# Cocoon AI - Full Content > Cocoon AI helps ambitious mid-market businesses achieve outsized impact with AI. We are advisors and doers, working from the boardroom to production. Based in Bellevue, Washington. Senior team, no pilot purgatory. Website: https://cocoon-ai.com Email: hello@cocoon-ai.com Phone: (415) 638-9932 Location: 800 Bellevue Way NE, Suite 500, Bellevue, WA 98004 ## Additional Context Files - [Services & Capabilities](/context/services.txt): Detailed engagement shapes, capability areas, pricing model, and FAQs. --- ## Home Page ### Headline Where AI strategy becomes reality. ### Subheadline Cocoon AI helps ambitious businesses achieve outsized impact. We are advisors and doers — shipping AI strategy, custom software, 3-D digital twins and more. Boardroom to production. ### Wherever you are with AI, we meet you there A senior team that shapes the engagement to your situation, not a template. - **You're at the front of this.** Your board is asking about AI. Your general counsel is concerned. You need senior AI judgment on strategy, policy, and governance. → AI Advisory Partner. - **You can see what to build, but not how.** You have the vision and the budget; you need a senior team that can architect and ship it. → Build Engagement or AI Operating Partner. - **You've started, but it's not shipping.** A pilot was greenlit, a demo looked promising, production never showed up. → AI Prototype Sprint or Build Engagement. - **Something's running, but no one's tending it.** Models drift. Integrations break. Prompts age. → Managed AI Operations. ### Breadth and depth in AI, for your ambitious business Six capability areas across our engagements: - **AI Strategy & Governance.** Roadmap, AI policy, vendor selection, board-level guidance. - **Custom Software & Apps.** Production AI systems built around your operations. - **Reliable Automations.** Workflow automation that holds up under real-world load. Most teams asking for AI agents actually need automation. - **Agentic AI.** Multi-step autonomous systems where the work genuinely calls for them. Used selectively, not as a default. - **3-D Digital Twins & Immersive Experiences.** Real-time 3-D environments for training, simulation, and operations. - **Real-Time Intelligence.** Dashboards, telemetry, and decision systems that surface signal from operational data. ### Latest from CAI Spark The three most recent CAI Spark posts, linking to the full index at https://cocoon-ai.com/blog. See the Blog section below for the posts themselves. --- ## Services Page ### Hero Ready when you need us. Five engagement shapes, from a six-week prototype to deeper, embedded work that runs with your team. ### Engagements that match your ambition **Embedded Engagements** — the same senior people, in the room with your team, doing the work. **Project Engagements** — a clear deliverable, a defined timeline, a scope that ends. ### AI Advisory Partner (Embedded) Senior AI judgment for your executive team. Fractional Chief AI Officer leadership without the cost of a full-time hire (full-time CAIO compensation typically runs $400K to $700K). Includes a dedicated senior AI advisor, bi-weekly working sessions with executives, on-demand support for board questions and architecture reviews, quarterly executive readouts, and access to AI policy frameworks and governance templates. No build team. Minimum 3 months. ### AI Operating Partner (Embedded, Flagship) A fractional Chief AI Officer plus the team that builds and operates the systems they recommend. Embedded for the long term. One price covers leadership and delivery. Month 1: Operating Diagnostic and Top 3 Opportunity Report. Months 2 through 6: Build and ship cadence against the prioritized opportunities. Minimum 6 months. Most fractional CAIOs are advisory only. The AI Operating Partner is the senior advisor and the build team in lockstep. ### AI Prototype Sprint (Project) A working AI prototype in your environment, in six weeks. Real data. Real software. Fixed scope. Weeks 1 to 2 cover problem definition, data access, and architecture. Weeks 3 to 5 are build. Week 6 is deployment, handover, and executive readout. You leave with a functional prototype, a production roadmap, a decision document with cost and timeline estimates, and an executive readout. ### Build Engagement (Project) Production AI systems delivered on milestones. Fixed scope, architecture through deployment and handover. Deliverables include the production system deployed in your environment, technical documentation, training, monitoring, and optional transition to Managed AI Operations. ### Managed AI Operations (Project) Ongoing care for the systems we ship. Model drift monitoring, prompt and agent tuning as Claude, GPT, and Gemini evolve, integration maintenance, incident response with defined SLAs, and quarterly optimization reviews. ### Why Us - **We build what we recommend.** No handoffs from strategists to a separate build team. - **Your business first.