Built by operators.
Installed in your company.
Eclipse installs AI into your company: an audit of how you work, a custom agent for every employee in Slack and a dashboard, and your data brought in-house. Built by engineers from NASA, Vercel, Shopify, and Nielsen.
A 10% productivity lift across a 200‑person company is worth ~$5M/year.
Most companies are leaving that on the table because AI lives with a few power users instead of the whole org. The gap between "we have AI licenses" and "every team works meaningfully faster" is where Eclipse lives.
Three things go live. Then we leave them running.
Eclipse isn't a training. It's a system we build into your company — an audit that tells you where the leverage is, an agent for every employee, and the data plumbing that keeps the opportunities coming.
The Audit
We embed, read your systems, and shadow how the work actually happens. You get a ranked map of what to automate, what to hand to an agent, and what to leave alone.
An agent for every employee
Custom agents tuned to each role, reachable in Slack or your Eclipse dashboard. Your team delegates the busywork and keeps the judgment.
Your data, in-house
Custom tooling pulls your data together under one roof, and an engine keeps surfacing where you can win on efficiency, productivity, and profitability.
Where your team meets their agent.
No new app to learn. Employees talk to their agent in the Slack they already live in — and watch the impact stack up on a dashboard built for the people who sign off on it.
how many refunds did we issue last week, and which SKUs drove them?
47 refunds, up 12% week over week. Three SKUs account for 60% of them — all the same supplier. Want me to open a ticket and draft the supplier note?
yes, and flag it in the Monday report
Done. Ticket OPS-2241 opened, draft in your DMs, and it's pinned to Monday's rollup.
Not just for engineers.
We build a custom agent for every role in the company, tuned to the tools and the work that role already owns. These are starting points — yours are shaped to your org during the engagement.
Drafts outreach, preps call briefs from your CRM, and flags renewals before they slip.
Triages tickets, drafts replies in your voice, and surfaces the bugs worth escalating.
Watches the numbers, opens tickets, and turns a Slack question into a Monday rollup.
Reconciles invoices, chases anomalies, and assembles the report before you ask.
Answers the policy questions, drafts the docs, and handles the onboarding checklist.
Pulls the cross-team picture into one readout — what shipped, what moved, what's next.
Three ways to install it. All built from the same protocol.
Every engagement runs the same playbook — audit, deploy, practice, scale. The tier sets the depth, the team size, and how much we customize. Not sure which fits? That's what the call is for.
Eclipse Spark.
A focused pilot for small teams ready to move fast. Onsite kickoff, full tooling setup, custom agents tuned to your codebase, and a 30-day forward plan. The fastest way to see what's possible.
Eclipse Sprint.
Real adoption across a mid-sized engineering org. Audit, deploy, role-specific practice, and measurement — compressed into two weeks of focused work. Includes a mid-engagement demo day where your team shows each other what they've built.
Eclipse Protocol.
The full installation. Org-wide fluency, custom agents built across departments, internal champions trained to run the protocol after we leave, business outcomes measured and reported to leadership. For companies committed to becoming AI-native.
Four phases. Compressed or
expanded to fit the tier.
Audit
We embed with your team, read your codebase, and shadow your workflows. You get a current-state map, capability gap analysis per role, and a ranked-opportunity backlog.
Deploy
The right stack, configured for your stack. Claude Code, Cursor, Cedar, MCP integrations, custom skills built against your conventions, SSO and security handled. By end of this phase, every engineer is shipping production work with AI in the loop.
Practice
Role-specific intensives, hands-on builds inside your real codebase, daily office hours, and an internal #ai-wins channel we seed and run. This is where excitement compounds and adoption locks in.
Scale
Custom agents embedded into the workflows that matter, measurement harness wired to real business outcomes, internal champions trained to extend the protocol after we leave. You get a forward roadmap and an exec readout.
And a library of Claude Code agents your engineers keep for good.
Beyond their own role agent, your engineers get our full library of coding specialists — one-command install into any repo. Pick a specialist, drop it in, get to work. We build custom agents against your codebase during the engagement.
Maps the score before the gig. Reads a codebase end-to-end, then proposes the smallest change that gets you the biggest win.
Treats your codebase like a safe. Threat-models, finds the soft spots, and writes the patches before the postmortem.
Sees what your logs are hiding. Stitches traces, metrics, and incidents into one narrative — then tells you the next move.
We're the team that ships, teaching the team that ships.
Operators, not consultants.
The people running your engagement have shipped at scale. We've architected commerce platforms, built AI products in production, and launched apps used by millions. We teach AI fluency because we use it every day to build real things.
Excitement over mandate.
AI isn't like a migration — we engineer the moments that make engineers want this. Day 1 wins, custom agents that solve yourteam's specific complaints, a tiny win-channel that runs itself. Surprising adoption is wins. Top-down adoption is the average of three.
The people who run the protocol.
Martijn Lancee is CEO and Co-Founder of Eclipse AI.
He has spent the past several years working at the intersection of AI implementation, enterprise adoption, go-to-market, and agentic workflows.
At Microsoft AI, Martijn worked with companies on AI implementation, Copilot adoption, and enabling teams to build AI agents inside the enterprise. He has also advised an AI services firm for the past two years, helping shape real customer AI implementations from strategy through execution.
Martijn has led more than 10 enterprise AI enablement sessions on how to implement and use Claude, including sessions for groups of up to 50 people.
His focus is simple: help companies move from AI curiosity to practical systems that improve how teams work, sell, support customers, and operate.
Evan has 10+ years of experience working at organizations like NASA, Google and Vercel.
Evan started his career as a Systems Engineer at NASA’s supercomputing facility before moving on to Google Cloud where he spent 8 years launching business units and helping Fortune 500 customers leverage AI and cyber security products. At Vercel, Evan partnered with strategic customers to help them build AI agents, applications and infrastructure.
Sam Davidoff is a self-taught engineer with more than 15 years building for the web, and a deep command of JavaScript, commerce, branding, and SaaS.
He's shipped storefronts for some of the largest brands on Shopify, leading tier-one builds from architecture to launch at platinum agencies like Domain and Lazer. Between commerce projects, he designs and builds custom SaaS and Jamstack applications end to end.
Thirty minutes.
No pitch deck.
Tell us about your org, your current AI usage, and what you'd want shipped in your engagement. We'll recommend a tier, scope the work, and send a proposal within a week. If we're not the right fit, we'll tell you who is.