AI workflows.
Production-grade. Code-first.

Repetitive tasks your team does over and over, lead routing, inbox triage, daily briefings, competitor watching, weekly reporting, converted into automations that run on schedule. Written as real code in your repo, not glued together in Zapier.

• Each automation runs on infrastructure you already own (no extra subscriptions to pay for)
• Outputs are structured and validated, so downstream systems never break on unexpected data
• You own the code at the end of the engagement, versioned in git, debuggable, modifiable
• Two weeks of post-launch support included to catch anything the eval suite missed

An MCP server is a way to give Claude or any AI agent direct access to your business data, your CRM, your database, your internal APIs, without copy-pasting context into a chat window. The AI calls your systems the same way a teammate would.

• Your APIs, database queries, and internal tools exposed as actions the AI can take safely
• Every external request signed and verified, so nothing untrusted reaches your data
• A reusable template repository handed off so your team can extend the server without me
• Optional Claude Code skills bundled in, so anyone on your team can install the workflow via one CLI command

A coordinated set of AI agents that handle multi-step work, research, planning, execution, review, with cost limits, error recovery, and visibility into every decision. The thing most teams demo but never get past the demo.

• Specialized agents that hand work off to each other instead of one bloated mega-prompt
• Hard cost ceilings per task and per user so a runaway loop can't blow your API bill
• Production observability so you can see what each agent did, why, and what it cost
• For deeper concurrency work, see Agentic OS

A test suite for your AI features. Real inputs, expected outputs, scored automatically on every code change. The difference between "the demo worked" and "we shipped this to customers and it stayed working." Most teams skip this. It's why AI features regress in production.

• 30-100 real test cases written from your actual product traffic, not made up
• Every code change runs the suite, if quality drops, the deploy is blocked automatically
• Prompts treated like code: versioned, reviewable, rolled back if needed
• You leave with a real test harness you can extend, not a one-off demo