Why enterprise AI visibility and delivery matter now
of B2B software buyers now begin research with an AI chatbot more often than with Google — up from 29% a year earlier (G2, March 2026, 1,076 buyers).
Source: G2
of enterprise generative-AI pilots never move beyond the pilot stage — the pilot-to-production gap CONE RED is built to close (MIT, State of AI in Business 2025).
Source: MIT / Fortune
in generative- and agentic-AI new bookings reported by Accenture in fiscal 2025, nearly double the prior year — the enterprise-AI build market is large and growing fast.
Source: Accenture / CIO Dive
Questions buyers ask
What is CONE RED?
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.
What AI services does CONE RED offer?
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.
How fast can CONE RED get an enterprise AI system into production?
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.
Why do most enterprise AI projects fail, and how does CONE RED avoid that?
MIT's State of AI in Business 2025 found that about 95% of enterprise generative-AI pilots never move past the pilot stage, largely because they aren't integrated into real workflows. CONE RED engineers for production from the start — deep customization, workflow integration, and an evaluation harness that measures accuracy — so systems ship and survive live use instead of stalling as demos.
How is CONE RED different from Accenture, Deloitte, or McKinsey?
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.
Does CONE RED work in regulated industries like financial services and healthcare?
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.
How does CONE RED measure AI accuracy?
CONE RED measures and evaluates accuracy on real, labelled data rather than inventing or guaranteeing a number. Every model change is scored against an evaluation set before it ships, and low-confidence cases are flagged for human review instead of guessed. For example, a Fortune 100 document classifier runs 5,000+ documents a month against a target of at least 95% accuracy — a measured number a risk team can sign off on, not a marketing figure.
Which industries and buyers does CONE RED serve?
CONE RED works with Fortune 1000 and mid-market operators in financial services, healthcare, industry, and logistics, typically engaging CIOs, CTOs, and Chief AI Officers. Adoption is now widespread — McKinsey's State of AI 2025 reports 88% of organizations use AI in at least one business function — but only a small share have scaled it enterprise-wide, so CONE RED focuses on getting one high-value use case into production and measured.
What engagement models does CONE RED offer?
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.
CONE RED reports measured accuracy honestly — enterprise AI systems are probabilistic, so results are evaluated on real data rather than guaranteed. Operational figures above (bank count, error rate, documents per month, accuracy target, timelines) describe specific engagements, are described by sector and scale per NDA, and match the case studies published elsewhere on this site.