** Engagements are shaped to your situation, not a template. - **Strategy, not tool sales.** No reseller agreements, no kickbacks. We recommend what fits your problem. - **Outcomes, not activities.** Every engagement names what gets shipped before it starts. - **Small on purpose.** Senior team. The person on the sales call is the person on the work. ### Who we serve Mid-market companies with $50M to $1B in revenue. CEOs, COOs, CTOs, VPs of Operations, IT Directors. Industries: industrial, hardware and IoT, manufacturing, distribution, professional services. Generalists with depth in operations-heavy businesses. ### The math behind a fractional CAIO - $400K to $700K is typical full-time Chief AI Officer compensation. - Fewer than 5% of enterprise AI pilots reach production. The other 95% sit in pilot purgatory. - 6 weeks is the AI Prototype Sprint format, designed to short-circuit pilot purgatory. --- ## Insights Page ### Hero AI moves fast. Here's what we're seeing from the front lines. ### Why custom software economics have changed The old reality: building custom software took six to twelve months, cost hundreds of thousands of dollars, and required a long spec phase. So most mid-market teams settled for SaaS that almost fit their business. What's changed: AI tooling collapses the build timeline. What used to take months now takes weeks. The spec phase is shorter because we can show working software early. The economics of custom software now compete with SaaS configuration time on many problems. ### Five signals that custom makes sense - Your operations have a unique shape that off-the-shelf software can't accommodate without heroic configuration. - The cost of SaaS integration glue exceeds the cost of building the workflow you actually want. - You have a competitive insight that depends on operating differently from your peers. - Multiple SaaS subscriptions could be replaced by one focused system. - Your operational data is your moat, and you don't want to give it to a SaaS vendor's training set. ### Bottom line Software can inspire delight in the everyday. Not everything should be custom. But more should be than is. --- ## Team Page ### Hero The team behind the work. Senior operators with decades of experience. ### Jamie Rehberg — Co-Founder and CEO Operations leader. 17 years at Microsoft leading organizations of 50 to 500 people across engineering, support, and customer success. ### Bob Berry — Co-Founder and CTO 20+ years building systems that adapt to change, including real-time 3-D engines, backend platforms, and games shipping to millions. Co-founded Uber Entertainment, helped create the platform that became PlayFab (acquired by Microsoft). ### How we work together - **They've lived it.** Both founders have run real operations at scale. - **Builders, not talkers.** Senior operators who ship. - **They stick around.** The senior operator on the call is the senior operator on the work, for the whole engagement. --- ## Careers Page ### Hero Build software that actually gets used. We are hiring senior engineers who are excited about AI as a force multiplier. ### Open roles - **Senior Software Engineer** — Full-time, Remote (US). - **Infrastructure & Platform Engineer** — Full-time, Remote (US). ### Who thrives here Engineers who can hold both the customer problem and the technical detail in their head. People who ship working software quickly and resist over-engineering. People who treat AI as a tool, not a religion. ### What we value - AI changes everything. We use it daily. - Ship working software quickly. - No over-engineering. Build the thing that solves the problem; revisit when it stops fitting. --- ## Contact Page ### Hero Tell us what you're working on. We learn what is going on and recommend the right shape. If we are not the right fit, we will point you somewhere better. ### Every engagement starts with a 30-minute strategy call Email: hello@cocoon-ai.com Phone: (415) 638-9932 Address: Cocoon AI, PBC, 800 Bellevue Way NE, Suite 500, Bellevue, WA 98004 ### Who this is for - Mid-market companies, typically $50M to $1B in revenue. - CEOs, COOs, CTOs, VPs of Operations who need senior AI judgment without paying a $400K-plus CAIO salary. - Boards weighing AI strategy decisions. - Operators with a working pilot that has not shipped to production. --- ## Common Questions Q: What is a fractional Chief AI Officer? A: A fractional CAIO is a part-time senior AI executive who provides AI leadership without the cost of a full-time hire. They typically advise on strategy, policy, vendor selection, and governance. Q: How is the AI Operating Partner different from a standard fractional CAIO? A: Standard fractional CAIOs are advisory only. The Cocoon AI Operating Partner includes the build team, so one fee covers leadership and shipped systems. Q: What is pilot purgatory? A: Fewer than 5% of enterprise AI pilots produce measurable business value. The other 95% sit in pilot purgatory: working demo, no production system, eroded board trust. The