Recent work.
A selection of recent engagements across UK energy, retail and financial services.
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Energy
Predicting energy leaks
A FTSE 100 energy network operator had data on environmental leaks across the grid but no team to make sense of it. We hired a six-person squad in eight weeks, reframed the brief from reaction to prevention, and built a predictive model that secured £1.75m in Net Zero funding.
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Retail
Fixing retail data
A FTSE 100 grocery retailer was heading into an audit re-test on inconsistent own-brand food data. We pushed back on patching the reports and rebuilt the layer underneath: warehouse migration to Snowflake, data contracts across upstream feeds, and a catalogue any team could use. Audit scoring went from around 60% to over 90%.
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Financial Services
Mapping enterprise data
A Tier 1 finance group needs a clear picture of its enterprise data, its governance, and a plan to integrate third-party vendors into the data layer. Started as advisory; now executing. Customer onboarding time has dropped from weeks to days, with automated, enriched onboarding data in place across hundreds of KYC and KYB data points.
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More to come
We're working on writing more case studies up. Check back, or drop us a line if you want to know more about the engagements we've run.
Energy
Predicting leaks before they happened.
The problem.
The brief was uncomfortable. Leaks of SF6 (a potent insulating gas used in high-voltage switchgear) were happening across the network, and nobody had a clear picture of where, how often, or why. There was data: IoT sensors, drone imagery, video from on-site inspections, paper logs. There was no team to make sense of it.
What we did.
We built one.
In eight weeks, we hired a six-person discovery squad: a data architect, a data scientist, and four engineers, mostly on contract. We mapped the data landscape, worked with environmental and engineering specialists to turn their tacit knowledge into features the model could learn from, and consolidated the patchwork of sources into one usable dataset.
The hardest call came early. The senior sponsor wanted us to skip discovery and "just build the model". We held the line. A predictive model trained on data nobody trusted would have been worse than no model at all. We did the discovery work first. The modelling came second.
We also reframed the brief. The original ask was "find the leaks". We pushed it to "predict the leaks", because the funding case for prevention was stronger than the funding case for reaction.
The outcome.
Outcome: £1.75 million of new funding secured for the Net Zero initiative, on the back of the data and the model. The contract was taken in-house so the operator could continue to develop it. As a side benefit, we worked with on-site engineers on AR-enabled access to digitised station infrastructure using Meta Quest hardware, cutting the time to find inspection records in the field.
What this shows: building a data product from a vague brief. Hiring the team, defining the data model, navigating a senior stakeholder, delivering something that paid for itself. This is what a Lead engagement looks like at scale.
Retail
Fixing the data, then passing the re-audit.
The problem.
An external audit had flagged the retailer's own-brand food data (allergens, ingredients, supplier records) as inconsistent and unreliable. The re-audit was already scheduled. The data sat across legacy spreadsheets, ageing internal tools, and an on-prem Oracle warehouse that nobody wanted to touch. Patching the audit findings line by line would have shifted the same problem to the following year.
What we did.
We argued for fixing the layer underneath, not the reports on top of it. The sponsor backed it.
The work split three ways. We migrated the warehouse from on-prem Oracle to AWS and Snowflake, so the team could query data without queueing for batch jobs. We introduced data contracts, so every upstream feed had clear ownership and quality expectations. And we built a metadata catalogue, so any team could find the data they needed without asking an engineer.
We coached the squad through the migration in parallel. Several were new to Snowflake, and a few of the data contracts ran across team boundaries that had been protected for a long time. We worked through both.
The outcome.
Outcome: the re-audit passed. Audit scoring went from around 60% to over 90%. Manual reporting time fell. Data accessibility went up across the business. The contract ended on its natural expiry.
What this shows: governance and migration work where the deadline is real and the problem is plumbing, not personality. This is the kind of engagement a Lead retainer exists to take on.
Financial Services
Mapping enterprise data, and a path through governance.
The problem.
A senior director needs two things. First: a clear picture of the enterprise data landscape and the governance surrounding it (what data exists, where it lives, who owns it, and what rules apply to it). Second: a plan to integrate third-party vendor data into the enterprise data layer, populate downstream systems with it, and enrich what is already there.
What we did.
This started as advisory. We sit with data owners across the business, walk the third-party contracts, and test the assumptions about what can and cannot be ingested under the firm's governance rules. Some of those assumptions do not hold up. The plan names that, with alternatives.
The plan is now being implemented.
The outcome.
Outcome: customer onboarding time has dropped from weeks to days. Automated, enriched onboarding data is in place across hundreds of KYC and KYB data points.
What this shows: advisory work that takes a vague-but-important brief, makes it specific, and makes it actionable, and stays in long enough to see the plan land. This is the shape of a Lead engagement when the brief is "help us think about this clearly", and then "help us actually do it".
More to come.
We're working on writing more case studies up. If you want to know about engagements we've run, drop us a line.
Some of the work shown was led by our founder under prior consulting contracts, before Reignites Ltd was founded in 2026. The approach and the standard remain the same.