What it is
A generative AI product built for your business: your copilot, your generator or your assistant, with your brand and your rules.
What's included
- Designing the experience and use cases.
- Custom generative AI architecture.
- Quality controls and guardrails.
- Integration into your platform or product.
- Iteration with real users.
How we work: service methodology
- Phase 1 · Data Audit and Goal Definition (Week 1) — Identifying the specific use case and selecting the right base model.
- Phase 2 · Data Curation and Preparation (Weeks 1-2) — Structuring training examples from the company's real material.
- Phase 3 · Model Training (Weeks 2-3) — Fine-tuning the model on the prepared data, with process monitoring.
- Phase 4 · Benchmark Evaluation (Weeks 3-4) — Comparing results against the base model before fine-tuning.
- Phase 5 · Production Deployment (Weeks 4-5) — Rolling out the fine-tuned model in the defined environment.
What we need from your team
- Sample documents, style guides or real conversations representative of the domain to train.
- A clear definition of what the model should and shouldn't say.
- Access to compute infrastructure (in-house or cloud) for training.
Who it's for
- Software companies that want to differentiate.
- Businesses with a clear generative use case.
- Teams that want their own copilot, not a generic one.
Signs you need it
- The company wants to offer a proprietary generative AI feature, but today only has access to generic market tools.
- Competitors already integrate generative AI into their product and the current offering starts to feel outdated.
- There's a clear use case for an AI copilot inside the product, but no team or architecture to build it.
- Without your own quality controls or guardrails, any internal attempt at generative AI ends up unpredictable or risky.
Expected outcome
- An AI feature that sets you apart.
- A generative experience with your brand and your rules.
- A product asset, not a loose experiment.
Why jsadsAI
We combine product design, AI architecture and quality judgment so your generative AI is reliable, useful and truly yours.
Frequently asked questions about this service
How is this different from a RAG system?
While RAG provides real-time context from documents, fine-tuning modifies the model's base behavior for a specific style or domain. One or the other (or both) is chosen depending on the case.
What problem does it mainly solve?
It reduces the need for lengthy instructions in every query and improves the model's understanding of your industry's specialized terminology.
How long does the full process take?
Between 4 and 6 weeks, depending on the volume of training data and the complexity of the domain.
What ownership do I have over the resulting model?
The fine-tuned model and its weights are documented as part of the deliverable, per what's agreed in the project scope.
Technology stack
- IA Generativa
- Diseño de producto
- Arquitectura LLM
- Guardrails
- Integración a tu plataforma