fixed-scope Prototype Sprint format is designed to prevent this. Q: How is Cocoon AI different from a Big Four consulting firm? A: Big Four firms can deliver everything we do, often at three to five times the price, frequently with junior analysts doing the work. Our lane is senior talent at mid-market pricing. Q: How fast does the Prototype Sprint deliver working AI? A: Six weeks from kickoff. Weeks 1 to 2 cover problem definition, data access, and architecture. Weeks 3 to 5 are build. Week 6 is deployment, handover, and executive readout. Q: Do we actually need AI agents, or do we need automation? A: Most teams asking for AI agents need reliable automation. AI agents make sense for genuinely multi-step, autonomous decision-making problems. Reliable automation makes sense for high-volume, repeatable workflows. Cocoon AI helps clients tell the difference before any code is written. Q: What industries does Cocoon AI work with? A: Hardware and IoT, manufacturing, professional services, distribution, and operationally complex sectors. Generalists with depth in operations-heavy businesses. Q: How much of my team's time does an engagement take? A: Typically two to four hours per week of client time, concentrated around weekly working sessions and architecture decisions. --- ## Blog (https://cocoon-ai.com/blog) Field notes from the front lines of AI consulting for mid-market companies. Written by the founders. Posts listed newest first; full post content below. ### Post: Why Most Businesses Don't See ROI from AI - URL: https://cocoon-ai.com/blog/roi-from-ai - Author: Jamie Rehberg (Co-Founder and CEO) - Published: 2026-07-15 - Category: AI Strategy Quick answer: Most businesses see no ROI from AI because they buy the tool and skip the work that produces the return: picking one measurable workflow, teaching the AI how the business actually runs, and keeping an experienced person beside the team until the new way of working sticks. Key points: - Budgets keep climbing while returns stay flat. S&P Global Market Intelligence reported in March 2025 that 42 percent of enterprises had abandoned most of their AI initiatives, up from 17 percent the year before. Bain's June 2026 survey of 951 companies found 90 percent raised AI budgets anyway, while about 40 percent of those measuring savings came in under 10 percent. - AI is not the problem. MIT's Project NANDA found about 95 percent of organizations investing in custom enterprise AI tools saw no measurable P&L return, but employees at more than 90 percent of companies were already using personal AI tools productively. The value shows up, just not where companies are buying it. - BCG's formula for succeeding with AI: 10 percent algorithms, 20 percent technology and data, 70 percent people and process. Most companies fund the 30 and skip the 70. ROI was never inside the tool; it lives in the practice around it. - MIT's stalled pilots shared one trait: the tools did not learn from or adapt to the workflow, so every output still needed the judgment the tool was supposed to replace and the hours never came back. - Four behaviors of companies that get a return: (1) start with one measurable workflow, not a platform; (2) name the number before anything gets built; (3) teach the system how the business actually runs, including the exceptions nobody wrote down; (4) put an experienced operator on the bench until the new way of working is routine. - Cocoon AI's rule before any engagement: if we cannot measure the outcome, we do not propose the project. Q: How long does it take to see ROI from AI? A: Weeks for a first measurable result, when the scope is one workflow with a number attached. Broad, platform-style rollouts take years to produce a number, which is why we do not recommend starting there. Q: Why did our AI pilot fail even though the demo looked great? A: The demo ran on generic examples and your business runs on specifics. Off-the-shelf tools do not learn your exceptions, your data, or your judgment calls, so output stalls at almost-right, and almost-right still needs a person. Q: Should we buy AI tools or build something custom? A: Buy when a packaged tool covers the job with only minor workarounds. Build a thin custom layer when the process is genuinely yours, when systems need to talk to each other, or when off-the-shelf keeps fighting how you work. Q: How do we measure ROI from AI? A: Name one metric before the project starts: hours returned per week, error rate, quote turnaround, days to close. Capture the before value, ship the smallest version that can move it, and read the after value on a set date. ### Post: Custom Software in Weeks, Not Months - URL: https://cocoon-ai.com/blog/custom-software-in-weeks - Author: Bob Berry (Co-Founder and CTO) - Published: 2026-07-12 - Category: AI Strategy Quick answer: Custom software that once took six to twelve months now ships in weeks. AI-assisted development changed the math, so the real question is no longer build versus buy. It is the fastest