Why a Claude-specialist consultant
Most AI consultants are LLM generalists. They learned the word "Claude" last month. Claude has specific strengths: long context (up to 1M tokens), native tool use, Claude Code for engineering teams, the Agent SDK for building robust agents, MCP for connecting your internal tools.
A generalist won't know when to reach for Claude over GPT, how to set up prompt caching to cut your API bill by 3x, or when the Agent SDK is overkill versus a simple prompt workflow.
It's not a tech problem. It's a method problem.
For your business
Claude API integration into your stack
Internal chatbots wired to your documentation (RAG), connections to CRM / Notion / Slack, automation of repetitive tasks via API + n8n. I pick Claude when it fits, another model when it fits better.
Claude audit and roadmap
I map your processes and data, identify high-ROI use cases, estimate budget and sequencing. Clear deliverable: what to do, in what order, for how much. If Claude isn't the answer, I tell you before billing anything.
Compliance and data residency
Where your data runs: Claude for Work versus direct API, opt-out of training, regional Anthropic workspaces for data residency. We define confidentiality rules before writing any code.
For your engineering team
Agent SDK & agent design
When the Agent SDK is justified versus a simple prompt workflow. Architecture, memory, sub-agents, skills, custom tools. I design agents that hold up in production — not demos that break on the first edge case.
Claude Code for your devs
Team rollout: hooks, custom slash commands, MCP servers, PR review and test generation workflows. Hands-on training so your devs are productive in week one.
MCP servers & tool use
Design and ship custom MCP servers to connect Claude to your internal stack (business DB, private docs, internal tools). Auth, permissions, observability. Open protocol, no vendor lock-in.
Claude shines on long context (reading a large repo, analyzing a 200-page contract) and long agentic tasks (Claude Code holds up on 4-8 hour sessions where GPT drifts). Anthropic's prompt caching cuts repeated-prompt costs by 60-90%.
GPT is still stronger on vision, often cheaper on small prompts, and has a broader third-party ecosystem. It's not "Claude always" — it's "know when Claude."
Stack
Live stack & proof
I live in the Claude ecosystem daily. A few public projects:
- TodoAI — Task management app where Claude decomposes and executes tasks autonomously. Uses Agent SDK + MCP.
- Eaight — AI-native browser with a built-in MCP server. Claude Code can see and control the browser.
- llm-cost-profiler — Python profiler to track Anthropic and OpenAI API costs. Directly relevant for teams shipping Claude to production.
How it works
Frequently asked questions
Claude excels on long context and long agentic tasks (Claude Code running for hours, agents reasoning over large corpora). Prompt caching dramatically cuts costs for repeated prompts. GPT is still often better on vision and cheaper on small prompts. The right choice depends on the use case — we figure that out before writing code.
Any. SMBs, mid-market, startups. The question isn't size but use-case fit. A 15-person SMB with a repetitive customer-support process can extract more value from Claude than a large enterprise that wants to "do AI" to tick a box.
Daily rate between $625 and $1125 depending on scope. Typical POC: $6-19k over 2 to 4 weeks. Scoping audit: a few days. The 30-minute intro call is free, used to scope the work and estimate whether the project is worth the investment. I bill daily or by project, whichever fits. If I don't think the ROI will be there, I say so before starting.
By default, Claude API calls go through Anthropic, but your data isn't used to train models (the "no training" option can be enabled). You can also run Claude via AWS Bedrock or Google Vertex to keep data in your own cloud. We define the rules before writing any code.
Yes. Via the API, via MCP servers (Anthropic's open protocol), or via n8n. Almost any modern tool with an API can be connected to Claude. For custom internal tools, we write a dedicated MCP server.
Claude Code is Anthropic's CLI tool for developers: it reads your code, edits files, runs commands, all with Claude as the engine. Useful if your engineering team spends time on repetitive code tasks (tests, refactors, PR reviews). Not useful for non-technical teams.
A simple prompt is enough for 80% of cases (text transformation, classification, extraction). The Agent SDK is justified when you need: long memory, multi-step with feedback loops, chained tool calls, specialized sub-agents. If unsure, start simple and only add complexity when the simple version fails.
2-3 weeks for a POC on a well-defined case. No six-month development tunnel before seeing a result. If after 3 weeks it's not convincing, we stop — you'll have spent a small envelope, not a year.