitive data never leaves your environment. That's the core of the data security case for regulated workloads. The model runs inside your existing systems, and you hold the weights and the audit trail.
What if we don't have enough clean data to train a model?
That's what the Opportunity & Data-Readiness Assessment is for. We check data quality and data availability before any training is scoped, and if there are gaps, we plan the data collection and labeling needed to close them. The assessment also shows whether data scientists will need additional data collection work to label data for the use case. Many AI projects stall because nobody checked the data first. We find that out in the first two to three weeks, not after a build.
Will a custom model work with our existing or legacy systems?
Yes. The model is built to plug into what you already run, whether that's a modern agent pipeline, existing or legacy systems handling claims, contracts, or tickets. Through AI Gateway, it can also sit alongside frontier models, so each request goes to whichever one fits it best.
What do FullStack's AI model development services include?
FullStack builds small, task-specific models for mid-to-large enterprises and fast-scaling teams, especially in healthcare, financial services, legal, and government. These models handle repetitive, high-volume work like sorting, extracting, summarizing, and powering agent workflows, where general-purpose frontier tools get expensive fast. We handle the full path, from opportunity assessment and model training to secure deployment inside your data perimeter, integration with your existing systems, and ongoing maintenance. The result is AI with lower operating costs, stronger data privacy, and fewer compliance risks.
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