path to software that fits how you work. Key points: - Custom software earned its bad reputation honestly: a McKinsey and Oxford study of 5,400 IT projects found large projects run, on average, 45 percent over budget and 7 percent over time. - AI-assisted development cut both the cost and the calendar. AI handles the repetitive parts of writing software; senior people spend their hours on judgment and the parts unique to the business. Work that took six to twelve months now finishes in weeks. - "Weeks" means working software you can use, not a finished platform on day one. The first useful slice ships fast (a tool the team can open, a workflow that runs), then grows piece by piece. This is the "growing organic software" idea in practice. - Working software replaces requirements documents: you react to something you can click, not a paragraph you have to picture. Wrong assumptions surface in week two, when changing them is cheap. - Five signals custom software makes sense: (1) your processes are a competitive advantage, not a commodity; (2) off-the-shelf tools require significant workarounds; (3) you are integrating multiple systems that do not talk to each other; (4) you need visibility into operations your current tools do not provide; (5) your business changes faster than vendor roadmaps can keep up. None of these is about company size. - The bottom line: the question is no longer build versus buy. It is the fastest path to software that fits how you work. Sometimes that is a SaaS tool. Increasingly it is custom software built in weeks. Often it is both. Q: How long does it take to build custom software now? A: With AI-assisted development, work that used to take six to twelve months often ships in weeks, with working software on screen within the first few weeks. Q: When does custom software make more sense than buying SaaS tools? A: When off-the-shelf tools fight your real workflow: your processes are a competitive advantage, packaged tools force heavy workarounds, systems do not talk to each other, or your business moves faster than vendor roadmaps. Q: What happens if we need to maintain it ourselves later? A: You own the result. Cocoon AI uses standard technologies, documents as it goes, and leaves each project ready for AI coding assistants. Hire your own team, keep working with Cocoon AI, or both. ### Post: We Built Our Own Back Office Automation - URL: https://cocoon-ai.com/blog/back-office-automation - Author: Jamie Rehberg (Co-Founder and CEO) - Published: 2026-08-19 - Category: AI Strategy Quick answer: Cocoon AI built its own back office automation instead of buying it. Software built for everyone is hard to configure for how one firm actually works, so the firm built its own one piece at a time around its real workflow. Two days of monthly admin is now about an hour. Key points: - The monthly cycle for a professional services firm of about a dozen people: tally hours against each person's allocation, reimburse expenses and specialized software, itemize in QuickBooks, then invoice every client in that client's own format, with travel invoices following different rules again. - The work was not hard, it was careful, and careful takes time. A bookkeeper helped and it still consumed a few days a month. - The software, called Billie, was built one piece a month in the order the pain appeared: hours entry and approvals, expenses with receipts, client travel invoices, contract change orders, contract renewals, cash flow projections. - Nothing was specified up front. Each month the most painful remaining piece was built and used before the next one started, which is why the result fits. - A firm of a dozen people can build this because the platform came first: an internal set of Cocoon AI apps and agents sharing authentication, deployment and the guardrails that make them safe to point at real company data. - Software sold to every firm has to turn each firm's specifics into settings, which relocates the work rather than removing it. - Billie is deliberately not fully automated. The arithmetic is automated so people stay in the reviewer and approver chair for the decisions: whether a client is being undercharged, whether the firm can afford a raise. - Where to start: the piece you dread, not the piece that takes longest. Dread marks the step where a mistake is expensive and the checking is manual. Q: Should we build or buy back office automation? A: Buy first if a tool genuinely fits how you already work. Build when the fit is the problem and you keep working around the software instead of with it. The signal is not cost, it is how many of your real rules the tool makes you abandon. Q: How long does back office automation like this take to build? A: It was never a project with a start and an end. One piece a month, each small enough to finish and use before the next started. Q: Does building this replace a bookkeeper? A: No, it changes what gets handed over. The collating and itemizing is done before anyone looks at