Services / Case Studies
We design, build, and stabilise data and AI systems where failure is expensive and ownership matters.
Our work spans platform engineering, applied AI, and operating models for AI-native organisations.
Selected engagements below are anonymised by design
Pantheon — An Agentic AI CRM Built for Production
AI-native B2B CRM platform, designed and operated end-to-end in live production
Pantheon was built as an AI-native CRM intended to operate as a system of record, not a layer of automation bolted onto an existing workflow. The system needed to handle real operational complexity, including persistent state, security boundaries, and auditability, while embedding AI directly into day-to-day work.
The platform spans a PostgreSQL data model, Python and FastAPI services, and a React and TypeScript frontend. AI agents generate and send emails, receive and interpret replies, place and transcribe calls, summarise interactions, and update CRM state based on outcomes and newly surfaced information. AI components were treated as fallible subsystems, with explicit schemas, observability, and failure modes designed in from the outset.
The result is a production CRM where AI augments human operators without obscuring ownership, correctness, or system behaviour under load.
Data Foundations Under Scrutiny
Global enterprise SaaS platform with complex product and commercial decision-making
This engagement focused on rebuilding a data platform in an environment where data existed but could not be trusted. Ownership was fragmented across teams, definitions had drifted over time, and confidence in reported numbers had collapsed to the point where decisions relied on manual reconciliation.
The work centred on restoring the ability to reason about the data at all. Ingestion and modelling layers were rebuilt to make assumptions explicit, transformations inspectable, and lineage traceable end-to-end. Governance was enforced through structure rather than policy, with clear ownership boundaries and controlled schema evolution.
Instead of optimising for speed of delivery, the system was designed to remain stable as sources, teams, and requirements changed. The outcome was a platform teams could explain, defend, and extend, re-establishing confidence in shared metrics under ongoing scrutiny.
Decision Systems That Changed Behaviour
Tier-1 UK telecommunications environment with regulatory and operational constraints
This work addressed a common failure mode: extensive reporting infrastructure that informed reviews but failed to influence decisions. Dashboards existed, but metrics were interpreted differently across teams, and incentives remained misaligned with operational reality.
The analytics layer was redesigned around decision points rather than outputs. Metrics were pruned to remove noise, definitions were aligned across product, operations, and leadership, and analytics were embedded directly into workflows rather than presentation decks. Low-signal measures were removed entirely, forcing clarity at the point of action.
The result was not more reporting, but fewer disputes. Teams converged on shared interpretations of performance, parallel analysis diminished, and decisions became traceable to a consistent operational view rather than competing narratives.
AI Systems That Survived Production Reality
People-powered insight platform operating across live customer and employee data
This engagement followed early AI experimentation that demonstrated promise but failed under real-world conditions. Reliability issues, unclear cost profiles, and unexamined failure modes made the systems unsuitable for operational use.
The work shifted focus from expansion to containment. Use cases were narrowed, evaluation criteria defined, and AI components placed behind explicit interfaces with monitoring and rollback paths. Human-in-the-loop controls were introduced where outcomes carried operational or reputational risk.
Rather than maximising automation, the system prioritised auditability, predictability, and ownership. The resulting AI capabilities delivered value in practice, operating within known constraints and without introducing silent failure modes into production systems
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