AI and Machine Learning Development for models trained on records only you hold.

We scope it with you rather than publish a number that would be wrong for almost everyone. Five things move it: how ready your data is, whether the answer is retrieval or a trained model, how high the accuracy bar has to be, how much inference you will run, and whether the model can sit behind a hosted API or has to stay in your environment. Inference is the line buyers forget, so the build sets a cost budget per request rather than discovering one on the first invoice.

The state of your data sets the clock, not the modeling. If your data is accessible, labelled and owned by someone, a prototype with a real accuracy number comes early and the rest is productionizing and integration. If records sit across several systems with no shared key, the first stretch is data engineering. We quote the date after the data audit, because that is when the date can be a real one. If you do not yet know what your records can support, the fixed-scope AI readiness assessment answers that question on its own.

Yes, and on most builds the integration is the larger half of the work. A model is only useful where a decision gets made, so we wire it into the ERP, CRM, warehouse or product surface that consumes the output. Where the model is one step in a larger workflow, our AI integration services cover the plumbing and MCP exposes your systems in a permissioned way. Legacy systems with no API are a real constraint, and we name the integration route for them in discovery, so the estimate carries it from the start.

Both, and we pick the one that fits the problem. Many tasks are solved best by combining an existing foundation model with your data through retrieval or light fine-tuning, which is faster and cheaper than training from scratch. We build custom models when your data is proprietary, your accuracy or latency needs are strict, or no general model handles the task. Still deciding? Our AI consulting maps the opportunity first.

Use RAG (retrieval-augmented generation) when the model has to answer from your own documents, when that information changes often, or when you need source citations: it grounds the model in your data without retraining, so an update is a re-index. Use fine-tuning when you need consistent tone, format or domain behavior that prompting cannot hold. Many production systems use both.

We start with a data audit and scope the build around your constraints. unicrew is ISO 27001:2022 and ISO 9001:2015 certified, renewed through a multi-stage audit with Quay Audit UK, and we work within GDPR, HIPAA or PCI-DSS where they apply. On a build engagement that means least-privilege access agreed with your technical contact, read-only wherever the work allows, no write access to production systems during discovery, self-hosted or private models wherever the training data cannot leave your environment, and masking sensitive fields out of training sets and logs. Where your own customers, insurers or shippers require it, we work inside your cloud environment rather than ours. unicrew has five offices: Ukraine, Poland, Estonia, the UK and the US.

The prototype is the easy part. We wrap the model in the serving, authentication, error handling and cost controls it needs under real load, then deploy it into your stack and build the integration into your systems around it. Nothing ships without an evaluation showing its accuracy on your real cases; evals and observability afterwards are their own engagement.

We instrument the model in production to track accuracy, cost and drift, the slow decay as real-world data moves away from what the model was trained on. When quality slips we retrain or tune, rather than waiting for a user to report a bad output. For higher-risk decisions a human stays in the loop.

TensorFlow and PyTorch to train and fine-tune models, Apache Spark where the training data is past what one machine will process, and hosted LLM tooling for language work. Our own AI products, Snaplore and Talkmetry, run on Whisper AI, AssemblyAI and GPT models. Our internal LLM and agent builds are a separate stack, on Python, AWS Bedrock and LangChain for orchestration.

Early on, the most useful thing we do is match each task to the right build. Machine learning is the right tool when the pattern sits in records only you hold, the logic cannot be written down as a rule, and someone can state what a correct answer looks like. Where the logic is already written down, we build it as rules; where there is no data to learn from yet, the data work comes first. Where an AI feature inside a SaaS you already run covers the workflow end to end, the trade is that you cannot tune its accuracy or see why it decided what it decided, and tuning that accuracy is what a model of your own gives you. We are the right firm when the business case is an operational outcome, the kind our own agent work was measured on (timely work-time logging across the team); when the data work is part of the plan, because every model we price starts from audited data; and when what you need is production engineering on proven models.

Compare below the headline, on four things you can check. The reviews behind the rating: each quotation above links out to the client's own Clutch review. The certifications and who audited them, which matters because a training set is a copy of your data sitting somewhere new: ours are ISO 27001:2022 and ISO 9001:2015, audited by Quay Audit UK. Who does the work, because a model gets retrained when it drifts and whoever chose your labels should still be reachable: unicrew has 100+ senior in-house engineers across six countries. And the evaluation set a team would build first: ours is a held-out set of your real cases, scored before the build is funded, and that score decides the build.