Claude Enablement: Claude, embedded inside your team.

Claude Enablement is helping a team turn Anthropic's Claude into a real production capability, not a demo and not a chatbot wrapper. At Sarvaswa we deliver four pieces in every engagement: custom Claude Skills tailored to the team's domain, MCP (Model Context Protocol) servers connecting Claude to the team's systems, the context engineering work that keeps the model reliable (retrieval, prompt registry, evals), and the operational runbook the team uses to ship and operate Claude on its own.

A Claude Skill is Anthropic's packaging format for a reusable Claude capability, instructions, examples, tools and metadata bundled together so Claude can perform a specific task consistently. Compared to hand-tuned prompts, Skills are versioned, evaluable, and reusable across products. We design Skills as production code: domain-aware, gated by evals, and rolled out the same way you would ship a service.

MCP, the Model Context Protocol, is the open standard Anthropic created so Claude (and other models) can talk to your tools, databases, and APIs through a uniform interface. It matters because it lets Claude act on your systems instead of just answering, with the integration layer under your own authentication and access controls. We build MCP servers scoped to least privilege, so Claude reaches only what each workflow needs.

It depends on the workload. We default to Claude Sonnet for most production agents; fast, cheap enough at scale, smart enough for tool use. Opus comes in for genuinely hard reasoning tasks where one strong answer is worth several Sonnet calls. Haiku is for high-volume, low-latency workflows where most calls are pattern-matching. Model selection is a first-class deliverable in every engagement, not an afterthought.

Most engagements move from kickoff to a production-grade Claude integration in 6 to 12 weeks, faster than our 8 to 14 week baseline for full custom builds because the model layer is already proven. The first one to two weeks are discovery and design, Skill architecture, MCP map, model selection, eval plan. The rest is build, test, and operate. We always leave behind a runbook so the team can extend the work without us.

The Skills, the MCP servers, the evals and the deployment runbook, documented from day one. Sarvaswa enables, then steps back. The capability should not depend on us continuing to be in the room.

Yes. We deploy via the direct Anthropic Claude API, AWS Bedrock under your account, or both; whichever fits your compliance and procurement story. The Skills, MCP servers, and eval harness are deployment-agnostic, so you can switch later without re-architecting.