Most companies need AI engineering expertise for 6-18 months, not permanently. We deploy fractional AI engineers who architect, build, and launch production systems — then transition ownership to your team.
Who this is for
Series A-C companies with $2M+ ARR needing AI features
Teams building customer-facing AI products or internal automation
Engineering teams without ML/AI expertise but strong backend fundamentals
Companies burned by AI consultancies that delivered demos, not production code
Organizations needing AI systems in 3-6 months, not 18+ month research projects
What fractional AI engineering actually delivers
We architect and build production AI systems, not proof-of-concepts. Our fractional engineers ship end-to-end solutions: data pipelines, model training infrastructure, API endpoints, monitoring dashboards, and deployment automation.
Recent deliverables include a customer support AI that reduced ticket volume 40% for a B2B SaaS company, and a document processing system handling 10k+ PDFs daily for a fintech client. Both systems run in production 18+ months later.
The engagement includes knowledge transfer sessions, technical documentation, and code reviews with your existing team. By month 6, your engineers can maintain and extend the system independently.
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Technical capabilities and stack expertise
Our fractional AI engineers specialize in production-ready systems using modern ML stacks. We deploy PyTorch and TensorFlow models, build real-time inference APIs with FastAPI, and implement vector databases like Pinecone or Weaviate for RAG applications.
Cloud infrastructure spans AWS, GCP, and Azure — including Kubernetes orchestration, MLflow model registries, and automated CI/CD pipelines. We integrate with existing data stacks (Snowflake, BigQuery, Databricks) rather than forcing architectural changes.
For LLM applications, we implement fine-tuning pipelines, prompt optimization workflows, and cost monitoring systems. A recent client reduced OpenAI costs 70% through our prompt engineering and model selection optimization.
Engagement model and timeline expectations
Standard engagements run 3-12 months at 20-40 hours per week. Month 1-2 focuses on system architecture, data pipeline setup, and initial model development. Months 3-4 involve production deployment, monitoring implementation, and performance optimization.
Final months emphasize knowledge transfer and team training. We conduct code walkthroughs, document architectural decisions, and run troubleshooting workshops with your engineering team.
Pricing ranges $8k-15k monthly depending on seniority level and time commitment. This includes infrastructure setup, model development, deployment automation, and documentation — typically 60% less than equivalent full-time hiring costs.
Quality assurance and handoff process
Every system ships with comprehensive monitoring: model drift detection, performance metrics, error alerting, and cost tracking dashboards. We implement gradual rollout strategies with automatic failover to ensure production stability.
Code quality standards include 80%+ test coverage, type hints throughout Python codebases, and architectural documentation explaining design decisions. Git repositories include setup instructions, deployment guides, and troubleshooting runbooks.
The handoff process involves 4-6 knowledge transfer sessions covering system architecture, common maintenance tasks, and scaling considerations. Your team receives recorded sessions, technical documentation, and 30 days of post-handoff support for critical issues.
Industries and use case specialization
We focus on B2B SaaS, fintech, and e-commerce companies building customer-facing AI features. Common projects include intelligent document processing, recommendation engines, fraud detection systems, and customer support automation.
Recent engagements span automated contract analysis for legal tech startups, personalization engines for e-commerce platforms, and anomaly detection systems for cybersecurity companies. Each project required different ML techniques but similar production engineering challenges.
We avoid early-stage research projects or unproven business models. Our sweet spot: companies with clear AI use cases, existing technical teams, and 6+ month implementation timelines.
Fractional AI engineering works best for companies that need production systems, not endless research cycles. If you have a specific AI use case, existing engineering talent, and a 3-12 month timeline, this model delivers faster results at lower costs than full-time hiring. The key is choosing practitioners who've shipped production AI systems, not consulting generalists who speak fluent buzzwords.
Frequently asked questions
Answered by The Editor, with notes from Atlas and Roxy.
How is fractional AI engineering different from traditional AI consulting?
We write production code, not slide decks. Traditional consultancies deliver strategy documents and proof-of-concepts. Our fractional engineers ship working systems with monitoring, documentation, and handoff processes. You get maintainable code, not vendor dependency.
What happens if the fractional engineer leaves mid-project?
All code lives in your repositories with comprehensive documentation from day one. We maintain backup coverage across team members and provide detailed architectural documentation. Most handoffs happen smoothly because systems are built for maintainability, not consultant dependency.
Can fractional AI engineers work with our existing tech stack?
Yes, we integrate with existing infrastructure rather than forcing rewrites. Common integrations include PostgreSQL databases, REST APIs, existing authentication systems, and cloud platforms like AWS or GCP. We adapt to your stack, not the reverse.
How do you handle data security and compliance requirements?
We work within your security policies and sign standard NDAs and data processing agreements. For regulated industries, we implement appropriate controls like data encryption, audit logging, and access restrictions. Your data stays in your infrastructure.
What if our AI project requirements change during the engagement?
We build systems with flexibility in mind and conduct monthly scope reviews. Minor requirement changes get incorporated naturally. Major scope changes may extend timelines or require additional budget, which we discuss transparently before implementation.
How do we know if fractional AI engineering is right for our company?
It works best if you have a specific AI use case, at least 2-3 existing engineers, and a 3-12 month timeline. If you need ongoing AI research or lack technical infrastructure, full-time hiring or traditional consulting might be better options.
