Work we have delivered.
Real engagements, anonymised clients. Internal tools built around how each company actually works.
The payment team was copying account details by hand for every transaction. They now run an internal tool that fills the bank portal automatically.
The old process. The payments team was manually reading beneficiary account numbers, sort codes, and payment references from their internal system and retyping them into the bank portal one by one. With 80 to 120 payments processed daily, the team was losing four hours every day to data entry. Errors were common. One payment reached an incorrect account. The team had flagged the problem for over a year but had no way to fix it without engineering resource they did not have.
The internal tool. The company now runs a lightweight browser tool that sits alongside their existing payment system. When a payment is queued, the tool reads the beneficiary details directly from the internal software and autofills the bank portal in under three seconds. The team reviews the pre-filled details, confirms, and submits. Nothing about their core system changed. The tool wraps around what already exists.
The outcome. Payment processing time dropped from an average of four minutes per transaction to under thirty seconds. The team recovered three and a half hours per day. In the six months since deployment, error rate has been zero. The tool now handles over 2,000 payments per month and has become a non-negotiable part of how the team operates.
The finance lead was spending half a day every month producing a manual summary of company spend. They now upload a statement and get a full AI analysis in two minutes.
The old process. Every month the finance lead would download the company bank statement, copy transactions into a spreadsheet, manually categorise the spend, and produce a summary for the partners. The process took the better part of a working day. The output showed totals but no patterns, flagged nothing unusual, and gave no recommendations. Subscriptions renewed unnoticed. Supplier costs drifted upward. A duplicate invoice sat unspotted for two months.
The internal tool. The company now runs an internal AI tool that accepts a bank statement upload and returns a full analysis within two minutes. It categorises every transaction, flags anomalies, identifies recurring charges, surfaces duplicate vendors, and produces a prioritised list of recommendations. The finance lead uploads on the first of each month and has a complete briefing ready before the morning meeting.
The outcome. In the first month the tool identified £4,200 in charges the team had not actively reviewed, including three overlapping software subscriptions and a supplier invoice processed twice. The monthly finance review went from a half day of manual work to a fifteen minute read-through. The partners now receive a sharper, more actionable summary than they ever had before.
Analysts were spending up to twenty minutes manually reviewing a chart before every trade. They now run a copilot that reads the live chart and returns a signal in seconds.
The old process. Before every trade entry the team manually assessed price action, trend direction, key levels, and momentum indicators on each chart. It took fifteen to twenty minutes per instrument and introduced inconsistency across the team. Two analysts reviewing the same chart would sometimes reach opposite conclusions. On fast-moving trades the delay cost them entry quality. The team knew the process was a bottleneck but had no way to standardise it.
The internal copilot. The team now runs an AI copilot that scans the live chart on screen and returns a structured signal in under ten seconds: Long, Short, or No trade, with a concise rationale covering the key factors. The copilot does not replace analyst judgement. It gives every analyst the same consistent starting point before they make a final call. The team stays in control. The copilot removes the inconsistency.
The outcome. Average pre-trade review time dropped from eighteen minutes to under two minutes. The team reports more consistent entry quality across all four analysts and a measurable reduction in trades taken against the prevailing market structure. The copilot now processes over 300 chart reviews per week and has become a standard part of the team's pre-trade routine.
A senior trader was spending a full day every week manually reviewing strategy performance and deciding what to run. That process is now fully automated.
The old process. Every week a senior trader would manually pull performance data from multiple sources, review results across all active strategies, and make a judgement call about which to continue and which to pause. The process took most of a working day, was entirely dependent on one person, and produced inconsistent outcomes depending on that person's bandwidth and attention that week. There was no systematic framework. Decisions were made by feel.
The internal system. The company now runs an autonomous governance system that manages the full weekly cycle without manual input. It pulls performance data across all active strategies, runs comparative analysis, conducts automated research to identify optimisation opportunities, and applies a promotion and demotion framework. Winning strategies receive increased allocation automatically. Underperforming strategies are paused or deprioritised. A summary report lands every Monday morning.
The outcome. The weekly governance process went from a full day of manual work to zero human input. The senior trader now spends thirty minutes reading the Monday report instead of producing it. Strategy performance improved in the first quarter after deployment as the automated framework surfaces the highest performing setups faster and more consistently than manual review had ever managed.
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