Custom AI Development
Grapefruit builds custom AI applications that run in production: generative AI features inside your products, fine-tuned models that write in your brand's voice, and forecasting or prediction tools. We start with an evaluated prototype on your data, then build, integrate, secure and operate the system, and hand you the code.
A working proof of concept on your data with an evaluation set, accuracy and hallucination report and a go/no-go recommendation.
The right model per task, and fine-tuning on your content where a general model is not good enough, as we did for UNTOLD's newsletters.
AI features built into your web platform, app or internal tools, with the UX that makes people actually use them.
Deployment in your cloud tenant or an agreed EU region, access control, logging, cost controls and a documented data flow.
Dashboards for quality, cost and usage, re-evaluation on model updates, and full handover of code, prompts and evaluation sets.
Our method
Frame the use case, users and data, agree success metrics and build the evaluation set before writing code.
Prototype on your data in your tenant, compare models, measure accuracy and cost, and decide go or no-go with evidence.
Production architecture, integration, guardrails, human review, security testing, AI Act documentation and launch.
Monitoring, evaluation re-runs on model updates, model and prompt upgrades, and cost optimisation.
Proof
Ways to start
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides.
From validated prototype to a production AI system your team trusts and runs.
Your AI keeps working, stays compliant and keeps improving.
Know the real state of your platform before you invest in it.
Industries
FAQ
Use retrieval when the model needs facts that change, such as policies, products or prices. Fine-tune when it needs a style or a task pattern, such as writing in your brand voice. UNTOLD's newsletters used fine-tuned models for tone. Many systems combine both, and the prototype shows which one pays off.
You do. Code, prompts, fine-tuning data, evaluation sets and documentation are yours and handed over. Fine-tuned models live in your account with the model provider or in your cloud, so you are not dependent on us to keep using them.
We cannot promise zero errors, so we measure them. Every system has an evaluation set of real cases run before launch and after every model update, plus guardrails, source citations where facts matter, and human review where mistakes are costly.
No. We use enterprise API terms or models deployed in your cloud, where your data is not used to train the provider's models. Where fine-tuning is needed, the resulting model is private to your account. Data flows are documented and covered by a data processing agreement.
Integration work and data preparation, more than the model itself. Other drivers are the number of users and languages, security requirements such as on-premises deployment, and the level of human review. We measure these in the prototype so the production estimate is based on evidence.
A prototype takes 3 to 4 weeks. For UNTOLD, a first version of the newsletter model was ready in 1 week with one AI engineer and one copywriter. Production timelines of 2 to 6 months depend mostly on integrations and approvals.
We are model-agnostic and pick per use case based on quality, cost, language support and data residency, including commercial models such as the GPT family and open-weight models when they must run in your own infrastructure.
Related
A working AI prototype on your own data in 4 weeks, so you decide with evidence, not slides. 3 to 4 weeks, fixed scope, no obligation to continue.
We reply within 1 business day, usually the same day. Not ready to talk? Take the free AI assessment.