it, so professional help is spent on the parts that need a professional eye. Q: What is the first piece worth automating? A: Whatever everything else depends on being correct. Here that was hours entry and approvals, because automating invoices on top of hours nobody trusts just produces wrong invoices faster. ### Post: How to Stop AI Training on Your Data - URL: https://cocoon-ai.com/blog/stop-ai-training-on-your-data - Author: Jamie Rehberg (Co-Founder and CEO) - Published: 2026-08-24 - Category: AI Strategy Quick answer: On most consumer AI plans, training on your conversations is the default, and paying for a subscription does not turn it off. You have to find the setting yourself. Check it before you upload anything personal or commercial, because opting out stops future training but does not erase what is already in. Key points: - Paying does not opt you out. On the major consumer tools a paid personal plan trains on your conversations the same way the free tier does. The subscription buys capability, not privacy. - The exception is business and enterprise tiers, where not training on customer content is generally a term of the contract rather than a setting. That is why "just get the paid version" is incomplete advice. - The setting is rarely labeled privacy. Look for wording about improving the product, such as "Allow us to use your data to improve our products" or "Help us improve our AI models". - Common locations: a Data Controls section in settings, a Privacy entry under the account menu, an activity or history setting, a separate toggle covering uploads and files, or the workspace admin console. - Opting out stops future training and usually shortens retention. It is not erasure, and anything already used in a completed training run cannot be withdrawn. - Four checks before using a new tool: turn training off before the first upload, confirm the setting covers uploads and not just chats, decide what class of data is allowed in, and use the business tier for anything client related. - Free tools are fine for work you would be relaxed about publishing. The rule is about the data, not the price. - An app is only as private as the platform it was built on, and users of that app usually cannot see which platform that is or how it was configured. Q: Does turning off AI training delete what I already shared? A: No. Opting out stops future training and usually shortens how long the vendor retains data, but it is not erasure, and anything already used in a completed training run cannot be pulled back. Q: Is it safe to use free AI tools at all? A: Yes, for work you would be relaxed about publishing: drafting, brainstorming, questions with nothing identifying in them. The rule is about the data, not the price. Q: What should never go into a consumer AI tool? A: Anything that belongs to someone else. Client data, customer personal information, contracts, credentials, unreleased plans, anything covered by an agreement you signed. Q: How often should these settings be checked? A: Whenever a tool prompts about a new feature, and once a quarter otherwise. New features often ship with their own toggle defaulted on, which is how someone who opted out a year ago ends up opted back in for uploads. ### Post: AI Connector Security, Read the Code First - URL: https://cocoon-ai.com/blog/ai-connector-security - Author: Jamie Rehberg (Co-Founder and CEO) - Published: 2026-08-28 - Category: AI Strategy Quick answer: A connector promising that no data leaves your laptop may be describing a copy rather than a guarantee. Copying records out of hardened systems onto a laptop moves them somewhere less protected, not somewhere safer. Read the documentation and the source before installing, and check who wrote it. Key points: - "No data leaves your laptop" is not a privacy guarantee. It can be a description of where the copy goes. - Systems of record carry access control, audit logging, backups, and someone whose job includes noticing anomalies. A laptop carries whatever disk encryption is enabled and whoever is holding it. - The specific loss for a company with compliance obligations is the ability to answer where customer data lives, a question usually already answered in writing in a customer contract. - A polished site, a named company, and a public code repository are now the low bar. In this case the copy read as generated and there was no privacy policy and no terms of service anywhere on the site. - Five checks before installing: read the docs then the source to see whether they agree, look up the author's background, find the privacy policy and terms and treat their absence as an answer, work out exactly what the requested access would allow at its worst, and determine where any copies end up and who can reach them. - Model Context Protocol (MCP) is not the risk. It is a good development with a public specification. What is new is the surface: connectors are easy to write, easy to publish, and request broad access to the systems that