Services
Enterprise AI services from Architecture Brief to production handover
Ten services. Each page lists who hires us, what usually went wrong before, the steps and artifacts, the stack, the case studies, and when we are the wrong fit.
Quick Summary
Xenqube Services: Xenqube delivers ten enterprise AI and engineering services for regulated operators: enterprise AI consulting with fractional CTO support, AI agent development, LLM application development, RAG development, private AI deployment, MLOps and platform engineering, data engineering and analytics, full-stack product development, the 51-Hour MVP Sprint, and Rust smart contract audits.
- ·32+ production AI systems; 15 published case studies with before/after ranges
- ·Scoped pilots run 4-12 weeks when data access exists; agents follow Connect, Shadow, Assist, Scale
- ·51-Hour MVP Sprint from $3,000, fixed scope, client owns the code
- ·Engagement models: fixed-scope pilot, production retainer (month-to-month), embedded pod of one architect plus two to four senior engineers
- ·Stack named per service: Temporal, MCP, LangGraph, Vercel AI SDK, vLLM, pgvector, Qdrant, dbt, Terraform, Kubernetes, Next.js
- ·September 2026: CTO as a Service, ai-automation, generative-ai, devops, cloud-architecture, data-analytics, saas-development, and api-development merged into the ten services above
Strategy & Advisory
Roadmaps and fractional technical leadership that end in a scoped build, not a slide deck.
AI & Machine Learning
Agents, LLM applications, retrieval systems, and private model stacks built into an existing system of record.
AI Agent Development
Agents that act only through an approval gate
6–14 weeks · Fixed-scope pilot, Production retainer, Embedded pod
Proof · Banking
Payment exception handle time: 12–25 min median
LLM Application Development
LLM features built into the product you already run
6–16 weeks · Fixed-scope pilot, Production retainer, Embedded pod
Proof · Broadcast / Media
First-draft formats: 11 surfaces in PoC scope
RAG System Development
Retrieval over your documents, with citations you can check
6–12 weeks · Fixed-scope pilot, Production retainer
Proof · Legal
Research time per matter: ~45-90 minutes
Private AI Deployment
Open-weight models inside your VPC or on-premise
4–10 weeks · Fixed-scope pilot, Production retainer
Proof · Banking
Payment exception handle time: 12–25 min median
Platform & Operations
The infrastructure that keeps models, pipelines, and data reliable after go-live.
MLOps, LLMOps, and Platform Engineering
The infrastructure that keeps AI working after go-live
6–12 weeks · Fixed-scope pilot, Production retainer, Embedded pod
Proof · Banking
Payment exception handle time: 12–25 min median
Data Engineering and Analytics
The data foundation the AI work depends on
6–14 weeks · Fixed-scope pilot, Production retainer, Embedded pod
Proof · Tech Services
Weekly research time: ~20-40 minutes (review)
Product Engineering
Full products and fixed-scope prototypes when the AI feature needs a real application around it.
Full-Stack Product Development
The application around the AI feature
8–24 weeks · Fixed-scope build, Embedded pod, 51-Hour MVP Sprint
Proof · Travel & Marine Tech
Boat types supported: 6 categories with AI-matched recommendations
51-Hour MVP Sprint
Fixed-scope vertical slice in 51 hours
51 hours · Fixed scope
Proof · Travel & Marine Tech
End-to-end marketplace in under 90 days: AI recommendations, dynamic pricing, sk
Security & Web3
Rust smart contract audits and security reviews for teams shipping on Solana, CosmWasm, and Near.
Choosing
Which service first
- You have a queue worked by people and a system of record it lives in
- AI Agent Development
- Answers must cite a document and the corpus cannot leave
- RAG System Development
- Legal or residency rules block API model vendors
- Private AI Deployment
- Twelve ideas, no ranking, board wants a plan this quarter
- Enterprise AI Consulting
- Models are live but nobody can evaluate, deploy, or roll back safely
- MLOps and Platform
- The AI feature needs a real product around it
- Full-Stack Product Development
- You want to test an idea before committing budget
- 51-Hour MVP Sprint
Questions
Frequently asked
- What services does Xenqube offer?
- Ten services in five groups: enterprise AI consulting (including fractional CTO); AI agent development, LLM application development, RAG development, and private AI deployment; MLOps and platform engineering, and data engineering; full-stack product development and the 51-Hour MVP Sprint; and Rust smart contract audits.
- How do engagements usually start?
- With an Architecture Brief from the contact form. We reply with a stack sketch, risks, and a 12-week plan. Scoped pilots run 4–12 weeks when data access exists; the 51-Hour MVP Sprint is the fast path for a vertical slice.
- Do you sell products or custom builds?
- Both. Xenith products (Sort, Twin, Yard, Spatial, Desk, Agents, RAG, Private AI) ship inside the relevant service. Custom engineering covers the workflow, compliance boundary, and integrations the product does not.
- What happened to CTO as a Service, DevOps, and API Development?
- They were merged in September 2026. Fractional CTO work runs under Enterprise AI Consulting; DevOps and cloud architecture under MLOps and Platform; SaaS and API builds under Full-Stack Product Development. Old URLs redirect.
Next step
Not sure which service? Send the workflow.
We reply with an architecture sketch and the service that fits, or tell you that AI is the wrong tool for it.