Most companies already have AI activity somewhere: a pilot, a licence, a team using it quietly. Far fewer can say where AI deserves real investment, and why that opportunity rather than the other five.
Pilots stall for reasons that have little to do with the model. Nobody owns the output. The source documents contradict each other. There is no agreed definition of correct, so nobody can tell whether it is working. The people expected to use it were never given a reason to change how they work.
Some teams need to decide, build, or operate an AI system. Others need independent judgment or better adoption of tools already in place. Each engagement stands on its own, with scope and price agreed before work begins.
Find the opportunity worth funding and define the safest path forward.
Explore →Design, evaluate, and deliver the selected system into production.
Explore →Keep the live system accurate, governed, and improving after launch.
Explore →Three phases, in order. Each has a defined objective, is scoped and priced before it starts, and can be the last one.
Senior, vendor-neutral judgment on architecture, suppliers, and priorities while your own team builds.
Explore →Turn the AI your company already pays for into daily practice, measured per team rather than assumed.
Explore →Neither is a phase, and neither requires a Celadon build. Both work alongside whatever your team is already doing.
Before spending build money, determine which opportunity has the strongest combination of business value, feasibility, organizational fit, and manageable risk. A fixed-fee engagement, typically 2–4 weeks, that ends in a recommendation you can fund — or decline — with confidence.
Every engagement is custom, but the shapes repeat. Grounded, cited, permission-aware systems built around how your teams actually work.
Grounded access to policies, documents, technical knowledge, and institutional expertise: cited, permission-aware, and answerable.
For example: internal knowledge assistants, contract and policy search, technical specification lookup.
Systems that answer, guide, qualify, and support customers, guests, members, or partners, with clean escalation when they should not.
For example: guest and member concierge, service triage, partner and distributor enablement.
Evidence synthesis for teams that read for a living, with citations and a clear trail back to the source.
For example: market and competitive intelligence, account research, executive decision support.
Systems that cut admin load, surface what is happening across the business, and keep ownership and ops decisions grounded in current data.
For example: ownership and revenue reporting, quoting and configuration support, ops dashboards with cited source data.
AnthropicxAIYou.com
DatabricksSnowflake
Microsoft
Google Cloud
Vercel
SupabasePineconeFour sectors where the same knowledge and service workflows show up again and again. The work is not exclusive to them.
Property knowledge, guest response load, pre-arrival service, and multi-property consistency.
Member and fan service, benefits and partnership knowledge, and staff enablement at scale.
Document-heavy workflows, research, firm-specific policy, and knowledge that survives turnover.
Scattered technical and product knowledge, distributor enablement, quoting, and field service.
Most AI work fails on architectural decisions, not model choice. The system is designed first: data flows, integration points, evaluation, and failure modes.
A pilot that never ships is not a result. Celadon stays involved through deployment and hands over systems that run, documented well enough for someone else to maintain.
Not tied to a platform, model, or vendor. The right tool is the one the system requires, recommended in your interest rather than a partner's.
Each phase is scoped, priced, and tied to a business objective before work begins. No per-seat licensing, no open-ended meter.
Each phase has a defined objective, is separately scoped and priced, and ends in a deliberate decision. Decision Sprints run $15,000–$40,000. Advisory retainers run $5,000–$12,000 / month. Build is scoped from the Sprint. A production phase cannot be priced honestly until the data, integrations, and failure modes have been examined. A Sprint fee is credited in full toward the first Build phase within 90 days. A customer can stop after any phase.
Objective, workstream, dependencies, stakeholders, and the acceptance criteria that define done, agreed before the phase begins.
A fixed fee for that phase, set inside a published range before work begins. New scope becomes a new agreed phase, not an invoice surprise.
Review against the criteria and choose: continue, adjust, or stop. Value and responsibilities are defined before implementation, not after.
One conversation is usually enough to tell where AI could create value, where it could not, and what is worth scoping first.
Tell us what you are evaluating, building, operating, or trying to get adopted. Email is the first step; if a call would be useful after we review the context, we will suggest one.
