Cipher Solutions

Project summary: Our actuarial models had outgrown the reporting infrastructure feeding them. Data latency was introducing risk into pricing decisions that the business had decided it could no longer accept.

We had worked with three agencies before this engagement. The comparison is not flattering to the others. What distinguished this team was a systematic approach to understanding the problem before proposing a solution — something that sounds obvious and is practiced far less often than it should be. The delivery phase ran to schedule, the codebase is clean enough that our internal engineers made positive comments during handover review, and we have not logged a critical incident in five months of live operation. We intend to use them for our next phase of work.

Project summary: Dynamic pricing had been a manual process for years. We knew the revenue management opportunity was significant but lacked the technical capability to build the models and connect them to our booking engine.

The technical quality of the final deliverable is the easiest thing to point to. The automated test coverage is thorough, the deployment pipeline is reliable, the documentation is genuinely useful rather than ceremonially produced. But the metric I keep returning to is the number of post-launch conversations we have not had to have. No incident calls at two in the morning. No emergency patches. No retrospective discussions about what went wrong. The absence of those events is the evidence I would show to someone considering this vendor.

Project summary: Our legacy LMS had been built for a classroom-first world. Hybrid delivery had exposed its limitations and student satisfaction scores had reflected that for two consecutive years.

The integration layer was the part of the project I was most concerned about going in. Our system landscape is complex, several of the upstream APIs we relied on were poorly documented, and two third-party vendors had a history of unpredictable response times on integration questions. This team managed all of that. They documented what the upstream vendors could not, built resilience into the integration architecture where the upstream behaviour was unreliable, and delivered a solution that works as specified in production. I could not have asked for more.

Project summary: Several years of incremental development had left us with a platform that was technically functional but strategically limiting. A structured rebuild was the agreed path forward.

The project brief was ambitious and we had received proposals ranging from two to five times our eventual budget from other vendors. This team came back with a proposal that was commercially realistic and technically credible — and then delivered against it. That alignment between proposal and outcome is not something I take for granted. I have been on the other side of it enough times to know it requires both honesty in the sales process and discipline in delivery. We experienced both.

Project summary: Our internal product thinking was strong but our execution capability in this specific technology domain was limited. We needed depth, not generalism.

What made the most difference in practice was the quality of the engineering judgment on this team. Not the ability to execute a specification — that is a baseline expectation. The ability to recognise when a specification was suboptimal, explain why, propose an alternative, and support the client in making a decision about it. That consultative dimension elevated the output beyond what the brief described and resulted in a product that is more fit for purpose than the one we had originally specified.

Project summary: Our actuarial models had outgrown the reporting infrastructure feeding them. Data latency was introducing risk into pricing decisions that the business had decided it could no longer accept.

What made the most difference in practice was the quality of the engineering judgment on this team. Not the ability to execute a specification — that is a baseline expectation. The ability to recognise when a specification was suboptimal, explain why, propose an alternative, and support the client in making a decision about it. That consultative dimension elevated the output beyond what the brief described and resulted in a product that is more fit for purpose than the one we had originally specified.

Project summary: A previous engagement had delivered something that worked in staging and struggled in production. We approached this project with greater rigour in vendor selection as a result.

What made the most difference in practice was the quality of the engineering judgment on this team. Not the ability to execute a specification — that is a baseline expectation. The ability to recognise when a specification was suboptimal, explain why, propose an alternative, and support the client in making a decision about it. That consultative dimension elevated the output beyond what the brief described and resulted in a product that is more fit for purpose than the one we had originally specified.

Project summary: Grid modernisation funding required us to demonstrate demand-response capability. The machine learning models existed on paper; we needed an engineering partner to build and productionise them.

Our stakeholder group included board members, clinical leads, compliance officers, and end users — each with different technical literacy and different success criteria. This team navigated that stakeholder landscape as well as any vendor I have seen. They adjusted their communication register depending on the audience without losing the substance. They managed expectations honestly throughout. And they delivered a system that each group can point to as meeting their requirements. That breadth is genuinely uncommon.