// AI ENGINEERING FOR FOUNDERS & PRODUCT TEAMS

Get your AI feature
into production.
And keep it there.

We build and operate AI features inside your existing systems. Bring us a scoped project or an ongoing roadmap.

Discuss your project → See production work →

$300M+: traded through Pear, a platform we helped build
600+: bookings through KaribuKit, our own hotel software
470: points on Hacker News for framedex, our open-source video indexer
14 yrs: shipping production software

// WHAT WE BUILD

The demo took a weekend. Production is where it stalls.

Real data, permissions, edge cases, cost. That's the work we take on. Three live examples: one for a client, two we run ourselves.

Customer + staff agents · Mara Hilltop · our safari lodge

An AI agent that answers safari guests around the clock.

Ranger handles inquiries on the lodge website, quotes from live availability in the guest's language, and hands staff a ready-to-confirm booking. Staff run reservations, check-ins and pricing by chatting with the same agent in the app or on Telegram. On 31 August it booked a $300 game drive for a guest with no one on staff involved.

We built KaribuKit, the property system underneath. 600+ bookings have run through it since March, and we live with every edge case.

AI crawlers saw 2 words of this site in May. Now 4,614. Get the same audit →

Visit ↗ KaribuKit PMS · custom MCP server · multi-model

Message Ranger…

Decision system with guardrails · Pear Protocol · client

A trading agent that sets up the position, then waits for the trader to confirm.

Agent Pear answers pair and market questions from live on-chain stats and sentiment, then stages long/short positions that execute only after the trader confirms. Live for Pear's retail traders on Telegram and web.

Two years as Pear's engineering partner. $300M+ has traded through the platform we helped build, and the on-chain vaults we built end to end filled their first $1M cap about ten hours after launch.

Visit ↗ Multi-model DAG · on-chain + sentiment pipeline · confirm-before-execute

Agent Pear · Signal Trade staged · waits for the trader to confirm

Agent-native product · SlopIt · ours

A blog platform where the AI agent is the user.

An agent publishes a post with one API call and gets a live URL back. Every post on our blog ships through it.

Visit ↗

MCP · slopit.publish

slopit.publish({
  title: "What shipping an agent-readable ToS taught us",
  body:  "...",
  tags:  ["agents", "slopit"]
})

→ 200 OK

Live at blog.simbastack.com

// HOW IT WORKS

Forward-deployed engineers.
In your repo, your systems, your standup.

A real conversation, free

45 minutes with NJ on what you're building and where it's stuck. If AI isn't the right fix yet, he'll say so.

Scope it, then ship it

We agree on one workflow and what done means, then build it in your repo for a fixed price.

We keep going, or we don't

If it works, the next piece on your roadmap. If it doesn't, you're not locked into anything.

Discuss your project →

// PRICING

Clear pricing. No discovery call to find out what it costs.

For scale: one senior AI engineer in-house costs $300K+ a year loaded, once you find one.

Start here

AI Build Sprint

$12,500

Two weeks to a working AI feature in your environment. One workflow, agreed acceptance criteria, in your repo. 30 days of support after launch.

Bigger builds, like several systems or complex permissions, get their own quote on the first call.

If we can't see a clear first build on that call, we'll say so and won't take the sprint.

Start a sprint →

Embedded AI Team

$10,000/mo

Two engineers embedded with your team, NJ on technical direction. We own what we've shipped, watch it in production, and work your backlog every month. Two-month minimum, cancel anytime after.

Already have a roadmap? Skip the sprint and start here.

Talk to us →

Not ready for a build?

AI Visibility Audit $1,500

Can ChatGPT, Claude and coding agents find and use your product? Five business days.

See the audit

// THE TEAM

No account managers. No layers. You work with the people who build.

Founder & Chief Architect

NJ has spent 14 years building software for other people's businesses, including Pear's trading platform, and now AI agents. He also runs a safari lodge in Kenya on software our team built. And he picks up the phone.

Upwork Top 1% Talent ↗ LinkedIn ↗ Twitter / X ↗

Direct line California · +1 (717) 683-9393 · call or WhatsApp

Senior Engineer & Delivery Lead

Deep

Leads client delivery end-to-end. The person who makes sure your system works in production, not just in a demo.

Senior Engineer & Systems Architect

Pranjal

ex-Swiggy. Our go-to for the hardest problems: Web3 infra, AI systems, anything where “figure it out” is the spec.

Full-Stack Engineer (Design Lead)

Chetan

Full-stack with design instincts. Most of our UI passes through him before it ships, and more and more of the AI integrations underneath it.

Full-Stack Engineer (Backend / Performance)

Hemanshu

Backend-first full-stack. Built a 250 req/sec layer behind one of our hospitality systems. Owns caching, API design, the parts that have to scale under load.

// VIDEO & WRITING

We show the work.

Short videos of AI doing real jobs, start to finish. The long version goes on the blog.

I think I've solved procrastination

How AI edits my YouTube videos now, from raw footage to the finished cut.

All videos on YouTube

Indexing a year of video locally on a 2021 MacBook. Front page of Hacker News, 470 points.

Hacker News front page: Indexing a year of video locally on a 2021 MacBook with Gemma4-31B (50GB swap), 470 points, 142 comments.

Read on the blog On Hacker News

// LET'S BUILD

Tell us what you're building.

A few lines on what you need in production. NJ reads every one and replies within a business day, including when we're the wrong team for it. Need a written proposal for your team first? Say so.

Or skip the form Call or WhatsApp NJ at +1 (717) 683-9393. He picks up.

Get your AI feature into production. And keep it there.