Your data. Your model. Our GPUs.
Custom LLMs trained on cutting-edge distributed infrastructure.
Your private AI — optimized for your domain, deployed anywhere.
Perfect For
Build your AI moat early. Custom models give you a defensible advantage over competitors using generic APIs.
Offer AI services to your clients with white-label models. Become the AI partner they need.
Deploy private intelligence layers with full compliance, security, and governance controls.
The Timing
The AI landscape has shifted. Custom models are no longer a luxury—they're a strategic necessity.
The cost of training large models is dropping 10x every 18 months
Open-source frontier models now rival proprietary alternatives
Enterprises demand private, compliant AI—not shared APIs
Your custom model is your competitive edge in the AI era
Foundation
Fine-tune frontier open-source models that rival proprietary alternatives.
2.5B → 235B MoE
8B → 70B
2B → 27B
Base & Instruct
Compatible with leading providers
The Process
From raw data to deployed intelligence—four weeks to your custom model.
Create, clean, or synthesize datasets. Domain distillation from frontier models.
Fine-tune models up to 235B parameters using distributed infrastructure.
Comprehensive benchmarks, safety tests, and real-world validation.
Private APIs, SDKs, white-label apps, and full integration support.
From initial data handoff to production-ready deployment. Complex enterprise projects may take 6-8 weeks.
The Advantage
Stop paying per-token. Own your intelligence layer.
For high-volume use cases, custom models eliminate per-token API costs. Own your inference infrastructure.
Your weights, your VPC. No data leakage. Enterprise-grade privacy with full control over model deployment.
Tailored tone, behavior, and domain knowledge. Your model speaks your language, understands your context.
We handle the entire infrastructure stack. You don't need to build your own GPU cluster, ML research team, or orchestration layer.
Our Edge
Not another agency — a full AI lab. We build models, agents, pipelines, infrastructure, and entire platforms.
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 were training models, distilling Qwen, orchestrating multi-model workflows, and building agents before GPT-4o, before Gemini, before the hype cycle. We've lived through everything from LLaMA-1 to LLaMA-3 and saw the entire open-weights revolution unfold. We didn't jump on the wave — we were here when the wave began.
We've fine-tuned Qwen, LLaMA, Gemma, Mistral, Mixtral, and multiple small models. We've built internal bilingual Bengali/English models, distilled models for production apps, and even crafted custom Orchestrator → Worker model chains inside LeemerChat. We understand models from the inside, not just the prompt.
LeemerChat alone has processed over 1B tokens for real users. That's 1B tokens of model reasoning, user queries, real-world edge cases, and what breaks and what scales. This is battle-tested experience, not theoretical knowledge.
We're partnered with Thinking Machines — the training platform founded by ex-OpenAI leadership — giving us distributed fine-tuning infrastructure most companies will never touch. This lets us fine-tune 7B → 235B models with fault tolerance, multi-node reliability, and RL support. We don't guess how to fine-tune. We fine-tune like the labs do.
We built Leemer Heavy, Leemer Heavy Fast, Leemer Research, and multi-agent pipelines using Qwen, Groq LPU models, GPT-4.1/4o, Claude, Kimi, LLaMA, and DeepSeek. We design architectures where small, large, and domain models collaborate — so your system is always fast, accurate, and cheap.
We don't just hand you weights. We deliver the entire intelligence layer: private APIs, white-label chat apps, internal agents, custom embeddings, RAG pipelines, Slack/Teams/WhatsApp bots, on-prem deployment, monitoring, rate-limits, logging, and analytics. Most agencies 'fine-tune a model'. We deploy your entire AI system.
Everything we sell, we use in our own products. We're not theorizing — we're operating. LeemerChat, Warren.wiki, HeyCouncil, ExamMate… these are full AI platforms built on the same systems we deliver to clients. If we didn't build real things, we wouldn't be here.
We back open-weights. We support local hosting. And at the end of the engagement, you own the model, the weights, and the intelligence layer. You're not renting intelligence from a Silicon Valley API. You're owning your own model.
We're proud of where we come from. We build world-class AI — in Ireland. No Silicon Valley ego, no bloated teams, no fluff. Just pure engineering, research, and delivery.
Processed across our ecosystem. LeemerChat alone has processed over 1B tokens for real users — that's 1B tokens of model reasoning, user queries, real-world edge cases, and what breaks and what scales. This is battle-tested experience, not theoretical knowledge.
Full compliance, security, and governance controls for organizations that demand the highest standards.
From Thinking Machines Lab · Founded by former OpenAI CTO Mira Murati
Tinker is a training API for large language models built by Thinking Machines Lab, the AI company founded by former OpenAI CTO Mira Murati and a team of ex-OpenAI researchers including co-founder John Schulman.
Instead of you managing clusters, GPUs, and training jobs, you write a simple Python training loop on your own machine, and Tinker turns it into fault-tolerant distributed training on their GPU infrastructure. Switching models—from small 1B variants to massive 235B MoE architectures—is often as easy as changing a single string.
