SimbaStack
SimbaStack is an AI engineering consultancy and development studio that builds AI features and agents inside founders' and product teams' existing systems, then operates them in production. Its work spans customer-facing agents, internal workflow agents, and guarded decision systems.
At a Glance
- Founders and startup product teams
- Product teams with existing software that need AI features shipped into production
- Hospitality and safari-lodge operators
- Web3 and trading products
- +2 more
AI Tools by SimbaStack
(1)
framedex
Media Archive Indexer CLI Tool
Discussions
No discussions yet
Be the first to start a discussion about SimbaStack
Latest News
MCP output schemas can break clients on edge runtimes
Claude edited my last three YouTube videos in DaVinci Resolve; SimbaStack published resolve-edit-kit
The editing pass can't tell you a sentence is false: AI agents for the lodge blog and Search Console learnings
Every database query cost us four round-trips for six months: a KaribuKit performance fix
Products & Services
A fixed-price two-week engagement to ship one AI feature in the client's environment and repository, with agreed acceptance criteria and 30 days of post-launch support.
An ongoing service providing two embedded engineers, with NJ Singh providing technical direction, production ownership, monitoring, and roadmap delivery.
An MIT-licensed, local-first Claude Code skill and CLI that turns video and photo archives into portable, queryable plain-text knowledge bases with sidecars, metadata, transcripts, translations, face detection, scene descriptions, ratings, and MCP access.
An MIT-licensed open-source QA agent that reads a repository, derives critical business flows, drives a browser with Playwright, checks backend state, reviews UI design, and runs on schedules.
Market Position
SimbaStack positions itself between a conventional AI consultancy and a productized implementation team: it takes ownership of the production integration and ongoing operation rather than stopping at a demo or prototype. Its differentiators are forward-deployed senior engineers, fixed-scope pricing, live internal systems used as proving grounds, and guardrails plus human confirmation for consequential actions. Relevant alternatives include general software/AI consultancies, in-house AI engineering teams, and specialized agent, QA, hospitality, media-indexing, and AI video-editing products.
Leadership
Founders
NJ Singh (Niranjan Singh)
Founder and Chief Architect. The official site says he has spent 14 years building production software, including Pear Protocol's trading platform; he also runs the Mara Hilltop safari lodge in Kenya on software built by the team. His public LinkedIn identifies him as Founder of SimbaStack.
Executive Team
NJ Singh (Niranjan Singh)
Founder & Chief Architect
14 years building production software; worked on Pear Protocol's trading platform and runs the Mara Hilltop lodge on the team's software.
Deep
Senior Engineer & Delivery Lead
Leads client delivery end to end and focuses on making systems work in production rather than only in demonstrations.
Founding Story
SimbaStack grew out of NJ Singh's long-running production-software work and the need to move beyond AI demos into reliable systems with real data, permissions, edge cases, cost controls, and human approval where appropriate. The studio's initial vision is to provide forward-deployed engineers who work in a client's repository, systems, and team cadence, with scoped builds or ongoing embedded delivery.
Business Model
Revenue Model
Services revenue from fixed-price AI implementation sprints and recurring embedded-engineering retainers; the studio also develops and operates its own software projects and open-source tools.
Pricing Tiers
Two weeks, one workflow, in the client's environment and repository, with 30 days of support after launch.
Two engineers embedded with the client team and NJ on technical direction; two-month minimum, then cancel anytime.
Target Markets
- Founders and startup product teams
- Product teams with existing software that need AI features shipped into production
- Hospitality and safari-lodge operators
- Web3 and trading products
- Teams with large local media archives
- Developers and teams needing continuous QA automation
- Shipping AI features from prototype into production
- Website and in-product customer support or booking agents
- Operations, quoting, scheduling, and back-office automation
- Hospitality property management and guest communications
- Trading and other guarded decision workflows
- Searching, organizing, and culling large local video and photo archives
- Pear Protocol
- Mara Hilltop
- KaribuKit