First Focus is putting $100,000 behind five client ideas that are designed to deliver real commercial returns from AI.
We’re backing projects that move beyond shiny demos and vague productivity promises, and instead use AI to solve real business problems that actually move the needle.
The rule is simple: if an idea doesn’t shift a number that matters, it doesn’t get funded.
Each successful submission receives $20,000 in support, including dedicated engineering time, a project manager, and hands-on consultancy to take the idea from concept to production. And we don’t stop at “we built it.” These projects are tracked over time to make sure they’re adopted, embedded, and delivering the returns they were meant to.
Lipman Burgon & Partners (LBP) is a private wealth boutique advisory firm serving ultra-high-net-worth clients, family offices, charities, and endowments. Their investment team’s rigour is a defining strength, built on one core belief: great capital allocation starts with thorough due diligence.
Their analysts work through large volumes of fund manager documents, performance data, and governance material to assess risk, opportunity, and alignment with client mandates. The quality of that analysis is a key differentiator for LBP, but it’s also one of the most time-intensive parts of the investment process.
Even a high-level screening of a single investment opportunity can take an analyst one to two full days. A full due diligence cycle can stretch anywhere from two to four weeks, with the document set often spanning several hundred pages.
LBP proposed building an internal AI assistant to remove the heavy manual lift from investment due diligence.
The idea is a secure, Azure-based assistant that ingests due diligence documents, tags them against LBP’s existing evaluation framework, and produces structured outputs. That includes draft summaries, key risk flags, and side-by-side fund manager comparisons. Every output is fully cited back to the source material, so analysts maintain confidence, transparency, and control.
Rather than forcing AI into areas where tone, nuance, and originality are critical (like marketing or client communications), LBP deliberately focused on a use case where AI’s strengths are most effective: factual ingestion, structured analysis, and summarisation at scale.
This isn’t about replacing analysts. It’s about stripping out low-value, repetitive work so the team can spend more time on judgement, decision-making, and better client outcomes.
Today, due diligence at LBP is slow, manual, and a real constraint on analyst throughput. Automating the first pass of document analysis is expected to deliver a 60 to 80 per cent time saving per analyst.
Based on LBP’s volumes, that equates to more than $200,000 in annual benefit from an upfront build cost of approximately $30,000 and an ongoing run cost of around $1,500 per year. That’s a projected 6 to 7x return in year one alone.
This was a clear example of AI delivering measurable P&L impact, without introducing unnecessary operational or compliance risk.
The solution will integrate directly into LBP’s existing SharePoint and Teams environment, so document intake and review happen inside familiar workflows.
Documents will be tagged using a structured schema aligned to LBP’s current due diligence framework, including team, process, performance, ESG, governance, and fees. A pilot will be delivered within 8 to 10 weeks, with accuracy and time savings validated before scaling to full production.
Key design principles built into the project from day one:
Success will be measured through analyst time saved, increased throughput, and improved consistency of outputs. The ultimate test is simple: can LBP handle more due diligence work, without adding headcount, while maintaining or improving quality?
We’ll be sharing updates as this rolls out, not just the end result, but the thinking, trade-offs, and lessons learned along the way. That includes short video interviews with the people actually using and building the solution.
The goal isn’t to showcase AI for the sake of it. It’s to show what happens when you apply AI to a real business constraint, tie it directly to ROI, and make it easier for your team to do their best work.
If you’re looking at AI and wondering where to start, this is the kind of use case worth paying attention to. Practical, measurable, and grounded in outcomes that actually move the business forward.
AI projects don’t finish when the initial build is delivered. The real returns come from ongoing support, iteration, and ownership as the model matures, data grows, and business needs shift.
That’s exactly the problem CORE was built to solve. CORE is our managed AI and automation service, designed to help organisations turn projects like this into long-term productivity gains.
With CORE, we help clients operate and improve AI systems safely in production, lift accuracy and adoption over time, adapt workflows as teams and priorities change, and maintain the governance and security that keeps everything running responsibly.
If you’re investing in AI to deliver real, measurable outcomes, CORE provides the structure and continuity to make that investment compound, month after month.
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