LLM Development & Fine-Tuning
Fine-tuning adapts an existing language model to your specific task, format or domain by training it further on your examples. We do this where it genuinely helps — specialised classification, consistent structured output, domain language, or cost reduction at high volume — and we tell you when prompting or retrieval would achieve the same result for far less money, which is frequently the case.
The problem
Where prompting runs out
The prompt is now four thousand tokens and still inconsistent
Every edge case added another instruction. It is fragile, nobody wants to touch it in case something else breaks, it costs more on every single call, and it still produces the wrong format often enough that something downstream has to handle it.
Legal will not approve the data leaving
The workload is otherwise straightforward. It is blocked entirely on residency, contract terms, or a sector rule about where inference may happen — and no amount of prompt engineering addresses that, because the constraint is not about quality.
You are paying frontier prices for a two-class decision
Millions of calls a month doing something narrow — routing, classifying, extracting three fields — priced at the rate of the largest model available, because that is what the first version used and nothing forced a review.
What a real fix takes
The honest first step is to try to lose the argument for fine-tuning. Baseline a properly prompted general model on your task, measure it against the same test set, and if it clears your bar, stop — a substantial share of fine-tuning proposals should die here, and the ones that survive that test are the ones worth running. Where it does survive, be clear about the shape of the work: the great majority of the effort is data preparation, not training. The training run is the short, interesting part everyone pictures. The cleaning, deduplication and consistent labelling is the part that decides the outcome, and inconsistent labels teach the model to be inconsistent with impressive fidelity.
Outcomes
What changes
Consistent behaviour on a narrow task
Where a general model needs a long prompt and still varies, a fine-tuned model can produce reliable output from a short one. Consistency is often the real win rather than raw accuracy.
Lower cost at volume
A small fine-tuned model matching a large general model on one specific task can cut inference cost substantially. This only pays off above a volume threshold, which we calculate with your numbers before recommending it.
Data residency and control
Self-hosted open-weight models keep everything inside your infrastructure. For some regulated workloads this is the deciding factor regardless of cost.
Domain language handled correctly
Specialised terminology, internal abbreviations and industry conventions that general models handle inconsistently.
What we build
What we build
Training data preparation
The dominant cost and the dominant determinant of success. Collection, cleaning, labelling, deduplication, and building held-out evaluation sets. A thousand well-constructed examples routinely outperform ten thousand noisy ones.
Label consistency review
Establishing that two people applying your labelling guide reach the same answer, before any training happens. Where they do not, the guide is fixed first — a model cannot be more consistent than the data it learns from.
Fine-tuning pipelines
using parameter-efficient methods — LoRA and QLoRA — which achieve most of the benefit at a fraction of the compute of full fine-tuning, and are what almost every practical project should use.
Evaluation
Task-specific benchmarks measuring whether the tuned model actually beats the baseline. Including the honest possibility that it does not, in which case we say so rather than shipping it.
Self-hosted inference
with quantisation, batching and autoscaling — sized against real load rather than peak fantasy.
Model routing
so the tuned model handles the task it was built for and a general model handles everything else. Most systems benefit from more than one model.
Retraining process
Fine-tuned models drift as your data and language change. We build the pipeline to refresh them rather than leaving you with a model that quietly ages.
What you receive
Three things, one of which outlives the model
The training and held-out sets, cleaned and versioned
The durable asset. Base models will be superseded, and when that happens, a clean dataset means retraining is a short job rather than a repeat project. The model is temporary; the data is not.
The baseline comparison
A single document showing the prompted general model and the tuned model on the identical test set, with accuracy, consistency and cost per call for each, plus the volume at which the tuned model becomes cheaper. It is what justifies the project internally, and it is equally capable of showing that the project was not warranted — which is a legitimate and useful result.
The retraining pipeline
Data preparation, training, evaluation and promotion, runnable by your team. Fine-tuned models drift as your domain language and data change, and a model you cannot refresh is one that quietly ages into being worse than the general model it replaced.
How it works
How it works
- Week 1
Feasibility
We establish whether fine-tuning is the right tool at all, by baselining a well-prompted general model first. If the baseline meets your requirement, we recommend stopping there and say so — it is a shorter engagement and the correct answer.
