MCP integrationsPrivate MCP Integrations
A safe, audited bridge between your internal systems and your AI tools — so agents can act on real data without a blank cheque.
Inventory → Scope → Operate
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Cloud-Based AI Deployment
Managed model hosting, frontier API integration, and hybrid architectures that use the cloud where it is the right answer and keep the sensitive parts on-prem where it is not.
- Time to value
- Architecture decision in 1-2 weeks; integrated in 3-5.
- Deployment
- Hybrid — sensitive steps can stay on-prem
- Category
- Cloud, Integration & AI Infrastructure
Either
cloud, on-premise, or a deliberate mix
The manual version of thisInvictt AI layer
Classify the workload
Choose the platform honestly
Integrate behind an abstraction
Build the hybrid boundary
Control cost and exposure
Once it's AI-assisted01/The problem
Running everything on your own hardware is the right call for some workloads and an expensive way to be slower for others. Committing to either extreme as a policy — all on-prem, or all cloud — means paying for it somewhere: in capital, in compliance exposure, or in the months it takes to discover the constraint you did not model.
02/How it works
The operating model
What enters, what the system does with it, and what reaches you.
Check
Classify the workload
Every component gets assessed separately on data sensitivity, latency budget, volume and cost curve. It is normal for one system to end up with document storage and embeddings on-premise and generation in a cloud region — the decision is per component, never a blanket policy.
System
Choose the platform honestly
Managed hosting on AWS Bedrock, Azure AI Foundry, Google Vertex, or direct frontier APIs — selected on your actual region, procurement position and volumes rather than on whichever we used last.
Integration
Integrate behind an abstraction
Model access sits behind an internal interface, so switching provider, or moving a workload back in-house, is a configuration change rather than a rewrite. This is the single cheapest piece of insurance in the whole architecture.
System
Build the hybrid boundary
Where data sensitivity requires it, the boundary is explicit and enforced: redaction and tokenisation before egress, on-prem embeddings with cloud generation, or on-prem inference with cloud burst capacity for peaks.
Check
Control cost and exposure
Per-tenant budgets, spend alerting, caching and model routing so cheap requests do not use expensive models. Plus data-processing terms and residency documented properly enough to hand to your compliance team.
Read the detail behind each stage
01
Classify the workload
Every component gets assessed separately on data sensitivity, latency budget, volume and cost curve. It is normal for one system to end up with document storage and embeddings on-premise and generation in a cloud region — the decision is per component, never a blanket policy.
02
Choose the platform honestly
Managed hosting on AWS Bedrock, Azure AI Foundry, Google Vertex, or direct frontier APIs — selected on your actual region, procurement position and volumes rather than on whichever we used last.
03
Integrate behind an abstraction
Model access sits behind an internal interface, so switching provider, or moving a workload back in-house, is a configuration change rather than a rewrite. This is the single cheapest piece of insurance in the whole architecture.
04
Build the hybrid boundary
Where data sensitivity requires it, the boundary is explicit and enforced: redaction and tokenisation before egress, on-prem embeddings with cloud generation, or on-prem inference with cloud burst capacity for peaks.
05
Control cost and exposure
Per-tenant budgets, spend alerting, caching and model routing so cheap requests do not use expensive models. Plus data-processing terms and residency documented properly enough to hand to your compliance team.
03/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Per-component workload classification on sensitivity, latency and cost
Managed hosting setup on AWS Bedrock, Azure AI Foundry or Google Vertex
Direct frontier API integration with failover
A provider abstraction layer so switching or repatriating is configuration, not a rewrite
Hybrid boundaries: redaction before egress, on-prem embeddings with cloud generation, cloud burst capacity
Cost controls — budgets, caching, model routing and spend alerting
Residency, retention and data-processing documentation for compliance review
A costed comparison against the equivalent on-premise deployment
Built with
- AWS Bedrock
- Azure AI Foundry
- Google Vertex
- Terraform
- Docker
Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.
04/Also in this category
Cloud, Integration & AI Infrastructure
What lets everything above run safely, and keep running.
ConnectSecureScale
MCP integrations
Content generationAI Content Generation
Text and image production pipelines that hold your brand's voice across real volume.
Encode the voice → Ground the facts → Review
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GenAI product featuresCustom GenAI Product Features
Generative capability embedded inside your own product, not bolted on beside it.
Find the moment → Design for editability → Control cost
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Is this the problem you have?
Tell us what it looks like at your end. We will say honestly whether this is the right fit — including when it is not.