Work
200+ companies · United States · United Kingdom
Selected impact
Showing 8 of 8 case studies.
A fast-growing data and AI services organisation was expanding its customer-facing solutioning capability across regions and industries.
Growth was outpacing shared process. Quality depended too heavily on individual experience, slowing collaboration across sales, delivery and engineering.
Helped build and scale the Solutions Engineering organisation while staying close to discovery, architecture, proposals, executive demos and live deals.
Enterprise opportunities involved technical, commercial and executive stakeholders with different definitions of value and risk.
The solution had to be technically credible while remaining anchored to the business decision—not a feature tour.
Led or supported discovery, solution architecture, POCs, RFP/RFQ responses, estimation, demonstrations and implementation roadmaps.
Complex data engagements required granular effort, resource and cost estimates before proposals could move forward.
The manual workflow took approximately 1.5–2 weeks and created avoidable back-and-forth between customer-facing and delivery teams.
Designed the estimation logic, translated it into a guided application and aligned the output to proposal and review workflows.
Two desks at a Nordic retail and corporate bank. Markets and Treasury, where transactions are processed and settled. Lending, where loans are originated and serviced. Both are systems the business does not get to pause: a stoppage is not a degraded experience, it is transactions that did not clear and loans that did not fund.
The work had to hold two things at once that usually pull against each other. The platforms had to run, which rewards changing nothing. And the departments needed new capability shipped into them, which means changing something inside the blast radius of a system nobody is allowed to break. Then a third: leadership wanted a view on moving the whole thing to AWS, which required understanding the estate well enough to say honestly what could move, what could not, and in what order.
Application development analyst on the account. Ran support and incident response across both desks, built and shipped new features into the Markets and Treasury department, and produced the assessment and wave planning for the AWS migration.
A US insurer starting a data lake platform, with standing delivery teams to build it, report on it and keep it running.
Turn a first platform project into a long-running engagement. Every expansion, renewal and change of scope needed its own estimate and proposal, and each one had to hold up commercially as the work moved from building the platform to running it.
Solutions engineer behind the first deals: scoping, effort and cost estimation, and the proposals for each expansion and renewal that followed. Delivery was by the firm's engineering teams.
A global enterprise software company with an analytics estate on Amazon Redshift that it wanted to migrate.
Win a small first project on its merits, then earn every extension: each one had to be scoped and estimated as the engagement grew from a migration into a standing team.
Solutions engineer on the first deal, co-led with a fellow SE, and a key part of scoping and estimating the extensions that followed. Delivery was by the firm's engineering teams.
A national wine and spirits distributor whose sales forecasts drive how it plans for seasonal demand and shifting consumer preferences.
The forecasting pipeline ran pre-processing, training and scoring in Pandas on large warehouses. It was compute-heavy and slow, and it was getting more expensive as the data grew.
Solutions engineer on the account: brought it in and scoped the extensions and expansions that followed. The forecasting work was delivered by the firm's data science team.
A consumer brands company whose Snowflake account had grown without a warehousing strategy: no standard naming, no data model, no documentation of the architecture, and access controls short of best practice.
Queries ran over full history instead of incremental data, which made them heavy, slow and costly. Before anything could be fixed, someone had to establish what was there and what it would take to put right.
Solutions engineer: scoped and sold the paid discovery, then the two extensions it led to. The platform work was delivered by the firm's engineering team.
A significant amount of my work sits under NDA and will never appear on a public website — banking modernisation programmes, competitive enterprise evaluations and internal tooling among them. If a specific sector or problem shape matters to you, ask me directly and I will share what I am able to.
Expertise
I run structured discovery with the people who own the numbers, map the current data and decision landscape, and rank opportunities by value, feasibility and time-to-proof. The output is a sequenced roadmap with named owners, not a slideware vision.
I design target architectures on Snowflake, AWS and Azure using lakehouse and medallion patterns, define modelling and dbt transformation standards, and set governance, cost and reliability guardrails before the first pipeline is written.
I build working RAG, embedding and agentic prototypes against your real content — then design the evaluation harness, retrieval strategy, guardrails and human-in-the-loop steps that decide whether the thing survives contact with production.
I scope POCs around a single decision, define success criteria with the sponsor before we start, build the narrative and the environment together, and rehearse the executive walkthrough so the technical story and the commercial story land as one.
I build the operating system for the function: discovery frameworks, qualification criteria, estimation models, proposal and architecture templates, reusable demo assets, and the coaching rhythm that makes them stick.
I embed part-time as your solutions leader: pairing with sales on live enterprise opportunities, shaping the technical narrative, running executive conversations, and hiring and coaching the team that eventually replaces me.
How I work
Typically 1–2 weeks
Understand the decision the business is trying to make faster, who owns it, and what a good outcome is worth.
1 week
Convert the opportunity into a value hypothesis with a range, a sponsor and a measurable success criterion.
2–4 weeks
Give stakeholders something they can react to before the budget conversation, not after it.
1–2 weeks
Test the hypothesis against the criteria agreed in step two, and put the result in front of the people who sign.
Ongoing or advisory
Hand over something the organisation can run and extend without me in the room.
Bars are to scale. The lighter extension is the gap between the shortest and longest a step takes; Scale is open-ended, so it is dashed rather than measured.
Engagement models
A standing few days a month for architecture review, roadmap decisions and second opinions on vendor and platform choices.
A defined piece of work with an agreed outcome — a strategy, a target architecture, a prototype, or a proof of concept run to conclusion.
Embedded part-time as your solutions leader: live enterprise deals, technical narrative, hiring and coaching the permanent team.
A worked example
Your real content, chunked and embedded — not a sample set.
Vector store, reranking and the prompt contract around it.
A scored baseline, so “better” becomes a number.
Human-in-the-loop, security and governance sign-off.
Per-interaction economics at production volume.
Technology landscape
Thirty minutes is usually enough to work out whether there is something worth doing here, and whether I am the right person to do it.
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