Packaged AI tiers and managed AI services
AI ready to deploy for mid-market firms, and sovereign production systems for large enterprises
Not every company should build its own AI platform
Mid-market firms want proven AI for common jobs without hiring a research team. Large enterprises need systems that run at scale, inside their own boundary, for years.
Both need the AI to keep working after launch: models change, costs drift and quality must be watched. Few want to run that operation themselves.
What you get
AI in use quickly
Packaged AI solutions that a mid-market firm can put to work quickly.
Systems built for scale
Custom sovereign systems built for high volume and strict control.
Run for you
Models, costs and quality looked after by XSIS, under agreed service levels.
What we deliver
SMB and mid-market AI solutions
Ready-to-deploy AI packages for common business jobs, configured to your data and systems.
Enterprise AI solutions
Custom, high-scale sovereign production systems, hosted in your own environment or a regional sovereign cloud, with your access rules built in.
Managed AI API, subscription and usage-based services
AI capabilities offered as a managed API, on subscription or by usage, with monitoring, model updates and cost control included.
How it works
Match the tier to the need
We review the job, the volume, the data and where it must run, and recommend a package, a custom system or a managed service.
Configure or build
We configure a package to your data and systems, or build the custom system, and test it on your own cases.
Launch with controls
We release with access rules, logging and evaluation in place, and agree the measures of quality and cost.
Run and improve
We monitor quality, usage and cost, update models as they change and report to you on agreed service levels.
Technologies and standards
Managed services rest on AgentOps telemetry, evaluation, hallucination guardrails and token cost control, with semantic caching and model routing to keep inference spend down.
- Methods and practices
- AgentOps
- Semantic caching
- Model routing
- Managed AI APIs
How to start
Mid-market clients usually start from a package and a short proposal. Enterprises start with the sovereign agentic proof of concept, then move to production and managed service.
What happens next
Agreeing a time
Our team emails you to agree a time for the session.
Pre-consulting session
A free 30-minute online pre-consulting session: we go through your goals, systems and constraints.
Readiness review
In a free 48-hour readiness review, a Principal Architect checks your project against the 6-point Definition of Ready and recommends the track that fits.
Choosing the track
Scope-ready projects receive a binding proposal within 3–5 business days. Concept-stage projects start in the Blueprint Studio. A live system that is failing, undocumented or needs integration starts with a diagnostic of 1–2 weeks. The Blueprint Studio and the diagnostic each have a fixed fee, quoted in the proposal.
Fixed-outcome projects and round-the-clock managed services
Fixed-scope, milestone-driven delivery with named delivery leads, and round-the-clock application managed services under SLA governance.
The quickest way in is the Sovereign agentic POC (MCP, A2A, GraphRAG) package, one of our rapid engagement packages.
Questions and answers
What is the difference between the AI tiers?
Packages are ready-to-deploy solutions for common jobs, configured rather than built. Enterprise systems are custom and sovereign, built for high volume. Managed services keep either running, on subscription or by usage.
Which tier is right for us?
It depends on how standard the job is, the volume, and where your data must stay. A common job at moderate volume suits a package. Strict residency, deep integration or high volume points to an enterprise system.
Where does our data go in a managed service?
Where you decide. Enterprise systems run inside your environment or a regional sovereign cloud, and no third party trains on your data. We agree the hosting and residency before anything is deployed.
What support comes with a managed AI service?
Monitoring, incident response, model updates and regular reports on quality, usage and cost. Response times are set in the service agreement, by severity.
How do you keep running costs under control?
We track token and compute spend per use case, cache repeated questions and route routine tasks to smaller models, keeping large models for the work that needs them.
Can we move from a package to a custom system later?
Yes. When you need more scale or control, we plan the move so your data, evaluation sets and integrations carry over.
Who owns what we build together?
Custom code, prompts, evaluation sets and fine-tuned model weights produced for you are yours once each milestone is paid. Packaged components and base models stay under their own licence terms, which we list for you.
Start with a conversation, not a contract.
Tell us what you need to build, connect or secure. An architect goes through it with you, then recommends where to start.