Predictive Analytics & Optimisation
Stop reacting to problems. Predict deployment failures, identify security risks earlier, and optimise pipeline performance before bottlenecks become blockers.
The Shift from Reactive to Predictive
Most engineering organisations operate reactively. Deployments fail, and then you investigate. Security vulnerabilities surface in production, and then you scramble. Pipeline performance degrades, and then someone notices.
Predictive analytics changes the game. Instead of waiting for problems to happen, you anticipate them. Instead of firefighting, you're preventing fires.
This isn't theoretical. With the right data foundation, models can predict deployment failures before they happen, identify security risks earlier in the development cycle, and surface bottlenecks before they impact delivery velocity.
What You Get
Deployment Failure Prediction
Models that analyse historical pipeline data to identify patterns that precede failures. Which code changes are high-risk? Which combinations of factors correlate with production incidents? Know before you deploy, not after.
Security Risk Scoring
Not all vulnerabilities are equal. Models that prioritise security findings based on actual exploitability, exposure, and business context. Focus your security team's time on the risks that matter most.
Pipeline Bottleneck Identification
Analysis that identifies which stages, tests, or dependencies are most likely to cause delays. Predictive alerts when queue times or build durations are trending toward problems.
Resource Optimisation Recommendations
Models that analyse utilisation patterns and predict capacity needs. Right-size your CI/CD infrastructure based on actual usage, not guesswork.
How It Works
Phase 1: Use Case Definition (Week 1)
Not every prediction is worth making. We work with your team to identify which predictive capabilities would create the most value for your organisation. What decisions would improve if you could see the future? What actions would you take with better predictions?
Phase 2: Data Preparation (Week 2-3)
Building predictive models requires clean, well-structured historical data. If you've completed a Data Foundation engagement, we can move quickly. If not, we'll identify what data preparation is needed and factor that into the timeline.
Phase 3: Model Development (Week 3-5)
Iterative model building with regular check-ins. We'll show you working prototypes early, validate that predictions make sense based on your domain expertise, and refine until we have something useful.
Phase 4: Deployment & Integration (Week 5-6)
A model that lives in a notebook is useless. We deploy predictions where your team will actually use them: integrated into dashboards, triggering alerts, or feeding into existing workflows.
Example Use Cases
Deployment Risk Scoring
Before each deployment, automatically score the risk based on factors like code change complexity, test coverage of affected areas, recent failure patterns, and time since last deployment. High-risk deployments get flagged for additional review or staged rollout.
Security Vulnerability Prioritisation
Instead of treating all critical vulnerabilities equally, prioritise based on predicted exploitability and business impact. A critical vulnerability in a public-facing service gets higher priority than one in an internal tool with no external exposure.
Pipeline Performance Forecasting
Predict build queue times and identify when you're approaching capacity constraints before they impact developer productivity. Plan infrastructure changes proactively instead of reacting to complaints.
Incident Likelihood Prediction
Correlate deployment patterns, code changes, and historical incidents to predict which changes are most likely to cause production issues. Use predictions to inform deployment timing, rollout strategies, or additional testing requirements.
This Service Is Ideal For Teams That...
- Have reliable, historical data (12+ months ideal, 6+ months minimum)
- Experience regular deployment failures and want to reduce them
- Need to prioritise security vulnerabilities more intelligently
- Want to optimise CI/CD infrastructure based on actual patterns
- Have the organisational maturity to act on predictions
Prerequisites
Predictive analytics requires a solid data foundation. If you haven't established reliable data collection and baseline metrics, we'll recommend starting there. Building predictions on unreliable data produces unreliable predictions.
Many clients complete a Data Foundation engagement first, then move to Dashboards for visibility, and finally to Predictive Analytics once they have the organisational context to act on predictions.
A Note on AI and ML
We use machine learning where it adds value, not where it sounds impressive. Many prediction problems are better solved with simple statistical models than complex neural networks. We'll recommend the right approach for your specific use case, whether that's a basic regression model or something more sophisticated.
The goal is predictions that work, not models that impress. Interpretability matters, you should understand why a deployment was flagged as high-risk, not just that it was.
Frequently Asked Questions
12+ months is ideal for most prediction use cases. 6 months is workable for some scenarios but limits what we can predict reliably. The key is having enough examples of the outcomes you want to predict (failures, incidents, etc.) for the model to learn patterns.
Accuracy varies by use case and data quality. A typical deployment risk model might correctly identify 60-70% of failures in advance, while flagging 20-30% of successful deployments as "high risk." We'll be honest about expected performance before we start, and we measure actual results against predictions.
That's why interpretability matters. We build models that explain why a prediction was made, not just what the prediction is. If a deployment is flagged as high-risk, you'll see the specific factors driving that score. Trust builds over time as the predictions prove themselves accurate.
We design integrations based on how your team actually works. Predictions might appear in dashboards, trigger Slack alerts, add comments to merge requests, or feed into CI/CD pipeline gates. The goal is getting predictions in front of decision-makers at the moment they're making decisions.
Predictive models can drift over time as your systems and practices change. We recommend periodic model reviews and retraining, typically quarterly. This can be handled through a retainer arrangement or as separate follow-up engagements. We also document the retraining process so your team can handle it internally if preferred.
ROI depends on the cost of the problems you're predicting. If deployment failures cost you hours of engineer time and customer trust, preventing even a fraction of them pays for the engagement. We'll help you estimate the value during scoping, and we measure actual results against predictions post-deployment.
Ready to Get Started?
Tell us about your predictive analytics & optimisation needs and we'll show you how we can help.
Describe Your Challenge