01
Tech Audit
Vetting before a build, or a clean read on your current AI architecture before you commit.
Production AI engineering for AI companies
Build your AI-native product with engineers who understand LLM applications, RAG, agents, evals, and the production systems around them.
Production architecture
The system we build around your model
Inputs, product data, guardrails, prompts, and evals move together toward a production release.
Audience fit
Is this you?
A quick fit check before either side spends time on a call.
Who this is for
- AI-product startups building LLM applications, AI agents, or RAG systems
- Companies with AI/ML at the core of their product
- Teams with technical founders or AI-literate CTOs
- Funding stage: pre-seed to Series B
- Builders in our 3 industries or adjacent AI verticals
Who this isn’t for
- Companies adding ChatGPT to a generic SaaS; we focus on AI-native products
- AI research labs or academic projects; we build production products
- AI consultancies running campaigns with AI; we build software, we don’t run campaigns
- Pure ML model-training shops; we don’t train custom models from scratch
AI products ship when the team understands the model, not just the framework
Engagements
What we typically do for ai companies
Start with the smallest engagement that resolves the next engineering decision.
02
Project Build
A new AI product MVP or production system, scoped and shipped to production.
03
Embedded Squad
Ongoing AI product development with an embedded engineering team that flexes with your roadmap.
04
AI-Enabled Teams
Velocity-focused engagements that bring our internal AI tooling stack to your build.
05
Project Rescue
A vibe-coded AI prototype rebuilt for production, stabilised, and made reliable.
The stack under our products
Chosen for boring reliability, not novelty. Every tool here has shipped to production for a paying client.
Next.js
React
React Native
TypeScript
Node.js
Python
Supabase
AWS
Docker
GitHub
Figma
Vercel
AI companies we’ve shipped with
AI Ads Assistant for Meta and TikTok
We built a minimum viable product to connect disparate advertising APIs into a single, intuitive user interface.
AI Mental Health Companion
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
AI Contract Generation Platform
We built the technical foundation for a SaaS platform that automates complex legal document creation with usage-based pricing system
Starting point
Where most ai companies start
Tech Audit or Discovery Sprint
For most AI companies, the right starting point is either a Tech Audit (if you’re assessing existing AI architecture) or a Discovery Sprint (if you’re scoping a new AI product). AI-Enabled Teams becomes relevant once you want velocity from our internal tooling stack.
Discuss my projectA note from the founder
From the founder
I started Robust Devs in 2019 after watching too many good products die between agencies. The pattern was always the same: big promises in the sales call, silence by week six. We built this firm to be the opposite of that. A small senior team, honest scope, and the uncomfortable truth delivered early.
If you are building in marketing, healthtech, or fintech, tell me what you are working on. If we are not the right team for it, I will say so on the first call and point you somewhere better.
Tayyab Hanif
CEO, Robust Devs
Frequently asked questions
No. We focus on application-layer engineering with off-the-shelf models. We’ll advise when custom training is the right call, which is rare.
Production AI engineering
Your AI product deserves engineers who understand the model layer
We build LLM applications, RAG systems, and AI agents that ship to production: with evals, guardrails, and observability from day one.
Building an AI-native product? Schedule a meeting.
No sales team, no SDR loop. Speak directly with engineers who have shipped AI products to production.
Schedule a meeting