- Weeks 2–4
Data preparation
Assembling and cleaning training data, resolving labelling inconsistency, building the held-out evaluation set. Consistently the longest phase and the one that determines the outcome.
- Weeks 4–5
Training and iteration
Base model selection, hyperparameter tuning, multiple runs measured against the evaluation set. Fast compared to data preparation.
- Weeks 5–7
Deployment
Inference infrastructure, quantisation, load testing, monitoring, routing and fallback.
Ongoing
Performance monitoring, drift detection, periodic retraining.
Seven weeks is typical where usable training data already exists. Where it must be created or labelled from scratch, add several weeks. The phase that surprises clients is data preparation, and specifically labelling disagreement: the discovery that two experienced people label the same case differently is common, uncomfortable, and has to be resolved before training rather than after. Be sceptical of anyone quoting a fine-tuning timeline without asking about your data first.
Technology
What we work with
Technologies we work with and reach for depending on the task. Not an exclusive stack and not partnerships — training and serving tooling turns over quickly, and provider fine-tuning availability changes, so the choice is made at the start of each build rather than carried over from the last one.
Base models: Llama, Mistral, Qwen and Gemma for open-weight work; provider fine-tuning APIs where hosted is acceptable and simpler.
Training: LoRA and QLoRA via Hugging Face PEFT, Axolotl or Unsloth. Full fine-tuning only where the case is unusually strong.
Serving: vLLM, TGI or Ollama depending on scale; GPU sizing calculated against real throughput requirements.
Evaluation: task-specific benchmarks plus held-out sets. Public leaderboards are close to useless for narrow production tasks.
Where this applies
Where this applies
Strongest where a narrow high-volume task runs constantly, where output format must be rigidly consistent, or where data cannot leave your infrastructure — which makes it most common in fintech, healthcare and legal and professional services, where residency and confidentiality frequently decide the architecture before cost does.
Weakest where the task is broad, the volume is low, or the knowledge changes frequently. Changing knowledge is a retrieval problem, not a training problem — fine-tuning a model on facts that will be outdated next quarter is a common and expensive mistake, and RAG is the correct tool for it.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
How we scope and price
Fixed scope, quoted after a feasibility assessment — and the assessment can legitimately conclude that no fine-tuning project is warranted. Three things drive the figure.
Training-data readiness
An existing, consistently labelled dataset makes this a modest engagement. Creating and labelling one from scratch dominates the budget, routinely by a factor of several.
Whether inference is self-hosted
Self-hosting adds GPU sizing, serving infrastructure and ongoing compute cost that a provider API does not. We model that cost before you commit rather than after.
How many models end up in the system
Routing between a tuned model and a general one is normal and adds integration work; a single-model deployment is simpler and cheaper.
FAQ
Frequently asked questions
RAG for knowledge, especially knowledge that changes or requires citation. Fine-tuning for behaviour — style, format, specialised task performance. If the requirement is "the model should know our documentation", that is RAG. If it is "the model should always respond in this exact structure", that is fine-tuning. They combine well.
When a well-prompted general model already clears your bar — which we test first, and which resolves a large share of enquiries. When volume is below the threshold where the cost saving pays back the build. When the thing you want the model to know changes regularly. And when you cannot assemble several hundred consistently labelled examples, because without them there is nothing to train on that will not make the model worse.
For task-specific fine-tuning, several hundred to a few thousand high-quality examples is often sufficient. Quality and consistency matter far more than volume — inconsistent labelling teaches the model to be inconsistent.
On its specific narrow task, often yes, and at much lower cost. On anything outside that task, no — and it may be worse than before. This is why routing matters.
Not with self-hosting. With provider fine-tuning APIs, it depends on their terms, which we review with you before recommending a route.
GPU cost, which is continuous rather than per-request. It becomes cheaper than API pricing above a volume threshold and more expensive below it. We calculate that threshold with your actual numbers rather than assuming self-hosting is cheaper.
Depends on how fast your domain and data change. Some models run for a year unchanged; others need quarterly refreshes. Drift monitoring tells you rather than a calendar.
Fine-tuning availability varies by provider and model, and changes over time. We check current availability at the point of scoping rather than assuming, and compare it against open-weight alternatives.
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