Labs · Development roadmap
Vertical AI
Industry-specific AI assistants — a practical roadmap even when you don't have large proprietary datasets.
What is a vertical AI?
A vertical AI is an artificial intelligence system designed to serve a specific industry, niche, or use case, rather than trying to be general-purpose like ChatGPT or Claude.
Bakery / retail AI — forecasts product demand, automates online orders, tracks inventory, and personalizes marketing campaigns.
Why vertical AI matters
Deeper accuracy
Trained on data specific to that industry.
Better ROI
Solves real, narrow problems directly tied to business outcomes.
Easier integration
Plugs into existing tools like POS systems, CRMs, or ERPs.
Faster adoption
Less "prompt engineering" required — it's already tuned to the user's context.
Vertical AI development roadmap
A framework for developing industry-specific AI assistants even when you don't have large proprietary datasets.
Development flow
- 1
- 2
User interactions
- 3
Feedback
- 4
- 5
Fine-tuning
- 6
Eval
- 7
Continuous loop
Phase 0 — Deploy with retrieval-augmented generation (RAG)
Ship a working assistant first, powered by live data instead of trained weights.
Start with a RAG-based system that uses your existing data sources (databases, documents, APIs) to provide intelligent responses. This allows you to deploy a functional AI assistant immediately without needing large training datasets.
Phase 1 — User interactions
Collect real-world usage patterns and user queries from your deployed system.
Monitor how users interact with your AI assistant. Track queries, responses, and user behavior to understand what works and what needs improvement.
Phase 2 — Feedback
Gather explicit and implicit feedback to identify areas for improvement.
Implement feedback mechanisms (ratings, corrections, user edits) to understand where the AI succeeds and where it fails. This feedback becomes valuable training data.
Phase 3 — Synthetic data
Generate training data from user interactions and feedback patterns.
Use the collected interactions and feedback to create synthetic training datasets. This amplifies your limited real data into a comprehensive training set tailored to your specific use case.
Phase 4 — Fine-tuning
Train a specialized model on your industry-specific data.
Fine-tune a base model (like IBM Granite 4.0) using your synthetic and real data. This creates a model that understands your industry's language, workflows, and context.
Phase 5 — Evaluation
Test and measure the performance of your fine-tuned model.
Evaluate your fine-tuned model against key metrics (accuracy, relevance, user satisfaction). Compare it against the RAG baseline to measure improvement.
Phase 6 — Continuous loop
Iterate and improve through continuous feedback and retraining.
The process doesn't end. Continue collecting user interactions, gathering feedback, generating new training data, and fine-tuning. Each iteration makes your vertical AI more accurate and valuable.