Representative delivery scenarios showing a balanced mix of data engineering, streaming, AI and LLM assurance, performance and resilience work. Specific client engagements can be discussed where appropriate and under NDA.
A UK financial services organisation had business-critical data spread across legacy databases, scheduled file transfers and manually maintained reporting processes. Delivery.
The programme needed a modern platform that could support analytics, operational reporting and future AI use cases without losing control of lineage, access or data quality.
We would establish a layered lakehouse architecture, define ingestion patterns for batch and near-real-time sources, and introduce repeatable transformation and validation.
Migration would be phased so high-value datasets move first, with reconciliation against the source estate and measurable acceptance criteria for completeness, timeliness and.
A governed platform design with reusable ingestion patterns, automated data quality controls, clearer ownership and a migration path that reduces dependence on manual reporting.
An organisation was preparing an internal LLM assistant that used retrieval-augmented generation to answer questions from policy, operational and knowledge-base content..
We would build an evaluation dataset around real business questions, difficult edge cases and known ambiguity in the source content. Testing would cover retrieval quality, answer.
Results would be converted into repeatable quality gates so future model, prompt and knowledge-base changes can be regression tested rather than assessed manually each time.
A clear go-live view of the highest-risk failure modes, evidence for stakeholders, prioritised remediation actions and an automated evaluation framework that can run as part of.
A digital service needed to move operational events between systems with predictable throughput and traceability. The existing integration pattern relied heavily on synchronous.
We would design a Kafka-based event flow with clear partitioning and keying rules, schema governance, idempotent processing, retry and dead-letter handling, and observability for.
The engineering work would be paired with performance testing so partition counts, consumer scaling and database connection pools are validated before production demand reaches.
A more decoupled integration architecture with controlled replay, measurable consumer lag, clearer recovery behaviour and an operating model that supports scale without losing.
An AI-enabled digital service depended on an LLM provider, vector search, product APIs and transactional services. Individual components had been tested, but the end-to-end.
We would create realistic user journeys and load profiles, measure end-to-end latency as well as model response time, and inject controlled dependency failures to test timeouts.
AI-specific measures such as token usage, model latency and answer quality would be viewed alongside conventional platform measures such as CPU, memory, queue depth, error rate.
A production-readiness view that joins AI quality with traditional non-functional engineering: where the system saturates, how it behaves when dependencies fail, what users.
Tell us about your data platform, AI initiative or production-readiness challenge. We can help shape the right engineering and assurance approach for your programme.