Engagement Models for Every Stage
Every company's AI journey is different. We don't believe in one-size-fits-all pricing — we believe in the right engagement for your stage, your goals, and your team. Contact us to discuss your specific needs and we'll design an approach that fits.
Starter Sprint
A focused 2-week engagement to audit your AI prototype and deliver a production readiness report with a clear architecture roadmap.
What's included?
- AI prototype audit & gap analysis
- Production architecture blueprint
- Technology stack recommendations
- 2-week turnaround, fixed scope
Production Build
Our core engagement — a full build-out from your PoC to a deployed, monitored, enterprise-grade AI system. Ideal for Series A+ startups and enterprise teams.
What's included?
- End-to-end AI engineering & deployment
- LLM fine-tuning, RAG, or agent workflows
- Cloud infrastructure setup & MLOps pipelines
- Monitoring, alerting, and SLA guarantees
Fractional AI Team
Embed our senior AI engineers into your team on a monthly retainer. Perfect for companies that need ongoing AI expertise without a full-time hire.
What's included?
- Dedicated senior AI engineer (part or full time)
- Weekly syncs and architecture reviews
- On-demand model optimization and debugging
- Priority support and rapid response SLA
Frequently Asked Questions
Everything you need to know about working with Tensorplay.
Do you work with startups at the pre-seed or seed stage?
Yes. We work with companies across all stages. For early-stage startups, our Starter Sprint is designed to give you a clear technical roadmap without a large upfront commitment.
What AI frameworks and cloud platforms do you work with?
We're framework-agnostic and cloud-agnostic. Our engineers have deep expertise in PyTorch, LangChain, LlamaIndex, Hugging Face, and deployment on AWS, GCP, and Azure. We recommend the right stack for your specific use case and team.
How long does a typical Production Build engagement take?
Most Production Build engagements run 6–16 weeks depending on complexity. We always begin with a Discovery phase so both parties have full visibility into scope, timelines, and deliverables before the build starts.
Can Tensorplay take over an existing AI project that another team started?
Absolutely. This is one of our most common scenarios. We'll conduct a thorough audit of the existing code, infrastructure, and model artifacts, and then propose an efficient path to get the system to a production-ready state.