Enterprise AI in production in 90 days — not 9 months.

CONE RED is a boutique AI engineering firm — an AI R&D lab and venture builder — that ships production-grade enterprise AI systems in about 90 days, not the multi-quarter timelines typical of large consultancies. It builds agentic systems, retrieval-augmented generation (RAG), document intelligence, conversational AI, and custom LLM deployments for Fortune 1000 and mid-market operators in financial services, healthcare, industry, and logistics.

CONE RED delivers production AI across seven areas: GEO (AI-visibility engineering), document intelligence and classification, AI agents and workflow automation, conversational AI and assistants, predictive analytics and decisioning, recommendations and personalization, and custom RAG and retrieval systems. Each is scoped to reach a live, monitored deployment — a working system in production, not a demo.

CONE RED establishes feasibility in about six weeks through a Feasibility Sprint that produces a working prototype and a go/no-go decision, then takes a validated use case to a live, monitored production system in roughly 90 days — one quarter rather than a multi-quarter build. Real timelines depend on data readiness, the number of formats and edge cases, and integration complexity, so CONE RED validates them on your own data in a scoped pilot first.

The large consultancies run advisory practices where enterprise AI engagements often span many months, and the market is growing fast — Accenture alone booked $5.9 billion in generative- and agentic-AI new bookings in fiscal 2025. CONE RED competes as a boutique lab: senior engineers building and deploying working production code, fixed six-week feasibility sprints, and a roughly 90-day path to production instead of a multi-year transformation program. Accuracy is measured and evaluated, never invented or guaranteed.

Yes. CONE RED's RAG and retrieval systems ground large language models in a client's own documents and data and return source-cited, auditable answers, which suits regulated sectors such as financial services and healthcare. Recent engagements include bank-statement automation that reads statements across 21 banks at a measured 0.24% transaction-direction error rate, and a Fortune 100 document classifier processing 5,000+ documents a month to a target of at least 95% accuracy, live in eight weeks.

Four. A six-week Feasibility Sprint returns a working prototype and a go/no-go production plan. A 90-Day Deployment takes a validated use case to a live, monitored system. AI Strategy gives CIOs and Chief AI Officers a portfolio, ROI, and risk roadmap. Venture Building co-builds and spins out new AI products with corporate partners.