VERTICAL AI

DEVELOPMENT ROADMAP · INDUSTRY-SPECIFIC AI

// 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

RAG

User Interactions

Feedback

Synthetic Data

Fine-tuning

Eval

Continuous Loop

RAG

User Interactions

Feedback

Synthetic Data

Fine-tuning

Eval

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.