Under the hood, Tinker uses LoRA (Low-Rank Adaptation) rather than full-parameter fine-tuning, based on their groundbreaking research which shows that with the right setup—correct learning-rate scaling, rank selection, and layer coverage—LoRA can match full fine-tuning for many post-training tasks, especially reinforcement learning. This means full-fine-tune-level performance with far less compute and cost.
The Technology
Tinker compresses an entire AI infrastructure team into an API.
Tinker handles GPU scheduling, checkpointing, fault tolerance, and multi-node training—so we focus on data, objectives, and evaluation instead of cluster babysitting.
Support for modern Llama and Qwen families—including huge MoE models—means we can train models competitive with proprietary labs while letting you own and export your weights.
Their 'LoRA Without Regret' research gives practical guidance on ranks, learning rates, and RL behavior. Full-fine-tune-level performance with far less compute and cost.
Thinking Machines is stacked with former OpenAI leaders—including co-founder John Schulman and other senior researchers—who've shipped frontier-scale systems before.
We chose to partner with Thinking Machines and join their early Tinker beta because it gives our clients something most agencies simply cannot offer.
We get the same style of distributed training stack that powered frontier models—exposed through a clean API—so we can fine-tune everything from compact 1B experts to MoE giants like Qwen3-235B for your domain.
Instead of treating LoRA as a hack, we use it the way the 'LoRA Without Regret' team intended: correct learning-rate scaling, rank selection, and layer coverage. Better sample efficiency, better RL behavior, faster iteration.
Because Tinker is built for open-weight bases (Llama, Qwen, etc.), we hand you exportable weights at the end of a project. You're not locked into our infra—or anyone else's.
Thinking Machines' mission is to make advanced AI more understandable and customizable, not more opaque. That lines up perfectly with what LeemerLabs Model Foundry stands for.
For LeemerLabs Model Foundry, that means we can reliably offer serious, research-grade training loops instead of "just another wrapper around someone else's API."
What We Offer
Not just training—deployment, hosting, white-label apps, orchestration, RAG, and evaluations.
Fine-tuning on Qwen3, LLaMA 3.1, Gemma 2, Mixtral/Mistral. LoRA adapters, multi-turn training, instruction tuning.
Dataset creation (manual + synthetic), cleaning & formatting, domain distillation, RL trajectory datasets, labeling pipelines.
Private API endpoints, downloadable weights, SDKs, hosted inference, LoRA merging, rate limiting, logging & analytics.
Vector database setup, embeddings optimization, document ingestion, custom retrievers, evaluation & hallucination reduction.
White-label LeemerChat, research agents, internal team chat, Slack/Teams/WhatsApp bot integration.
Benchmarks (TruthfulQA, MMLU, GSM8K, HumanEval), real client data evals, safety tests, hallucination analysis, benchmark reports.
Applications
Custom models power domain-specific intelligence across industries.
Domain-specific legal knowledge, case law analysis, contract review assistance.
Property analysis, market insights, client communication automation.
Medical knowledge models, patient interaction, documentation support.
Personalized learning, curriculum adaptation, student support.
Financial analysis, tax preparation, compliance checking.
24/7 support automation, ticket routing, knowledge base integration.
Academic research, citation generation, literature review assistance.
Public service automation, policy analysis, citizen engagement.
Investment
Transparent pricing for businesses of all sizes. Monthly retainer options available for ongoing support.
€1,200 – €3,000
For small businesses
€5,000 – €12,000
For startups / agencies
€15,000 – €50,000
For government & enterprise
€100,000+
Frontier Intelligence Systems
Frontier Intelligence Systems — Designed for large enterprises, national deployments, and multi-year intelligence initiatives
This is our highest tier, built for organizations that need full-stack, end-to-end, sovereign-grade AI systems.
Exclusive Access
Work directly with Repath "Ray" Khan on high-impact strategy and deployment.
€299
One day. One founder. One deep dive into your AI problem.
Work directly with Repath 'Ray' Khan — former Indian curry-house operator turned AI founder, builder of multi-million-token systems, board member of Oli's Foundation (15k+ meals donated to the NHS), and creator of LeemerChat, Warren.wiki, HeyCouncil, ExamMate, DeepThis, and more.
€25,000 – €75,000
per 6–12 months
Work directly with Repath 'Ray' Khan — founder of LeemerChat, Warren.wiki, HeyCouncil, and a dozen AI systems
This is the "board-level AI advisor" tier. Work directly with the founder, not just the lab.
Keep your model current with ongoing improvements: €499 – €2,500 per month
Questions
We are one of the few labs that merges EU compliance, Mistral fine-tunes, and Tinker-level infrastructure.
Yes. While we champion open-source models for ownership, we also offer expert fine-tuning for OpenAI models via Azure or direct API.
Note: Pricing for OpenAI fine-tuning is variable based on token usage and dataset size. We charge a service fee for data preparation and optimization.
OpenAI fine-tuning is excellent for formatting and specific output styles, but you do not own the weights. OpenAI can deprecate these models at any time.
Whether you're testing custom models, deploying enterprise intelligence layers, or fine-tuning with your own datasets, we keep the experience cohesive—one foundry, multiple specialized models.
Or schedule directly below: