Custom AI models trained on your data, deployed and operated by us
A newer frontier model is not automatically better at your job. We build a benchmark from your real outcomes, fine-tune a small open model on your historical data, measure it on a held-out test set, deploy it in your cloud or on your own GPUs and run it with the evaluation, monitoring and rollback that keep it reliable. Coinbase trained a fraud model this way for under $100 that beat Opus 4.5 on every metric and cut latency in half. We bring the same playbook to your company.
- Anthropic Partner Network 2026
- Six products of our own in production
- Cloudflare-native stack, GDPR by design
Who it is for
CTOs, heads of data and product leaders in companies with a repeated, narrow decision (fraud, support triage, classification, document extraction, moderation) and the outcome data to measure it, who want a lower cost per call, faster responses and data that never leaves their infrastructure.
What we deliver
Outcome benchmark
A held-out test set built from your historical outcomes, never used for training, with the metrics that matter to the business (precision, recall, dollar-weighted recall, latency), so every model and every upgrade is judged on your task.
Fine-tuning and post-training
Supervised fine-tuning or reinforcement learning with verifiable rewards (GRPO, LoRA) on open models such as Qwen, Llama, Gemma or Mistral, trained on a single GPU where the task allows.
Private deployment
The model served in your cloud, on your GPUs or on Cloudflare Workers AI, with an API your systems already know how to call. Data residency in your region is guaranteed on your cloud or your own GPUs; Workers AI does not offer regional inference.
LLMOps
Weights, prompts and policies versioned together, evaluation of every checkpoint, monitoring in production, cost controls and a tested rollback to the previous version.
Safe integration
The model as a second opinion next to your existing rules and systems: a single structured output your code turns into policy, with a human in the loop where the stakes require it.
Documentation and handover
Training pipeline, evaluation reports and runbooks delivered to your team, so you own the model and can retrain it when your data changes.
How we work
- 1
Week 1: data and baseline
We audit the decision, the outcome data and the current system (rules, ML model or frontier API) and set the baseline on your benchmark.
- 2
Weeks 2 to 3: benchmark and prototype
We build the evaluation set, measure the candidate frontier and open models on it and pick the training approach.
- 3
Weeks 4 to 6: training and evaluation
We fine-tune the model on the training split, select checkpoints on a validation set and compare the final model with the current system on the held-out test set.
- 4
Weeks 7 to 8: deployment and handover
Shadow mode or A/B test in production, monitoring, rollback and runbooks, then handover to your team.
Related case studies
- Aiteklabs joins the Anthropic Partner Network
We have been invited to the Anthropic Partner Network, giving Aiteklabs closer access to frontier models for the products we build.
- ClopinetteAI: a Cloudflare-native AI agent from Aiteklabs
ClopinetteAI is an Aiteklabs lab project exploring memory, tools, multimodal input and Cloudflare-native agent infrastructure.
- HoldsUp: we built a fact-checker for health podcasts
HoldsUp checks what podcasts claim against the published research, claim by claim. Next: paid verification for AI agents through x402 and MCP, without ever reading your health data.
Frequently asked questions
- Is a fine-tuned small model really better than GPT or Claude?
- On a narrow, repeated task with outcome data, often yes. Coinbase post-trained a 9-billion-parameter open model on its fraud outcomes for under $100 and it beat Opus 4.5 by 7.5 points of precision, 12 points of recall and 35 points of dollar-weighted recall, at less than half the latency. On open-ended tasks, frontier models still win, and we tell you when that is the case.
- What data do we need?
- Examples of the decision with its known outcome: transactions labelled fraud or not, tickets with their final category, documents with the extracted fields. A few thousand examples is usually enough to start. We handle class imbalance and data cleaning.
- Where does the model run?
- Wherever your data rules require: your cloud account, your own GPUs or a European GPU provider, with training and inference data kept in the region you choose. Cloudflare Workers AI is an option when latency matters more than data residency, since it does not run inference in a chosen region.
- Why do model upgrades break things?
- Each new model version behaves differently. In Coinbase's benchmark, several upgrades lost more than 20 points of recall. An outcome-based evaluation catches this before it reaches production, whether you use a frontier API or your own model.
- How much does it cost?
- Training itself is cheap: a single GPU for a few hours. Most of the budget goes into building the benchmark and the integration. Count six to eight weeks for a first production model, then a monthly plan if you want us to keep retraining and monitoring it.
Tell us about your project
Send a short brief with your goal, timeline and budget range. We reply within one business day with a first proposal.