The Foundry Report: Why Fine-Tuned Models Are Still the Sharpest Weapon in Enterprise AI

The wait is over. Tinker is officially out of beta and is now generally available. The waitlist is gone, meaning any team can sign up and start training custom models today. This shift fundamentally democratizes access to distributed fine-tuning and multi-node GPU orchestration.

But for our clients at LeemerLabs Model Foundry, this isn't new. Because we were Ireland's only Tinker Beta Partner, our clients have had access to this frontier-class training infrastructure for months. We've already battle-tested Tinker's fault-tolerant distributed training on everything from 1B experts to 235B MoE giants. Now that Tinker is GA, it validates what we've known all along: this is the premier platform for building production-ready models.

Text-only LLMs are no longer enough for complex enterprise workflows. Recent updates to Tinker bring native support for next-gen models, including Kimi K2 Thinking and comprehensive vision input capabilities. We now have full multimodal training capacity.

At Foundry, this means we fine-tune long-context, multimodal, and reasoning-capable models. We produce models that can literally see and understand diagrams, UI screenshots, and dense PDF documents. If your enterprise workflow relies on visual context, our fine-tuned models can natively process it without complex OCR hacks.

Innovation doesn't happen in a vacuum. Thinking Machines has announced extensive research and teaching grants to accelerate academic use of Tinker and drive fine-tuning innovation at the university level. We are seeing a massive wave of open research momentum.

Foundry aligns completely with this open research ethos. Our engineers are trained in frontier methods that are continuously validated by top universities using Tinker. This isn't just commercial trial-and-error—this is research-driven expertise brought directly into your enterprise models.

Look at any recent enterprise trend report: AI infrastructure is melting into internal systems. In 2026, AI is no longer a pilot project or a neat experiment—it is a core operational layer. Training, deployment, and governance must be built seamlessly into your existing tech stack.

This reinforces why LeemerLabs Model Foundry isn't just about fine-tuning weights. It's about full-scale infrastructure engineering and integration. When we build your model, we're building the intelligence engine that powers your organization's future.

Most 'AI agencies' wrap OpenAI and call it a day. We build models, agents, pipelines, infrastructure, and entire platforms. LeemerLabs is the research arm behind LeemerChat, Warren.wiki, ExamMate, HeyCouncil, DeepThis, and more—real systems used by real users every day.

We've been in the AI game since 2023. We were training models, distilling Qwen, orchestrating multi-model workflows, and building agents before GPT-4o, before Gemini, before the hype cycle. We've processed over 1 billion tokens across our ecosystem. We know what breaks and what scales in production because we're operating at that scale daily.

Behind the hype, major AI players are heavily prioritizing customized, domain-specific models over raw parameter count. Why? Because practical enterprise value demands specialization. Gigantic models are impressive, but they are expensive, slow, and often too generalized.

A model must be tuned to your specific data, your exact workflows, and your unique domain to deliver real business ROI. Small, highly-specialized models consistently beat massive generalists on domain-specific tasks, and they do so at a fraction of the inference cost—often 95% cheaper than OpenAI at scale.

LoRA (Low-Rank Adaptation) continues to dominate as the most efficient way to fine-tune custom models at scale with minimal compute cost. But not all LoRA is created equal. 'LoRA done right' requires deep expertise.

At Foundry, our engineers don't just use default parameters. We intentionally select ranks, learning rates, and layer coverage based on empirical research ('LoRA Without Regret'). Instead of treating LoRA as a hack, we use it the way the researchers intended. This intentionality ensures that our efficient fine-tuning matches full fine-tuning performance, especially for complex post-training tasks like RLHF.

Our process isn't an experiment; it's a battle-tested pipeline that typically takes just 4 weeks from kickoff to production.

Week 1: Data Forge—We create, clean, and synthesize datasets, including domain distillation from frontier models. Week 2: Model Crafting—We execute the distributed training loop on Tinker. Week 3: Evaluation—We run rigorous safety and benchmark evaluations (MMLU, HumanEval, domain-specific tests). Week 4: Deployment—We deliver private API endpoints, SDKs, white-label chat apps, and exportable weights. We don't just fine-tune a model; we deploy your entire intelligence layer.

AI Sovereignty: Enterprises are demanding strict data governance, privacy, and deployment control. You shouldn't be renting intelligence from a Silicon Valley API—you should own it. Foundry ensures you have full ownership of your exportable weights and deployment. Built in Waterford, scaling globally, we believe in 'Your data. Your model. Our GPUs.'

Agentic AI & Specialization: Enterprises are migrating toward AI that acts, not just responds. Specialized fine-tuned models running in fleets (Planner → Worker → Judge) are the new standard. Foundry builds these fleets, giving you active, tool-using agents that automate complex, multi-step workflows.