We help SaaS companies, startups, and software businesses automate internal operations, reduce churn, and improve delivery pipelines. Your engineers focus on the product. We focus on the operational AI tooling that helps your business scale without proportional headcount growth.
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Simulated deployment pipeline. Real implementations connect to your CI/CD toolchain, alerting, and observability stack.
As the customer base grows, operational workload grows with it. Support tickets, onboarding, billing queries, and internal processes consume more time without generating more revenue.
Manual steps in CI/CD pipelines, inconsistent deployment practices, and limited observability slow release cycles and increase the blast radius of incidents.
Churn is identified when customers cancel, not before. Without predictive signals, retention is reactive and the highest-risk accounts do not get attention until it is too late.
Fast growth creates shortcuts. Legacy code, undocumented systems, and inconsistent architecture slow delivery and make onboarding new engineers expensive.
Security reviews happen at delivery milestones rather than continuously. Vulnerabilities accumulate between reviews and are expensive to remediate when discovered late.
Data from product, billing, and support systems is difficult to unify. Analytics relies on manual exports and fragile pipelines that break when upstream systems change.
Automated build, test, and deployment pipelines with AI-assisted anomaly detection. Faster releases, fewer manual steps, and earlier detection of issues before they reach production.
Machine learning models trained on your product usage, billing, and support data that identify accounts at risk of churning before they cancel. Surface signals early enough to act.
Continuous automated code quality and security analysis integrated into your development workflow. Issues are flagged at the point of introduction, not at the point of discovery.
Intelligent monitoring systems that reduce alert noise, surface genuine anomalies, and provide context with each alert. Less time firefighting, more time building.
Unified analytics across your product, marketing, and support data. Understand feature adoption, identify power users, and surface the behavioural patterns that predict long-term retention.
Robust, well-documented data pipelines that consolidate your product, billing, and support data. Self-healing pipelines that alert on failures and recover without manual intervention.
We talk through the operational bottlenecks that are slowing your team down or costing your business. We identify the highest-impact problem to solve first and agree what a successful outcome looks like.
We review your current architecture, data sources, and toolchain. For data-driven projects, we assess data quality and availability. For pipeline work, we review your existing infrastructure and deployment process.
We design the solution and share the architecture with your engineering team before building. We use your preferred stack where possible and document design decisions so your team understands what is being built and why.
We build iteratively with clear milestones. For ML projects, model performance is validated against held-out data before deployment. For pipeline and automation work, staging environments are used throughout.
We deploy into your environment, run through the solution with your team, and provide documentation, runbooks, and code that your engineers can own and extend from day one.
We are available after deployment for questions, edge cases, and model retraining as your data grows. Most clients find they need minimal support after a well-documented handover.
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Your technical team is focused on building your product. AI consulting brings focused expertise on specific problems such as churn prediction, data pipeline automation, or operational AI tooling without pulling your engineers off core development. We deliver a solution and hand it over, so your team can maintain and extend it.
Most engagements for tech companies are delivered in 4 to 8 weeks because you typically have better data infrastructure and technical foundations than other sectors. Churn prediction and internal automation projects are often the fastest. More complex data pipeline or infrastructure AI work may take longer.
Yes. We build solutions that integrate with the tools and infrastructure you already use. We work in your cloud environment, use your preferred languages and frameworks where possible, and produce code that your team can maintain. We do not build black-box solutions that create dependencies on us.
We work under NDA, within your own infrastructure where possible, and follow the principle of least privilege for data access. We are happy to work with anonymised datasets during model development and only access production data when necessary and with explicit authorisation.
Costs are scoped per project. Most focused AI engagements for tech companies fall between £10,000 and £50,000 depending on scope and data complexity. We always agree a clear scope and fixed price before starting so there are no surprises.
Book a discovery call to discuss which AI automation will have the most impact on your business right now, without pulling your engineering team off the product.