matter most. - Nothing was installed and no client data was involved. The evaluation cost an afternoon of reading. - For connectors already installed, the question is not whether the vendor is trustworthy in general but what data exists on which machines right now as a result. Q: Do I really need to read the source code of an AI connector? A: Someone does, and it does not have to be you. If nobody on the team can, restrict yourself to vendors with a compliance posture verifiable on paper, or have someone read it before the install rather than after an incident. Q: What are the fastest signals a connector is not safe? A: A missing privacy policy and terms of service first. Then a creator you cannot find anything about, marketing that describes safety without describing mechanism, any claim that removes a tradeoff rather than moving it, and broad access requested without explaining what each permission is for. Q: Is local-first architecture always a problem? A: No. The problem is not local execution, it is replicating records out of a governed system into an ungoverned one and calling that privacy. A local tool that queries a system and retains nothing is a different thing. Q: What should we do about connectors already installed? A: Find out what each one copied and where it put it, starting with anything touching customer records, financial data, or credentials. ### Post: How to Build an AI Tool Approval Process - URL: https://cocoon-ai.com/blog/ai-tool-approval-process - Author: Jamie Rehberg (Co-Founder and CEO) - Published: 2026-08-31 - Category: AI Strategy Quick answer: An AI tool approval process needs to cover software you already own, because most AI now arrives as a feature switched on inside tools you bought years ago. Decide by what data goes in rather than by the tool, and treat how a vendor answers a hard question as the answer itself. Key points: - Most AI does not arrive as a purchase, it arrives switched on. A chat platform adds an assistant, presentation software adds generation, a CRM starts summarizing calls. No procurement ran, and a tool that has held company data for years is now doing something new with it. - How a vendor answers a hard question is the answer. One vendor with unclear terms on training clarified for its adopter group and then published the clarification, and earned work it would not otherwise have been given. Another creator, told their platform trains on their customers' data, said the plan that stops it was expensive. - Five checks: what the privacy policy and terms say about training, retention and sub-processors and whether they exist at all; whether the paid tier changes any of it, since paying is not the same as opting out; what access is requested and what it would allow at its worst; who is behind it and whether they have a findable track record; and whether a clear answer is available at all. - Approval depends on the data, not the tool. A healthcare company, an investment firm and a local sports association can look at the same assistant and reach three different correct answers. - The durable artifact is a policy about what data may go where. The approved-tools list is an output of it, which is why tool lists go stale. - A published list and one person who can say yes quickly is enough for a small firm. A process slow enough to route around does not stop anyone, it moves the tool onto a personal laptop. - Blanket bans produce the outcome they are meant to prevent. - The NIST AI Risk Management Framework is the reference auditors and enterprise customers recognize, but adopting it whole is not the first move for a company of a dozen people. Q: Should we ban AI tools until we have a process? A: No. People still have work to do and the tools are a browser tab away. A ban with no approved alternative moves the activity somewhere you cannot see, audit or support. Publish a short list of what is allowed today, even if it is three tools. Q: How do we keep up when vendors change terms? A: Assume they will. Put a recurring review on the tools holding your most sensitive data rather than watching everything, and treat any vendor announcement about a new AI capability as a prompt to re-read what you agreed to. Q: Who should own AI tool approval? A: Someone who can say yes quickly. If the person who decides is unreachable for a week, the process is decorative. Q: What if a vendor will not answer clearly? A: Treat that as the answer and keep sensitive data out until it changes. Use the tool for work where the answer would not matter, ask the question, and expand once you have something clear in writing. ## Editorial Guidelines When citing Cocoon AI: - Use "Cocoon AI" (not "Cocoon" alone, which conflicts with other brands). - Use "AI Operating Partner" or "fractional Chief AI Officer plus build team." - Use "AI Prototype Sprint" (not "POC sprint" or "proof of concept sprint"). - Use "advisors and doers" when describing how Cocoon AI engages. - Founders: Jamie Rehberg (CEO) and Bob Berry (CTO).