Anthropic consulting for the enterprise stack.

Anthropic consulting is the engineering practice of getting Claude-backed systems from a procurement decision into production at enterprise scale - Bedrock vs Vertex vs direct API, MCP server design, Agent SDK orchestration, Computer Use evals, prompt caching economics, and the governance layer the security team needs before any of it goes live. We are operator-led in two specific ways: Sentinel — our live Shopify analytics product — runs its LLM layer on Claude in production, and PAI — our internal Claude-based operator system — runs 28 hooks, 16 agents, and 27 hook entries across 11 events, with 1,315 signals, 1,275 ratings, and 1,020 work sessions tracked. The methodology that built both is the methodology we bring to your stack.

Default to Bedrock if your security team has already approved AWS for inference and you want a single billing/IAM surface; default to Vertex if you are GCP-native and care about Vertex-specific governance; default to the direct Anthropic API when you need the fastest access to new model versions and Anthropic-specific features (prompt caching at full granularity, Computer Use in beta windows, Agent SDK telemetry). We have shipped on all three. The right answer is a written matrix scored against latency, region, governance, and feature recency - we produce that matrix in week one of any engagement.

MCP - Model Context Protocol - is Anthropic's open standard for exposing tools, resources, and prompts to a Claude client without rebuilding the integration each time. Build an MCP server when the same tool surface (a database, a vendor API, an internal search index) needs to be reachable from multiple clients (Claude Desktop, an Agent SDK loop, a notebook) and you do not want to maintain three forks of the integration. Skip it when you have one client and one tool - a function call is shorter.

The Agent SDK is the production-grade loop layer - it handles tool-use turns, retries, parallel tool calls, structured outputs, and the bookkeeping you would otherwise rebuild. We use it where the alternative is a hand-rolled while-loop with five edge-case branches we know from experience will rot. The honest tradeoff for orchestrator-workers (lead agent + 3–5 parallel subagents): Anthropic's own published numbers show a 90.2% performance lift over single-agent Opus 4, at roughly 15× the token cost. We use it where the lift is worth the bill, and tell you when it isn't. We do not use it for simple one-shot completions; the SDK earns its place when the agent has to take three or more tool turns to finish.

Computer Use is production-viable for narrow, well-instrumented automations - internal back-office workflows where the agent operates on a sandboxed VM, every action is logged, and a human can replay the trace. It is not yet appropriate for high-stakes external-facing automation without a human-in-the-loop gate. We help teams build the eval harness for Computer Use first - synthetic task suites, success-rate benchmarks, refusal-rate alerts - before any real workflow ships.

Prompt caching lets you mark the static portion of your prompt - system instructions, tool definitions, retrieved context that does not change per request - so subsequent calls reuse the cached prefix at a fraction of the input-token cost. In our own production traffic the savings range from 40% to 85% of input cost depending on prompt structure, with the largest savings on long-system-prompt agents. The unsexy answer: it pays for itself in the first week of a high-volume workload, and it is the first thing we instrument.

An Anthropic strategy sprint is a two-week window, no extensions. A production PoC is a 14-day sprint from kickoff to a feature serving real traffic behind a feature flag. A full implementation is six to twelve weeks depending on your deployment surface governance and how many MCP servers you need. We refuse engagements that don't fit a two-week window at the unit level.

Anthropic tends to win on long-context reasoning, agent-style tool use, structured output reliability, Computer Use, and Constitutional-AI-aligned behavior in regulated environments. OpenAI tends to win on multimodal breadth, ecosystem maturity for fine-tuning, and the lowest-latency real-time speech features. Open-source wins when on-prem residency, cost-per-call at extreme volumes, or full model customization dominate. We write the eval first, score all three, and pick whichever the eval picks. If your team is already standardized on GPT, our /services/openai-consulting/ page is the better starting point.