AI development is the process of designing, building, and deploying artificial intelligence systems, including LLM-powered applications, AI agents, RAG systems, computer vision tools, and ML models. AppVerticals builds custom AI products from architecture stage, selecting the model approach, designing the data pipeline, and setting accuracy benchmarks before writing code.
A focused AI MVP typically takes 8 to 12 weeks from architecture sign-off to production deployment. An enterprise-scale AI product or platform typically takes four to nine months, depending on data readiness, compliance requirements, and integration complexity. AppVerticals scopes timelines at the architecture stage, not as estimates provided after contract signature.
Build custom when your use case requires proprietary data, specific accuracy requirements, compliance architecture, or integration with existing systems that off-the-shelf tools cannot accommodate. Use off-the-shelf when your requirement is standard and you have no data privacy or compliance constraints. AppVerticals can run a scoping session to help you make this assessment before committing to either path.
A specialized AI development agency selects the model architecture before writing code, defines accuracy benchmarks upfront, applies cost controls from sprint one, and includes post-launch monitoring as a standard deliverable. A regular software company integrates a model API without making those decisions, producing a system that performs in a demo and fails in production when the API changes pricing or the model drifts.
Generative AI produces outputs: text, images, summaries, code, or responses to a prompt. Agentic AI takes actions: it receives a goal, breaks it into steps, and executes those steps across connected systems without a human directing each one. A chatbot that answers customer questions is generative. A system that receives a customer query, looks up the account in your CRM, drafts a response, creates a support ticket, and routes it to the right team is agentic.
AppVerticals builds with foundation models from OpenAI, Anthropic, Google Gemini, AWS Bedrock, Meta Llama, and Mistral, as well as open-weight models for on-premise deployments. Model selection is based on your use case, data requirements, and cost constraints. We have no preferred vendor.
IP and data remain with the client. AppVerticals signs an NDA before any technical discussion begins. Your product concept, architecture decisions, training data, and proprietary data used in model builds remain entirely under your ownership and control.
We address hallucination risk at the architecture stage by selecting the right approach for the use case: RAG for knowledge-intensive applications, fine-tuning for domain-specific accuracy, and grounding mechanisms where required. We define and test accuracy benchmarks at every sprint and post-launch monitoring flags deviation before it reaches end users.
PUBLISH BLOCKER. This answer requires Faiq Ali to supply a confirmed cost range before publish. Reference point available from the portfolio: a production RAG chatbot build (SwiftSales AI Bot) has a documented build cost on file. Faiq Ali to confirm whether this can be referenced directionally and whether a broader range exists for the FAQ answer.
