Sarvaswa AI Labs
Claude Enablement

Claude, embedded inside your team.

We help SMEs, startups, and engineering teams turn Claude into a real production capability; through custom Skills, MCP servers running inside your perimeter, context engineering, and the operational scaffolding that keeps it reliable.

Scope a Claude buildSee use cases
What we ship

Skills. MCP servers. Context that actually works.

01

Custom Claude Skills

We design, author and ship Claude Skills tailored to your domain, packaged, evaluated, and owned by your team. From document workflows to internal copilots, we treat Skills as production code, not prompt experiments.

  • Skill design and authoring
  • Domain evals before shipping
  • Versioning, rollback, and prompt registry
02

MCP servers inside your perimeter

Model Context Protocol servers built around your internal tools, databases, and APIs, running where your data already lives. Claude reaches into the systems your team uses, without sensitive data ever leaving your boundary.

  • MCP server architecture and tool schemas
  • Auth, rate limits, and audit logging
  • Observability and incident playbooks
03

Context engineering

Retrieval pipelines, prompt registries, and the structural decisions that decide whether Claude is reliably useful or merely impressive in a demo. We treat context as the work, not an afterthought.

  • Retrieval pipelines and grounding
  • Long-context document workflows
  • Prompt registry, versioning, and reuse
04

Production scaffolding

Evals, red-team checklists, observability, and the operational runbook your team needs to keep Claude trustworthy at scale. Built so Claude becomes a capability, not a constant fire.

  • Eval harness gating every release
  • Red-team checklist and safety review
  • Cost, latency, and accuracy observability
How we enable Claude

From audit to runbook, in four phases.

01

Discover

We audit the workflows where Claude can move the needle, and the ones where it cannot. Honest scoping before any code.

02

Design

Model selection across Claude Sonnet, Opus and Haiku. Skill architecture, MCP map, retrieval strategy, and a deployment runbook your team owns end-to-end.

03

Build

Skills, MCP servers, retrieval pipelines, and the eval harness that keeps releases trustworthy. Engineers and designers shipping production-grade work.

04

Operate

Observability, continuous evals, prompt registry maintenance, and structured handoffs so the team can run it without us.

Who it's for

Built for teams that want to ship with Claude.

For startups

Claude-native from day one

A technical AI co-founder who designs your Skills, MCP servers, and context strategy alongside your team. You ship faster, and you own the result, not us.

  • Skill + MCP architecture
  • Model selection runbook
  • Eval harness from week one
  • Full ownership at handoff
For SMEs

Replace manual ops with Claude that fits your stack

We build Claude into the systems you already use. Internal copilots, document workflows, and tier-1 automation, embedded, evaluated, and observable.

  • Internal Claude copilots
  • MCP servers for your tools
  • Domain Skills with evals
  • Continuous observability
For enterprises

Production-grade Claude under your perimeter

Claude integrated under your compliance boundary. We design the architecture, train your engineers on the patterns, and leave behind eval suites and runbooks that survive without us.

  • Long-context document workloads
  • MCP servers in your VPC
  • Red-team checklists and safety review
  • Compliance-aware observability
How we think about Claude

Six principles that hold up in production.

01

Skills first, prompts second

Skills package intent into reusable, testable units. Hand-tuned prompts decay; well-designed Skills compound.

02

Context engineering > prompt engineering

Most "the model isn't good enough" problems are context problems. Get retrieval, the registry, and the schemas right and the model takes care of itself.

03

MCP belongs inside your perimeter

Claude reaches into your tools through MCP without your data needing to leave. We build the servers where they should live; in your cloud, behind your auth.

04

Evals are the release process

No model, prompt, or Skill ships without an eval. The eval harness is a first-class part of every engagement, not a polish-pass at the end.

05

Long-context is a workflow, not a window

Million-token windows are useful; well-shaped long-horizon workflows are useful. We design for the second.

06

Built to own

The Skills, MCP servers, evals and runbooks we ship belong to your team. Sarvaswa enables, then steps back, your moat, not ours.

Questions worth answering

Claude Enablement, FAQ.

The questions teams ask before they start working with us on Claude; answered straight, no marketing fluff.

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 running inside the team's perimeter, 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, while keeping the integration layer inside your infrastructure. We build MCP servers that live in your cloud, behind your auth, so sensitive data never leaves your perimeter.
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. 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.
Yes, fully. Source code, evals, Skills, MCP servers, and the deployment runbook are yours from day one. Sarvaswa enables, then steps back. Your competitive moat 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.

Ready to make Claude part of how your team works?

From the first Skill to a fully-evaluated production runbook, we build alongside your team, and hand it back, owned and operable.

Scope a Claude build