AI Dev Squads
Build. Ship. Scale.
AI Dev Squads
Full-stack AI development teams that design, build, and deploy complete AI-powered applications, from custom LLMs and AI agents to computer vision, predictive analytics, and cloud-native infrastructure
The Challenge
Building AI-powered applications requires diverse expertise most teams don't have
Full-Stack Complexity
AI applications need frontend, backend, ML engineering, and DevOps skills rarely found in one team
Time to Market
Building AI products from scratch takes 6-18 months with traditional approaches
Production Quality
Many AI proofs of concept never make it to production-grade applications
Ongoing Maintenance
AI applications require continuous model retraining, monitoring, and feature evolution
The Solution
AI Dev Squads deliver complete AI applications from concept to production
Full-Stack Development
React/Next.js frontends, Python/Node backends, and cloud-native infrastructure, all from one team
ML Engineering & MLOps
Model development, training pipelines, A/B testing, and automated retraining for production ML systems
Product-Driven Approach
We think like a product team: user research, UX design, rapid iteration, and measurable outcomes
Cloud-Native Deployment
Kubernetes, CI/CD pipelines, monitoring, and auto-scaling for production-grade AI applications
Full Code Ownership
Complete source code, documentation, and knowledge transfer. You own everything we build
Agile Delivery
2-week sprints with demos, transparent backlog, and continuous stakeholder involvement
Custom LLM Solutions
Fine-tuned language models and AI agents trained on your domain data for intelligent document analysis, content generation, and autonomous workflow automation
Computer Vision & Predictive Analytics
Image/video analysis for quality inspection and defect detection, plus ML models for demand forecasting, risk assessment, and predictive maintenance
How It Works
Scope & Plan
Define product vision, user stories, architecture, and delivery milestones
Sprint Development
2-week agile sprints building features end-to-end with weekly demos
Launch & Iterate
Deploy to production, gather user feedback, and iterate rapidly
Handover & Support
Complete code transfer, documentation, and optional ongoing support
Freelancers/Agencies vs AI Dev Squads
| Aspect | Traditional Agency | AI Dev Squads |
|---|---|---|
| AI/ML Expertise | Limited or outsourced | Core competency |
| Full-Stack Coverage | Frontend or backend specialists | Complete end-to-end team |
| Methodology | Waterfall or loose Agile | Product-driven Agile |
| Code Ownership | Often vendor-locked | 100% your code |
| Production Readiness | Demo quality | Production-grade from day 1 |
2 weeks
Sprint Cycles
100%
Code Ownership
Production
Grade from Day 1
Frequently asked questions
What is an AI Dev Squad?
An AI Dev Squad is a hybrid development team where human engineers (35% strategy) work alongside AI coding agents (65% execution). This model delivers 88% reduction in time-to-code, 94% increase in development velocity, and 86% faster time-to-production.
How does the human-AI split work?
Human leadership handles architecture decisions, code review, quality assurance, and DevOps strategy. AI agents handle code scaffolding (75% automated), test generation (98% coverage), documentation (90% auto-generated), and 24/7 pair programming.
What is the ROI of an AI Dev Squad?
Payback period of 3-4 months, Year 1 savings of 60-70%, and 3-Year ROI of 4-6x. Development velocity doubles (35 to 68 story points per sprint) while time-to-production drops from 6 weeks to 5 days.
What technologies do AI Dev Squads support?
Specialized agents cover .NET/C# (EF Core, SignalR), Frontend (React, Components), Data & ML (Pipelines, Models), and Cloud Ops (Azure, Terraform). The knowledge engine indexes your codebase, ADRs, PR history, and standards with 97% precision.
How quickly can an AI Dev Squad be deployed?
Our 12-week implementation roadmap covers discovery (weeks 1-2), skills and knowledge base setup (weeks 3-4), agent development (weeks 5-6), pilot with 4-5 developers (weeks 7-9), and full org-wide rollout (weeks 10-12).