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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Predictive Modeling & Forecasting
Forecasts your team will actually use — demand, churn, risk — with the uncertainty stated rather than hidden.
- Time to value
- Baseline and feasibility in 2 weeks; production in 6–8.
- Deployment
- Can run fully on your infrastructure
- Category
- Cloud, Integration & AI Infrastructure
The manual version of thisInvictt AI layer
Establish the baseline
Build features
Model and validate
Explain
Monitor
Once it's AI-assisted01/The problem
A forecast that is a single number with no error bar is a guess in a suit. Useful prediction tells you how confident it is, what is driving it, and what would change its mind.
02/How it works
The operating model
What enters, what the system does with it, and what reaches you.
Check
Establish the baseline
We measure the naive forecast first — last month repeated, or whatever the team does now. A model that cannot beat it is not worth deploying, and knowing that early is cheap.
System
Build features
Seasonality, promotions, weather, lead time, pipeline stage — whatever genuinely moves your numbers, sourced from systems you already run.
Check
Model and validate
Backtested on held-out time periods, never on random splits, because random splits let a model see the future and flatter itself.
Outcome
Explain
Every prediction comes with its drivers and a confidence interval. Unexplained forecasts do not get used, no matter how accurate they are.
Check
Monitor
Accuracy tracked against reality after the fact, with alerts when the world drifts away from the training data.
Read the detail behind each stage
01
Establish the baseline
We measure the naive forecast first — last month repeated, or whatever the team does now. A model that cannot beat it is not worth deploying, and knowing that early is cheap.
02
Build features
Seasonality, promotions, weather, lead time, pipeline stage — whatever genuinely moves your numbers, sourced from systems you already run.
03
Model and validate
Backtested on held-out time periods, never on random splits, because random splits let a model see the future and flatter itself.
04
Explain
Every prediction comes with its drivers and a confidence interval. Unexplained forecasts do not get used, no matter how accurate they are.
05
Monitor
Accuracy tracked against reality after the fact, with alerts when the world drifts away from the training data.
03/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Naive baseline measurement before any modelling
Feature engineering from your existing systems
Time-aware backtesting and validation
Prediction intervals and driver explanations
Delivery into the tool your team already uses
Ongoing accuracy monitoring and drift alerting
Built with
- scikit-learn
- XGBoost
- PyTorch
- MLflow
- Postgres
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
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.