Speed to Conviction: Osman Ghandour on Building AI Systems That Scale

In this week’s Just Curious, we sit down with Osman Ghandour, Founder & CEO of Soal Labs, a data and AI consultancy focused exclusively on private equity and private credit. Osman helps GPs unify their data, automate the heavy lifts of diligence and reporting, and build intelligence that compounds.

As he told me:

“Firms don’t need more tools. They need clarity on how work gets done.”

That clarity is paying off. As you’ll see, one private credit client increased deal capacity by 50 % and cut underwriting time by 70 %, all without adding people. Osman’s calm, structured approach shows how AI works best: when it’s embedded into standardized, auditable workflows that scale with the firm.

What to Expect:

• Why data fragmentation kills AI ROI

• A four-stage roadmap: Align → Blueprint → Deliver → Expand

• How Soal Labs helped a credit fund boost deal throughput 50 %

• The 70 % time reduction across underwriting stages

• Why “you can’t systematize what isn’t standardized”

• How to spot real AI readiness signals inside a fund

• What’s next: the rise of the “AI Investment Committee Member”

Watch the full interview with Osman Ghandour

👉 Watch here! → 

Expert Q&A: Osman Ghandour, Co-Founder @ Soal Labs

(Each week, we ask our applied AI experts a rotating set of questions to surface their frameworks, lessons, and insights. Osman’s answers follow, with Stu’s thoughts and context from our full conversation.)

Osman’s work sits at the intersection of architecture and execution, bridging the gap between data chaos and operational precision.

1. Describe who you are, what you do, who you do it for, and what makes your approach unique.

“I’m Osman Ghandour, Founder & CEO of Soal Labs. We’re a data and AI consultancy that helps private equity and private credit firms modernize how they operate. We design and implement data and AI strategies that improve deal execution, portfolio oversight, and investor communication, combining the right mix of custom development and best-in-class tools.

Our niche focus is what sets us apart: we work exclusively with GPs across fundraising, origination, due diligence, and portfolio management. Because we understand how deal, ops, and reporting teams actually work, we move fast and build systems that fit seamlessly into how firms operate day to day.”

Stu’s Thoughts:

Soal’s advantage is precision through context. Osman’s background in logistics and data architecture gives him a builder’s eye for process. 

In the interview, he described how his team bridges “idea to execution,” a recurring failure point for many firms. “Our clients don’t need more dashboards,” he said. “They need a single source of truth.” That grounding lets Soal deliver results measured in throughput, not theory.

What stood out in Osman’s story is how industrial discipline meets financial decision-making. He built Soal Labs after years optimizing logistics systems for UPS and Amazon, places where a one-second delay matters. That mindset shows up in how he designs AI for private markets.

“Private equity is a decision-making business. The speed at which you can make those decisions—and how you use data to inform them—is extremely important.”

By grounding AI in measurable velocity, and not theory,  he’s built a consultancy that helps firms move from idea to execution. The same logic that made supply chains efficient is now helping investment teams process 50% more deals with the same headcount.

2. What problem are you most focused on solving right now, and how are you anticipating solving this problem with AI?

“A major challenge we’re solving is helping firms that have seen early AI success scale it into something durable. Many funds recognize AI’s potential, but their data still lives in disconnected systems — Excel, CRMs, data rooms, and portfolio reports that don’t talk to each other.

We build the foundation: unifying data across the investment lifecycle and structuring it so it’s consistent, queryable, and auditable. That’s the inflection point where AI can move from proof-of-concept to production.

Once that’s in place, AI can automate reporting, surface insights, and reason across the firm’s collective knowledge.

Stu’s Thoughts:

Osman’s four-stage model—Align, Blueprint, Deliver, Expand—anchors every engagement. He told me, “If you skip alignment, you just buy software.” The method mirrors how PE firms think: diligence, plan, execute, scale. His teams typically reach measurable outcomes within a quarter, a cadence that matches fund reporting cycles and keeps adoption momentum high.

In practice, Alignment means finding the internal owner, often a CTO or head of ops, and mapping blockers before a single line of code. 

Blueprint follows within 1–2 months, setting a six- to twelve-month roadmap with clear “build vs. buy” decisions. 

Delivery takes another 2–3 months and produces a live system that improves measurable outcomes, faster reporting, greater deal capacity, cleaner fund data. 

Then comes Expand: replicate that success across adjacent workflows.

