I will fine-tune and deploy a custom AI model for your use case

I will fine-tune and deploy a custom AI model trained on your own data, then hand you a live, ready-to-call endpoint that speaks your domain, your tone, and your task — no prompt-engineering gymnastics required.

If generic models keep getting your domain wrong, hallucinating your product details, or ignoring your formatting rules, fine-tuning fixes it at the weights — not with a longer prompt. I take your examples, train a model on them, and ship a deployed endpoint you can call from day one.

What you get

• A fine-tuned model trained on YOUR data — built from your labeled examples, transcripts, support tickets, documents, or task-specific input/output pairs, so the model learns your exact behavior instead of approximating it.
• A deployed inference endpoint — a working HTTPS API URL plus an auth key, so your app can send a request and get your model's response immediately. No notebook to babysit, no "it works on my machine."
• Model selection guidance — I recommend whether to fine-tune an open-weight model (e.g. Llama, Mistral, Qwen, Gemma family) or a hosted/managed fine-tune (e.g. OpenAI, together-style providers), based on your latency, privacy, and budget constraints — and I explain the trade-off honestly before we train.
• A data preparation pass — I clean, de-duplicate, format, and split your dataset into train/validation sets, and flag examples that will hurt the model. Bad data is the #1 reason fine-tunes fail; I treat this as real work, not an afterthought.
• An evaluation report — before/after comparison on a held-out test set, with concrete examples showing where the fine-tuned model beats the base model on YOUR task.
• Integration handoff — a short README with the endpoint URL, request/response schema, a working curl example, and a code snippet (Python or JavaScript) so your developer can wire it in within the hour.
• A reproducible training recipe — the config, hyperparameters, and data format used, so the model can be retrained or extended later without starting from zero.

Plans

	Basic	Standard	Premium
Best for	A focused single-task model	A production-grade domain model	An ongoing, owned ML capability
Model	One open-weight or hosted fine-tune	Model selection + base-model comparison	Multi-candidate bake-off, best one shipped
Data prep	Light cleanup of a dataset you provide	Full cleaning, formatting, train/val split	Deep curation + synthetic example generation guidance
Training	Single fine-tuning run	Iterative runs with hyperparameter tuning	Extended tuning until eval targets are met
Evaluation	Basic sanity check	Held-out test set + before/after report	Full eval suite + edge-case stress testing
Deployment	Hosted endpoint, ready to call	Endpoint + auth + integration README	Endpoint + your-choice hosting (your cloud or mine)
Handoff	Endpoint + quick-start	Endpoint + code snippets + recipe	Full recipe, retraining docs, walkthrough call
Revisions	One revision round	Two revision rounds	Iterate until acceptance criteria pass
Hosting	Endpoint live for the project window	Optional monthly hosting/retainer	Optional monthly hosting + periodic retraining

All tiers deliver a real, working endpoint. Monthly hosting and retraining are available as an add-on retainer when you want the model kept live and improving over time.

How it works

1. You send me a brief and a data sample. Tell me the task ("classify support tickets," "answer questions about our manual," "rewrite copy in our brand voice") and share a sample of your data so I can assess feasibility.
2. I scope it honestly. I confirm whether fine-tuning is the right tool, recommend a base model, and tell you what your data can and cannot deliver — before you commit to a tier.
3. I prepare your data. Cleaning, formatting into the correct training schema, de-duplication, and a held-out test split.
4. I fine-tune the model. Training runs with monitored loss and validation metrics; I iterate on hyperparameters where the tier allows.
5. I evaluate it. Side-by-side base-vs-fine-tuned results on your held-out set, with example outputs you can read.
6. I deploy the endpoint. A live HTTPS API with an auth key, tested end-to-end.
7. I hand it over. You get the endpoint, request/response schema, working code samples, and the training recipe — plus a revision round to dial it in.

Why choose this

• The endpoint is the deliverable, not a slide deck. You finish with something your app can actually call, not a research write-up.
• Honest scoping up front. If your task is better solved by retrieval (RAG) or a good prompt than by fine-tuning, I'll say so before you pay — fine-tuning isn't always the answer, and I won't sell you a training run you don't need.
• Data quality is treated as the real job. Most failed fine-tunes die on messy data; I budget real effort here instead of dumping your raw file into a trainer.
• Open or hosted — your call, informed. I work across open-weight models and managed fine-tuning providers, so the recommendation fits your privacy, latency, and cost needs rather than whatever I happen to prefer.
• Reproducible. You receive the recipe, so you're never locked into me to retrain or extend the model later.

Who it's for / use cases

• SaaS teams automating support: a model fine-tuned on your past tickets that triages, tags, or drafts replies in your house style.
• Founders with a domain corpus (legal, medical, financial, technical docs) who need accurate, on-domain answers a generic model gets wrong.
• Agencies and content teams that need a model locked to a specific brand voice or editorial format, consistently, at scale.
• Developers who are tired of 2,000-token system prompts and want the behavior baked into the weights instead.
• Ops and data teams doing structured extraction or classification where a tuned small model is cheaper and faster than a giant general one.
• Startups that want to own an ML capability — endpoint plus recipe — rather than rent a black box forever.

FAQ

Q: What data do I need to provide? Ideally a few hundred to a few thousand high-quality examples of the input and the output you want. For some tasks fewer works; I'll tell you honestly after seeing a sample whether your dataset is enough.

Q: Do you fine-tune open-source models or hosted ones like OpenAI? Both. I recommend open-weight models (Llama, Mistral, Qwen, Gemma, and similar) or managed hosted fine-tunes depending on your privacy, latency, and budget needs, and explain the trade-off before we start.

Q: Will the endpoint stay live after delivery? The endpoint is delivered live and working. Keeping it hosted long-term is available as an optional monthly retainer; alternatively, on the Premium tier I can deploy into your own cloud account so you fully control hosting.

Q: Is my data kept private? Yes. Your data is used solely to train your model, is not shared, and can be deleted after delivery on request. For strict requirements, we can use open-weight models so nothing leaves an environment you control.

Q: How is "it actually got better" proven? Every Standard and Premium order includes an evaluation on a held-out test set the model never saw during training, with before/after examples so you can judge the improvement on your real task.

Q: What if the results aren't good enough? Each tier includes revision rounds, and Premium iterates until agreed acceptance criteria pass. If your data fundamentally can't support the task, I'll have flagged that during scoping — not after taking your order.

Q: Can the model be retrained later as my data grows? Yes. You receive the training recipe and data format, so the model can be retrained or extended at any time, by you or by me under a retainer.

Q: Do I need ML experience to use the result? No. You get a plain HTTPS endpoint with an auth key and copy-paste code samples. If you can call any web API, you can use your model.