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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MLOps & Deployment
Getting the model out of the notebook and into something that stays up on a Sunday.
- Time to value
- 4–8 weeks depending on your existing infrastructure.
- Deployment
- Can run fully on your infrastructure
- Category
- Cloud, Integration & AI Infrastructure
The manual version of thisInvictt AI layer
Package
Serve
Version
Monitor
Retrain
Once it's AI-assisted01/The problem
The model scored well in the notebook. Now it has to serve traffic, version cleanly, roll back in one command, and tell somebody when the world has drifted away from its training data. That is a different discipline, and it is where most models stall.
02/How it works
The operating model
What enters, what the system does with it, and what reaches you.
System
Package
Reproducible containers with pinned dependencies, so the thing that runs in production is the thing that was tested.
Integration
Serve
Batching, autoscaling and GPU scheduling sized to your actual traffic shape rather than a peak that never happens.
Check
Version
Every model and dataset versioned, with shadow deployment and one-command rollback.
Check
Monitor
Latency, throughput, prediction distribution and input drift on one dashboard, with alerts that go to a person.
System
Retrain
A scheduled or drift-triggered retraining path that does not require the original author to still work there.
Read the detail behind each stage
01
Package
Reproducible containers with pinned dependencies, so the thing that runs in production is the thing that was tested.
02
Serve
Batching, autoscaling and GPU scheduling sized to your actual traffic shape rather than a peak that never happens.
03
Version
Every model and dataset versioned, with shadow deployment and one-command rollback.
04
Monitor
Latency, throughput, prediction distribution and input drift on one dashboard, with alerts that go to a person.
05
Retrain
A scheduled or drift-triggered retraining path that does not require the original author to still work there.
03/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Reproducible containerised packaging with pinned dependencies
Autoscaling inference with request batching
Model and dataset versioning with shadow deploys
One-command rollback
Latency, throughput and drift monitoring with alerting
Automated retraining pipeline and full handover documentation
Built with
- Docker
- Kubernetes
- MLflow
- Prometheus / Grafana
- GitHub Actions
Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.
04/Example work
What this looks like in practice
Reference implementations, including what went wrong.
05/Also in this category
Cloud, Integration & AI Infrastructure
What lets everything above run safely, and keep running.
ConnectSecureScale
MCP integrations
Cloud deploymentCloud-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.
Classify the workload → Integrate behind an abstraction → Control cost and exposure
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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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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.