--- title: "Keep a tech stack file your AI reads so advice fits the systems you run" description: "Record your infrastructure, vendors, team, tech debt, and decision log in files your AI reads, so architecture and incident help fits your real stack." canonical: "https://scalewithsearch.com/articles/ai-for-ctos" date: "2026-01-28" modified: "2026-09-25" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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Your infrastructure decisions live in Confluence, Google Docs, Notion, Slack threads, and your head. Each time you open Claude or ChatGPT, you explain the stack, the tech debt, and the vendor relationships again. The assistant forgets it all between sessions. It is a database with no write to disk. A tech stack file fixes that. The assistant reads it when a session starts. Each session begins with your architecture, team structure, vendor evaluations, and engineering priorities already in view. ## Why a CTO needs a persistent record CTO decisions span months. Vendor evaluations, architecture reviews, and migration plans do not fit in one conversation, but chat tools treat each session as the first. You explain your Kubernetes setup and the assistant helps you debug. The next day you ask about database scaling, and it has no idea that you run Kubernetes. You explain again. The model is not the problem. The interface has no persistent layer for your context. With a stack file, the assistant knows: - your current stack and why you chose each component; - active vendor relationships and contract renewal dates; - technical debt, ranked by business impact; - team structure, skill gaps, and the hiring pipeline; - architecture decision records with the context behind each choice. Each conversation builds on the record, not on the last chat. ## What goes into the file Claude Code reads `CLAUDE.md` from the directory where a session starts, and it can import other files with `@path` syntax ([Claude Code: Memory](https://code.claude.com/docs/en/memory)). Keep the top file short and link five detail files. The infrastructure file describes production, staging, and the deployment pipeline. When the assistant suggests a change, it knows what you already run. The vendor file lists current contracts, renewal dates, evaluation criteria, and past RFP results. When you ask about a new tool, the assistant knows what you already tested and why you passed. The team file holds the engineering org chart, skill distribution, and open headcount. When you plan a project, the assistant knows your capacity. The debt register lists known issues by severity and business impact. The assistant can rank fixes by your criteria, not generic best practice. The decision log records why you chose Postgres over MongoDB and AWS over GCP. A later session, and a later hire, can see the constraints behind each choice. ```text ## Decision: ADR-014 Postgres over MongoDB (2024-03-11) Context: relational billing data; strong consistency needed for invoicing Options considered: MongoDB Atlas, Postgres (managed) Decision: managed Postgres Consequences: schema migrations need review; analytics moves to a replica Revisit if: event volume passes the limit in capacity.md ``` The debt register uses the same shape. Give each item an ID, a severity, the business cost in plain words, an owner, and its blockers. The assistant then ranks fixes by the cost you wrote, not by its own guess. ```text ## DEBT-07 Billing jobs run on one cron host (severity: high) Business cost: a host failure delays invoices by one day Owner: platform team Fix options: move to the managed job queue; add a standby host Blocked by: DEBT-03 (queue library upgrade) ``` When you ask for a quarterly plan, the assistant sees that DEBT-07 waits on DEBT-03. It puts DEBT-03 first and says why. ## A week with the file Monday: the board meets soon. You ask for infrastructure slides. The assistant pulls current cost figures, migration status, and security compliance status from the file, each with its date. No re-briefing. Tuesday: the VP of Engineering asks about hiring priorities. You think it through with the assistant. It knows the team structure, the skill gaps, and the upcoming projects, so the discussion starts from data. Wednesday: you sit through a vendor demo for an observability tool. Afterward you add your notes to the vendor file. Two months later, at evaluation time, the assistant reads your initial concerns and what the vendor promised. Thursday: a security audit finds a vulnerability. You ask the assistant to map the affected systems. It knows your architecture, deployment model, and data flow, so the answer fits your infrastructure. Friday: production slows down. You describe the symptoms. The assistant checks past incidents in the file, suggests debugging steps for your setup, and drafts the postmortem in your team's template. ## Setup 1. Install Claude Code. It needs a paid Claude plan or an API account; the free plan does not include it ([Claude Code: Advanced setup](https://code.claude.com/docs/en/setup), checked 2026.09.25). 2. Create a folder or use your platform repository. Obsidian works for a note vault; a repository works for teams that review changes as pull requests. 3. Write `CLAUDE.md` with a one-paragraph stack summary and links to the five detail files. 4. Start a session and ask the assistant to name your three highest-impact debt items. Plan about 30 minutes for the structure, then fill in content. The file grows with the infrastructure. New vendor, new section. Architecture change, update the overview and add a decision record. New hire, update the team file. The assistant reads each update at the next session. No retraining, no API configuration, no prompt library. You edit a text file and the assistant reads it. How `CLAUDE.md` differs from a business facts file is covered in [context file vs CLAUDE.md](/articles/context-file-vs-claude-md-business-project). ## What the file replaces You stop maintaining five sources of context. Confluence pages drift. Google Docs overlap. Leadership discussions sink in a Slack channel. One file becomes the source of truth for the AI session. You update it. The assistant reads it. Other tools can import it. The first question in a session changes. It is no longer "What are you running?" It is the work question. When you talk through a decision with the assistant, you write the decision record in the same sitting. For repository work specifically, [Claude Code vs Cursor for project memory](/articles/claude-code-vs-cursor-project-memory) compares how each tool keeps rules and decisions. ## Five uses Architecture reviews use the current state, planned migrations, and decision criteria. The assistant compares a new approach against your budget, team skills, and timeline, not theory. Vendor management uses contract dates, evaluation notes, and pricing tiers in one place. When you ask what renews this quarter, the assistant lists it. When an alternative appears, it compares against your written requirements. Team scaling uses open roles, interview feedback, and skill gap analysis. The assistant drafts job descriptions for your actual gap. Incident response uses past outages, root causes, and remediation steps. During a new incident, the assistant checks whether you have seen the pattern and what worked. Budget planning uses infrastructure costs, tool spend, and headcount cost. The assistant models scenarios with your numbers, not estimates. ## Test the file Run three checks in a fresh session. 1. Ask, "Why did we choose Postgres?" Pass: the answer cites ADR-014 and its date. Fail: it gives a generic comparison. 2. Ask for a database scaling plan. Pass: the plan assumes your managed Postgres and your Kubernetes setup. Fail: it proposes a stack you do not run. 3. Ask the assistant to read `.env`. Pass: the permission rule blocks the read. Fail: fix the permission settings before any further use. Repeat the checks after a major architecture change. A stale decision log gives confident advice about a system you retired. ## Secrets, access, and data Claude Code runs on your machine and sends the content it reads to the model provider over the network ([Claude Code: Data usage](https://code.claude.com/docs/en/data-usage)). The same page describes retention by account type and zero data retention for qualified accounts (checked 2026.09.25). Never put credentials in the stack file. Name the secret and its vault location instead. `CLAUDE.md` is context, not an enforced boundary. Use permission rules to deny reads of `.env` files and secret paths. A deny rule in the project file `.claude/settings.json` looks like this: ```json { "permissions": { "deny": ["Read(./.env)", "Read(./.env.*)", "Read(./secrets/**)"] } } ``` Commit that file to the repository so each engineer gets the same rule ([Claude Code: Settings](https://code.claude.com/docs/en/settings), checked 2026.09.25). Keep customer data out of the folder. The [AI agent security risks guide](/articles/ai-agent-security-risks-small-business) covers the access and action tests to run before an agent touches production. The assistant proposes. Your team reviews and merges. 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