We build LLM-powered features and AI infrastructure that ship to real users: not proof-of-concept demos that sit on a shelf.
AI development for a business product is the engineering of features that use large language models in production: retrieval-augmented generation (RAG) pipelines that answer from a company's own documents, LLM-powered features inside an application, and the logging, tracing and evaluation that show how the feature performs. It combines machine learning engineering with ordinary software discipline.
GrossiWeb is a web development agency founded in 2006 and based in Atlanta, Georgia, serving Raleigh, Los Angeles and businesses nationwide. Its AI builds service develops RAG pipelines, integrates OpenAI and Anthropic models into products with prompt engineering, streaming and cost management, and adds observability, and is quoted after a scoping call.
Building with AI requires both ML engineering and software engineering discipline. We bring both: architecting RAG systems, building LLM-powered features, and deploying everything with the reliability and observability that production requires.
Document ingestion, embedding, vector storage (Pinecone/pgvector), and retrieval: built for your specific data.
OpenAI and Anthropic API integrations with prompt engineering, streaming, and cost management.
Logging, tracing, and evaluation infrastructure so you know how your AI is performing.
18%
of US firms used AI in a business function, Nov 2025 to Jan 2026; 32% when weighted by employment
58%
of US small businesses say they use generative AI, up from 40% in 2024 and 23% in 2023
88%
of organizations surveyed use AI in at least one business function, up from 78% in 2024
5%
of custom enterprise AI tools reach production; most stall on brittle workflows and tools that don't learn
An AI development company builds features that use language models inside a product and ships them to real users rather than leaving them as demos. GrossiWeb builds RAG pipelines (document ingestion, embedding, vector storage in Pinecone or pgvector, and retrieval tuned to the client's data), LLM feature integrations on the OpenAI and Anthropic APIs, and the logging, tracing and evaluation infrastructure that shows how each feature performs.
A demo answers a handful of hand-picked questions; a production feature has to handle real inputs, fail safely and be measured. GrossiWeb defines the failure modes and evaluation set before building, adds streaming and error handling, logs every request, and reviews evaluation results after launch so prompt or retrieval changes are made from evidence. Model choice is kept swappable so a newer or cheaper model can be tested without a rewrite.
GrossiWeb quotes AI builds after a scoping call and does not publish a starting price, because a RAG pipeline over a few hundred documents and a customer-facing feature inside an existing product are different projects. The quote separates the build from the model and hosting usage fees, which vendors bill to the client directly and which GrossiWeb helps estimate from measured usage.
The best fit for a small business is a firm that audits processes before proposing tools, builds features it can also maintain, keeps the client's data and accounts in the client's name, and quotes fixed projects. GrossiWeb does the audit through AI strategy consulting and the build here, with deployment into the tools a team already uses described under AI and automation.
GrossiWeb quotes every AI build and automation after a scoping call; the price depends on the data sources, integrations and evaluation work involved, and model usage fees are billed by the vendor separately. No flat monthly figure is published. Smaller automations that connect existing tools are often scoped as workflow automation rather than a custom build, and an AI agent is quoted as its own project.
Every GrossiWeb AI build ships with logging, error alerting and an evaluation set, so a failure or a change in model behavior shows up in the monitoring before customers report it. Model versions are pinned and upgrades are tested against the evaluation set first. Ongoing fixes are covered by a maintenance and support plan; the automation and scale solution describes the operating model.
Development work rarely stands alone. These are the services we most often pair with this one.