Practical AI for measurable business value
Add intelligent automation, analytics and AI-assisted workflows to products where speed, prediction and decision quality matter.
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Typical use cases
Expected outcomes
Delivery Model
Frequently Asked Questions
Andgate builds AI-powered product features including large language model integration, retrieval-augmented generation systems, document automation, prediction models, AI-assisted customer workflows, analytics systems and intelligent process automation.
AI investment makes sense when you have repetitive high-volume tasks that require judgment, when you need to surface patterns across large datasets, when you want to personalize user experiences at scale, or when document processing, classification or extraction is a bottleneck in your operations.
Both. Andgate integrates existing LLM APIs such as OpenAI, Anthropic and Google Gemini where appropriate, and also builds fine-tuned models or RAG pipelines for domain-specific use cases where general-purpose AI is insufficient.
Retrieval-Augmented Generation (RAG) is an AI architecture pattern where a language model is given relevant documents or data at query time rather than relying solely on its training data. Andgate uses RAG for knowledge base assistants, contract review tools, support automation and internal documentation systems.
Andgate implements structured prompt engineering, output validation layers, human-in-the-loop fallback systems and evaluation pipelines that measure accuracy over time. Production AI features are instrumented for observability so degradation can be detected and corrected quickly.
AI feature development ranges from $8,000 for a focused automation feature to $100,000+ for a full AI product layer with custom pipelines, fine-tuned models and evaluation infrastructure. Cost depends heavily on data availability, complexity of reasoning required and required accuracy thresholds.
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Bring your product challenge, modernization need or growth constraint. We will help shape the next practical move.