AI Engineering Studio for B2B SaaS
We build AI systems that work in production — not just in demos.
LotusNex designs and builds production AI systems for B2B SaaS teams — whether you're introducing AI into your product, stabilizing systems under real load, or scaling AI across your platform. Architecture-first. Built for the team that inherits it.
B2B SaaS · Production systems · Regulated environments
Sound familiar?
You know AI should be part of your product. You're not sure how to start without building the wrong thing.
The pressure is real — from investors, from competitors, from your roadmap. The risk is equally real: starting with the wrong architecture means months of rebuilding. You need constraints defined and a design validated before writing a line of production code.
It works in your demo. Real users are a different story.
Your AI feature impresses in controlled conditions. With real users, real data, and real edge cases, it hallucinates, breaks, or fails silently — and you're not sure why.
Your team is arguing about whether to rebuild.
The prototype was fast to ship. Now it's load-bearing and nobody fully understands it. Fixes create new bugs. The architecture wasn't designed for production.
You need to ship — but you can't afford to ship broken.
Investors want to see the AI working. Customers are already using it. The cost of a visible failure is higher than the cost of getting it right the first time.
This is the moment we're built for.
Selected work
Permission-aware knowledge retrieval for a regulated SaaS platform
Zero permission violations in 6 months of production use. Retrieval latency held under 380ms at p95 across 40,000+ indexed documents.
Read the architecture pattern →Agentic workflow system with human approval gates for an operations team managing production processes.
Operations team assumed full ownership by week 10 with no post-handoff support. Agent operating cost remained within 8% of projected budget at 90 days.
Read the architecture pattern →What we build
Your agentic workflows need approval gates, audit trails, and defined failure modes — not just a loop that runs until it crashes.
Agent Orchestration Systems
- Tool integrations + permissioning
- Human-in-the-loop approvals
- Logging, evals, and cost controls
Your RAG implementation retrieves content. It doesn't enforce who should see what, or measure whether it's actually working.
Retrieval Infrastructure
- Ingestion + chunking + indexing
- Permission-aware retrieval
- Evaluation harness + regression checks
AI systems need the same CI/CD, observability, and reliability engineering as any other production software. Most don't have it.
Platform & Reliability Engineering
- Cloud & hybrid architectures
- DevSecOps + CI/CD automation
- Performance + scalability engineering
How we're different.
Freelancers prototype and leave. We architect, build, harden, and hand off — with runbooks and documentation your team can operate without us.
Agencies build to spec. We start with an architectural review — defining constraints, failure modes, and evaluation strategy before a single line of production code is written.
Consultancies advise. We build. You get a running, observable, tested system — not a strategy deck.
Hiring a senior AI engineer takes 3–6 months. We're in architecture review within a week. When the engagement ends, the system, the documentation, and the operational knowledge stay with your team.
Point of view
Production software fails when it's designed to demo, not to operate.
Whether it's an AI agent, a retrieval pipeline, or a custom integration — production systems have orchestration layers, failure modes, data contracts, and operational constraints. These must be engineered, tested, and monitored.
How we work
Clear milestones, transparent communication, and a bias for shipping—with production constraints defined upfront.
1) Architecture Review
- Threat model
- Data access plan
- Evaluation strategy
2) Build
- Integrations
- Retrieval pipeline
- Agent orchestration
3) Hardening & handoff
- Observability
- Guardrails + approvals
- Runbooks + documentation
Built by engineers, not consultants.
Every engagement is led personally by the founder — architecture through handoff.
LinkedIn →
Before you ship — know where your system will break.
We begin every engagement with an Architecture Review — a focused assessment of your system's data boundaries, evaluation strategy, and operational risks.
You'll leave with a clear picture of where your system will fail in production — and how to fix it.
No commitment required. You'll leave with a clear architectural assessment — whether we work together or not.
Not ready for an Architecture Review? Start here.
Download our Production AI Checklist — 12 architectural questions your system should answer before it ships.
Download the checklist →We work with 2–3 companies at a time to maintain depth of engagement. Current intake: open.