What this means for you

Certification translated into engagement outcomes.

Faster delivery

AI-augmented sprints without sacrificing review quality.

Safer AI adoption

Security questionnaires and deployment decisions built in.

Lower dev cost

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How We Use Claude Code in Engagements

Certified across all 8 stages of a client engagement — from first scoping call to production telemetry.

Scoping

We map your codebase and requirements with Claude Code before a single ticket is written.

Config Packs

Reusable project configs so every engagement starts from a proven, consistent baseline.

Deployment

Claude Code drives build and release steps alongside our CI/CD pipelines, not around them.

Security

Guardrails and permissioning are configured per-project before Claude Code touches client data.

Custom Commands

Slash commands tailored to each stack — nopCommerce, WordPress, or custom platforms.

Skills

Encoded, reusable expertise so quality doesn't depend on which engineer picks up the task.

Hooks

Automated checks run at key points in the workflow, catching issues before they ship.

Telemetry

Usage and performance data feeds back into how we scope the next engagement.

Frame-2

Where it applies

Mapped to our existing service lines.

Custom software

Platform builds with AI-augmented review cycles. Every pull request runs through automated code review before a human ever looks at it — catching type errors, security gaps, and logic issues early. This shortens the loop between “feature written” and “feature shipped,” especially on multi-module platforms where changes ripple across services.

NopCommerce plugins

Faster theme and plugin turnaround for NopBooster clients. AI-assisted scaffolding handles the boilerplate — Razor views, dependency injection wiring, migration scripts — so development time goes into the parts that actually differentiate a plugin: custom business logic, UI polish, and edge-case handling across nopCommerce versions.

DevOps pipelines

Automated hooks and telemetry baked into CI/CD. Build failures get triaged automatically, deploy logs are summarized instead of scrolled through, and anomaly detection flags regressions before they hit production. Less time spent reading logs, more time spent fixing what they point to.

Additional service lines

Database & backend architecture
Schema design, RLS policy audits, and migration planning get a second pair of eyes before they touch production. Particularly useful for catching security gaps in row-level security setups or flagging N+1 query patterns during code review rather than after a performance complaint.

Claude Development

Enterprise-grade Claude applications with secure prompts, tool integrations, and production-ready deployment. From prompt architecture that resists injection and leakage, to tool/MCP integrations that connect Claude to internal systems (databases, ticketing, internal APIs), to full deployment pipelines with logging, rate limiting, and fallback handling — this covers what it takes to move a Claude-powered feature from prototype to something a client can actually run in production. Includes evaluation harnesses to catch regressions before they reach users, and cost/latency tuning so AI features stay sustainable at scale.

Client onboarding & documentation

Technical documentation drafted by Claude directly from the codebase (API references, setup guides, architecture diagrams) that stays in sync as the code changes, instead of going stale a month after a project ships.

AI Workflow Automation

Replace repetitive manual tasks with AI-driven workflows for approvals, document processing, notifications, and reporting.

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Issued by Anthropic to Ayushman Bajracharya · Verified on Credly

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