AI SERVICES
Custom MCP servers, AI agents, copilots, RAG pipelines, and editorial workflows — shipped by the team behind the open-source Sitecore MCP and Figma MCP servers. We ship production AI, not strategy decks.
What we build
From a single copilot grounded in your docs to a multi-agent pipeline that runs your editorial team, the shape varies. These engagements cover where most teams ask us to start.
01 / 05
We've shipped two flagship open-source MCP servers — Sitecore MCP (100+ tools, 3× faster Sitecore development) and Figma MCP (Product of the Day on Product Hunt). We bring that depth to your internal systems: ERP, CRM, ticketing, search, billing, custom platforms. Every tool typed, audited, packaged with a test harness — not vibe-coded.
Talk to us about a custom mcp servers02 / 05
From a Claude-powered support copilot grounded in your knowledge base to a multi-agent pipeline that drafts, reviews, and ships work without a human in every loop. We design the agent topology, wire tool use, memory, and guardrails, then ship into your stack — Claude Agent SDK, OpenAI Assistants, LangChain, or hand-rolled orchestration when those are the wrong fit.
Talk to us about a custom ai agents03 / 05
We moved our own engineers from clicking around Content Editor to staging PRs before standup ends. Same playbook for your team: senior engineers walk yours through the tools, set up the agent flows, and leave you with a working setup — Claude Code, Cursor, or Codex wired into the MCPs that fit your stack, tuned to your repo and review process.
Talk to us about a ai workflows for dev teams04 / 05
We've shipped Jira → Sitecore pipelines where n8n picks up new tickets every minute, an AI Editor drafts the content, an AI QA reviews it, and a human verifies the result. Same shape for translation, campaign rollouts, brand-guideline enforcement, and bulk editorial work. Built around your existing tools — Jira, n8n, Slack, your CMS — not a black-box SaaS.
Talk to us about a ai workflows for business teams05 / 05
Strategy decks die in drawers. We pair a two-week assessment with a working prototype, so leadership sees real outputs on real data on day fourteen. Then we plan rollout, eval, observability, and — when it makes sense — fine-tuning or small open-weight models for cost and privacy reasons.
Talk to us about a ai strategy & governanceSITECORE MCP
Wire Claude Code, OpenAI Codex, Cursor, and the rest of your AI engineering stack directly into your Sitecore instance — built and battle-tested by the team running the migrations.
FIGMA MCP
An open-source MCP server that lets Claude, Cursor, and any MCP-aware client read AND write Figma design documents — not just read them. Built before Figma shipped the Claude Code plugin, Anthropic released Claude Design, and Google made Stitch.
Open-source MCP server with WebSocket plugin relay. Apache 2.0.
Workflows in action
We built the MCPs. Then we wired them into real delivery.
Figma → CMS in minutes: paste a design link, the agent reads it with Figma MCP, scaffolds the component, content schema, and entries via your CMS's MCP, then verifies the result in Chrome with DevTools MCP. Commit staged, PR open, before standup ends.
Jira → CMS on autopilot: n8n picks up new tickets every minute. An AI Editor drafts the work, an AI QA reviews it, a human verifies and approves. Async — runs while your editors sleep.
These aren't slideware. They're how we ship our own work, and the same shape we wire up for clients on whichever stack you're on.
Capabilities
Beyond the headline engagements, here's what we routinely ship inside them. Every one of these is wired into client CMSes, ops systems, or internal tools — not lab demos.
Drop AI into the product you already have — natural-language search, smart drafts, structured-output features, function calling. Behind your auth, on your stack, with your latency budget.
Production-grade chatbots and voice assistants — grounded in your knowledge base, handed off to humans cleanly, observable end-to-end. Not a prompt-and-pray demo.
Internal copilots tuned to one job — sales-deck assembly, support-ticket triage, contract review, ops runbooks. Live in Slack, your CRM, or your admin UI.
Retrieval-augmented generation over your docs, wikis, CRM, codebase, or ticket archives — with the chunking, eval, and access controls that make it actually trustworthy in production.
Multi-step automation that reads a ticket, queries the systems it needs, drafts the work, and routes for review — with checkpoints, retries, and a human off-ramp at every step.
AI inside the admin, not next to it — bulk translation, content generation, image edits, and editorial QA wired straight into your CMS or back-office, with your workflow rules intact.
Outcomes
Faster CMS development
Sustained pace, measured across the team, since wiring CMS MCPs into the workflow.
Faster Figma → CMS
Components, content schemas, and seed entries scaffolded from a design link in one prompt — no manual wiring.
CMS platforms
Sitecore, WordPress, Contentful, Sanity, Storyblok, Strapi, Drupal, AEM, Optimizely — we wire AI into the CMS you already have.
"Claude Code opened the design, drafted the plan, scaffolded the rendering and its datasource template with every field wired, then spun up test pages in two languages across a handful of variants and walked through them in Chrome. The commit was staged and the PR was open before standup ended."
Bogdan D.
Senior Developer, EXDST
The stack
The runtimes, MCPs, agent frameworks, and observability tools we work in — the same set we use on our own delivery, picked per project, not by sponsorship.
Frequently asked
No — and any vendor saying otherwise is selling, not engineering. AI removes the tedious parts: scaffolding, boilerplate, repetitive wiring. Your senior engineers move from typists to reviewers and architects. We use the same workflows on our own delivery and the team ships more, not less.
Yes. Most retainers start with a two-week enablement sprint: tools installed, MCPs wired, agent flows configured, and your engineers paired through real tasks on the real codebase. After that, your team runs the workflows; we stay on call.
All of the above. MCP servers are how we got known (Sitecore MCP, Figma MCP — both production, both open-source), but the bulk of our AI engagements are agents, copilots over private knowledge bases, RAG, and editorial automation in client CMSes. MCP is a tool, not the offering.
Every engagement starts with a data audit — what leaves your network, what doesn't, what gets logged. We deploy on-prem (Docker, your VPC) when the data is sensitive, route through approved enterprise gateways (Bedrock, Azure OpenAI, Vertex) when that's the policy, and ship with audit logs and access controls baked in. Open-weight small models for the hardest cases.
Two layers. Build-time evals — accuracy, safety, brand-voice, hallucination — using custom evaluators and golden datasets your domain experts review. Run-time observability — cost per request, latency, refusal rate, escalation rate, and time saved vs the manual baseline. ROI is reported in hours saved per workflow, not 'AI maturity scores'.
Start a conversation
A senior engineer will be on the reply — not a sales rep. We respond within one business day with concrete next steps, not a brochure.