LLMs, generative AI and machine learning
AI Development Services
Most AI work today is one of two things: putting a language model into a product that never had one, or building a model that predicts something specific to your business. Devs-Hive does both — scoping the use case, choosing the approach, and making it hold up once real users are on it.
What does AI development actually involve?
AI development is the work of turning a business problem into something a model can handle reliably — and then proving that it does. That means choosing between prompting a hosted model, retrieving your own data into its context, and training on your own examples; building the software around it that checks the output and falls back when the model is wrong; and measuring whether the feature works before customers see it.
The model itself is usually the smallest part. Most of the effort goes into the data, the evaluation and the integration.
Generative AI and classical ML are different projects
“AI project” now covers two kinds of work that share almost nothing operationally, and mistaking one for the other is the most common reason a budget goes sideways.
Generative work starts with a model that exists. You are not training anything — you are deciding which model to call, what context to give it, how to check what comes back, and what happens when it is wrong. That is integration, retrieval, evaluation and cost control: closer to custom software development than to data science.
Predictive work starts with your data. You are building a model to answer one narrow question — which orders will be late, which machines will fail, which customers are about to leave — and the hard part sits upstream: access, labelling, feature engineering, and whether the historical data supports the question at all.
Plenty of products need both. An assistant that answers from your documentation is generative; the model that routes a ticket is predictive. We will tell you which one your problem is — and if neither is ready, because the data is not there or a rules engine would do the job, we will say that too. When an idea needs proving first, our R&D service is the cheaper place to find out.
AI Development Services We Deliver
From a first language-model feature to predictive models in production — and the engineering that keeps either one trustworthy.
Model Integration
Putting a language model into a product that was not built around one: the API layer, prompt management, streaming, retries, rate limits, and a fallback for when the provider is down.
RAG Over Your Own Data
Retrieval-augmented generation, so the model answers from your documents and databases rather than its training data. Ingestion, chunking, embeddings, retrieval and permission checks at query time.
AI Agents & Workflow Automation
Systems that take multi-step actions — reading a queue, calling your APIs, drafting a change for a person to approve. We scope where the agent acts alone and where it must ask.
Copilots & In-Product Assistants
Assistants that work on the user’s real data inside your product — drafting, summarising, explaining a screen — with interface patterns that let people correct the model rather than only accept it.
Evaluation & Guardrails
The part most AI features skip. Test sets built from your own cases, automated scoring, output validation, input filtering, and reporting that shows when quality drifts.
Predictive Models & Forecasting
Demand, churn, risk, capacity, maintenance windows. Framing the target variable, building features from your historical data, validating against a baseline, and deploying where the decision happens.
NLP & Document Processing
Structure out of unstructured text: contracts, invoices, forms, support conversations. Classification, entity extraction and summarisation, with a review step wherever a mistake is expensive.
Computer Vision
Image and video classification, object detection, quality inspection and document capture — with an honest read upfront on how much labelled data the task needs.
Recommendation & Personalisation
Ranking for catalogues, content and offers, including the cold-start problem and the measurement setup that shows whether the new ordering beats the old one.
MLOps & Deployment
Models out of notebooks and into production: versioning, reproducible training, hosted or self-hosted inference, scaling, and the retraining pipeline that keeps them current.
Prompting, RAG or fine-tuning?
Three ways to make a general-purpose model behave like it knows your business. Most products combine them, but the order you try them in decides what you spend finding out.
| Prompting | RAG | Fine-tuning | |
|---|---|---|---|
| What it does | Instructs a general model at call time | Retrieves your content and passes it to the model | Adapts the model itself on your examples |
| Best for | Format, tone and straightforward reasoning | Answers grounded in your own documents | An output shape the model keeps missing |
| Where your data sits | Sent per request, nothing stored | Stays in your index; only matches are sent | Used for training; needs a reviewed dataset |
| Keeping it current | Edit the prompt | Re-index when the source changes | Retrain |
| Effort to set up | Hours to days | Weeks — ingestion and retrieval are the work | Longer, plus a labelled dataset |
| Cost to run | Per token, grows with prompt length | Per token, plus index hosting | Less per call, but training is added |
| Reach for it when | Always first — the cheapest test of the idea | The model needs facts it never saw | The other two work but output still varies |
Get an estimate for your AI project.
Tell us the problem you want solved and we will tell you which approach fits.
Your data, your model provider, and what you have to decide
Every AI conversation reaches the same question: what happens to our data?
It depends on choices you make. With a hosted model, your prompts leave your infrastructure and arrive with the model provider you choose. Providers differ on retention windows, on whether inputs may be used for training, and on which regions they process in — terms that are contractual, not technical. Where the answer has to be that nothing leaves, an open-weights model on your own infrastructure is a real option: it costs more to run and tends to sit behind the strongest hosted models, and that trade-off is yours to make.
Either way the engineering takes the same shape. Keep personal data out of prompts when it is not needed, redact what has to go in, log what was sent, and enforce your existing permissions at the retrieval layer instead of trusting a model to keep secrets. Teams that want the same engineers embedded long term, inside their own security and review process, usually run this as a dedicated team rather than a fixed scope.
What it takes to keep an AI feature running
Four things that decide whether a prototype becomes a feature you can afford to leave switched on.
Inference cost
Prompt length is the bill. Cost per call scales with the context you send, so a feature that looks cheap in a demo multiplies once it carries conversation history and retrieved documents. We size it during scoping and design around the number.
Latency
A model that takes several seconds changes what the feature can be. Streaming, smaller models for the easy cases and parallel retrieval buy time back — and some workflows are better run in the background than made to wait.
Evaluation
“It seemed fine when we tried it” is not a release criterion. We build a test set from your real cases and score against it, so a prompt edit or a model version bump can be compared rather than guessed at.
Drift and maintenance
Providers deprecate model versions and your documents change. Predictive models degrade quietly as the world stops resembling their training data. Both need monitoring and a named owner.
How We Run an AI Project
Deliberately front-loaded: the expensive mistakes in AI work are made before the first line of model code.
1
Frame the Problem
We start from the decision or task you want changed, not the technology. This is where we agree what “working” means and how it gets measured.
2
Check the Data
Before anything is built we look at what you have — volume, quality, labels, access — and say whether it supports the idea, needs work first, or does not fit.
3
Pilot
A narrow version on real data, built to be judged rather than demoed. The output is a decision: build it properly, change the approach, or stop.
4
Harden It
Evaluation suite, guardrails, permissions, cost and latency budgets, fallbacks. The work that separates a prototype from something you can put in front of customers.
5
Run It
Deployment, monitoring and a maintenance path for prompts, retrieval and models as they change. We hand this to your team or keep operating it alongside you.
Why Devs-Hive?
We scope before we sell
We work out whether AI is the right tool for your problem before anything else. If better reporting or a repaired data pipeline would get you there, we will say so.
Product engineers, not only modellers
An AI feature is mostly ordinary software: APIs, queues, permissions, interface, error handling. We cover that surface too, so the model is not bolted onto a product that cannot carry it.
You keep what we build
Code, prompts, evaluation sets, infrastructure definitions and documentation are yours. Nothing sits on a proprietary layer you cannot take with you.
FAQ
The questions that come up in almost every first conversation about an AI build.
How long does an AI pilot take?
Will our data be used to train someone else’s model?
Should we build an AI feature or buy a tool that has one?
What does an AI feature cost to run in production?
How do we know whether the AI feature is actually working?
Can you work with the models and cloud we already use?
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Tell Us What You Want AI to Do
Describe the task or decision you want to change, and what data you already have around it. We will come back with the approach that fits.
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