AI and Machine Learning Development for features evaluated on your data.

We quote the project against its data preparation, evaluation, integration and deployment scope. Inference volume, latency and any ongoing monitoring also affect the operating plan. Any per-request cost criteria need to be agreed for the individual build.

Billing follows the engagement model:

• Fixed price: outcome based, quoted per project.
• Time and materials: billed hourly, quoted per project.
• Team extension: billed monthly per team member.

We estimate the production timeline after assessing the data, prototype evidence and implementation scope. The plan identifies preparation, evaluation and deployment milestones, including access arrangements and domain-owner review.

If you first need a broader assessment of system and data readiness, the AI readiness assessment is a separate scoped offer. Its timebox does not set the duration of a model build.

We integrate the selected feature with the ERP, CRM, warehouse tooling or product interface in scope, using supported interfaces and approved access. Discovery defines input and output contracts, authentication, error handling and latency requirements with your technical contact.

AI integration services cover a wider workflow implementation; MCP development provides a separate route for model access to systems. A legacy interface may need additional work before a connected feature is feasible.

We build custom models and adapt existing ones, choosing the approach by the task's data and evaluation requirements. The prototype provides evidence for that choice.

Representative examples, labels where required and target outputs matter more than the data's ownership alone. Limited or unstructured data can mean the prototype needs preparation, retrieval or an adapted model to test the task properly. AI consulting ranks opportunities when the decision is still which task to fund.

Use retrieval-augmented generation (RAG) to supply relevant private or current information at runtime. Use fine-tuning to adapt model behavior, style or task performance, as described in Microsoft's RAG guidance.

RAG supplies source material without training that knowledge into the base model. Fine-tuning changes how the model performs the task. The methods can be combined; retrieval quality and model outputs still need evaluation against your cases.

We scope training, evaluation and inference around permitted data and deployment requirements. The build defines which records and fields may be used, and any masking needed for that use.

• Build access is least-privilege, agreed with your technical contact, and read-only wherever the work allows. Discovery has no write access to production systems.
• We work inside your cloud environment where your customers, insurers or shippers require it.
• Your data, legal and security owners confirm applicable obligations and any selected provider's processing and retention terms.

Rescue and maintenance access are scoped separately. Diagnostic assessments and audits have their own read-only, least-privilege access arrangements under NDA.

We turn the selected prototype into an integrated feature with defined evaluation and operating criteria. Release responsibilities determine how the feature is accepted for production.

• Implement serving, authentication, error handling and the integration into your systems.
• Check task results, latency and inference-cost criteria for the deployment target.
• Define monitoring, handover and any continuing evals and observability scope.

We agree quality signals, costs, drift measures and alerting thresholds with the feature owner, then implement the monitoring in scope. Drift concerns changes in production data or behavior compared with the evaluated baseline.

Available feedback and labelled cases determine how quality can be reviewed after launch. The operating scope defines review responsibilities and any retraining cadence; evaluation evidence informs whether retraining, adaptation or a data update is appropriate. Human review is defined for the task's decision risk.

We select the tools around the task, data scale and deployment requirements. Training and adaptation options include TensorFlow and PyTorch; Apache Spark supports larger-scale data processing.

Our experience includes GPT models, AssemblyAI and Whisper in owned-product work. A separate internal project-management proof of concept used Python, AWS Bedrock and LangChain. The tool choice for your feature follows its evaluation and operating criteria.

Machine learning fits a prediction or classification task when representative examples can support training and evaluation. Retrieval or model adaptation can fit language and search tasks; deterministic rules fit explicit logic, and an existing SaaS feature can meet a defined workflow.

unicrew fits when you want engineering to take a selected task through prototype evidence and a production feature, including the data work it needs. A capable internal team can also own the task when it can provide the required data preparation, evaluation and production operation.

Compare task-specific evidence and the work required to turn it into an operated feature. A model score and a production business outcome should be assessed on their own terms.

• Ask how representative cases, labels and evaluation metrics will be selected, and how the prototype supports the build decision.
• Review data preparation, integration, inference costs and agreed monitoring responsibilities.
• Confirm access, deliverable ownership, handover and the team responsible for ongoing work.

unicrew holds ISO 27001:2022 and ISO 9001:2015 certifications, renewed through a multi-stage audit with Quay Audit UK. Relevant client references provide the context for reported results.