“That’s when you show results: capacity up, reporting faster, LP reach wider.” Most clients see results inside one reporting cycle, a cadence that keeps adoption momentum high and ties AI impact directly to fund performance.

3. Where have you seen AI drive the fastest ROI?

“Quick wins come from automating data extraction—turning unstructured information into usable data. For most GPs, that means parsing CIMs and financials during screening. AI can pull key metrics and populate models or memos automatically. What took analysts days now happens in minutes.

Another fast ROI area is fundraising support: automating DDQ responses by pulling accurate answers from fund docs and historical data. It saves hours and reduces errors.”

Stu’s Thoughts:

His private credit case study speaks to this.

The fund’s entire deal flow lived in Excel and SharePoint wtih no bridge between origination, underwriting, and portfolio systems. Soal Labs built a custom underwriting platform that connected directly to iLEVEL and Dynamics, creating a single source of truth.

Then came the automations:

• Document extraction: AI pulled key metrics from CIMs and financials automatically.

• Deal memos and one-pagers: generated from that data instead of built manually in PowerPoint.

• Portfolio sync: data flowed cleanly into the monitoring tool without re-keying or copy-pasting.

Through their work, stage-to-stage turnaround time dropped 70 %, and the team executed 50 % more deals, without adding headcount. 

“Speed to conviction,” Osman said, “is the metric that matters.”

For operators, that’s tangible leverage: higher throughput, fewer errors, and a scalable process that compounds over time.

4. What’s the most common mistake companies make when they try to “do AI”?

“Most start by picking a platform instead of defining a process. They get excited about tools before clarifying what decision or workflow they’re improving. The result is scattered pilots and low adoption.

The firms that get it right map and standardize how work gets done first. You can’t systematize what isn’t standardized. That alignment makes automation effective and lasting.”

Stu’s Thoughts:

“You can’t systematize what isn’t standardized.”

Osman told me how Soal refused to automate one fund’s diligence process until MDs agreed on a unified workflow. That extra alignment cycle unlocked adoption later.

He described a clear pattern: before writing a line of code, every function lead sits down to define how the firm actually runs deals. “Different MDs had different ways of running their processes,” he said. “We needed them to agree on one unified way—or at least on which exceptions mattered.” That dialogue isn’t bureaucracy; it’s architecture.

Once the process is standardized, Soal Labs co-designs the system with users, mocking up interfaces, walking through real cases, and validating each step until the workflow feels natural. Only then does automation follow. For Osman, that’s the real differentiator: AI that reinforces the firm’s muscle memory rather than fighting against it.

5. What’s an AI capability that’s just around the corner that businesses should prepare for now?

“We’re entering an era where AI can become a real participant in investment decision-making — effectively, a member of the Investment Committee. Systems can now synthesize diligence materials, compare deals against historical patterns, and flag risks before a human ever opens the memo.

To get there, firms need the right foundation: unified data across sourcing, diligence, and portfolio monitoring, and a culture that documents how decisions are made.”

Stu’s Thoughts:

Osman calls this next phase “the AI IC Member.” In his words, “AI will soon have a seat in the room—not to decide, but to inform.”

He sees it as the natural evolution of the systems he’s already building: when every deal, outcome, and IC rationale is captured in a unified, queryable format, AI can start surfacing real patterns, comparing new opportunities against the firm’s own historical performance. “You can’t get that level of intelligence,” he said, “until your data reflects how you actually make decisions.”

The operators who win will treat this as augmentation, not replacement. Their edge comes from codifying judgment, turning intuition into data the model can learn from. Osman’s quiet warning: the firms that document today’s reasoning will own tomorrow’s pattern recognition. Those who don’t will be re-teaching their systems from scratch.

Key takeaways

1. Map before you buy. Audit every workflow across fundraising, origination, diligence, and reporting; standardize before selecting tools.

2. Build the data spine. Connect CRMs, data rooms, and portfolio systems into one auditable source of truth.

3. Pilot for proof, then expand. Use Osman’s Align → Blueprint → Deliver → Expand loop to capture results within one reporting cycle and replicate success.

Connecting with Osman

A couple of options:

• Respond to this newsletter, and we’ll help facilitate (it’s free)

• Check out Osman’s Just Curious profile and connect with him there (also, free)

• Explore Soal Labs: soallabs.com

• Watch our interview → 

Lastly, what stands out in Osman’s work is how deliberate it feels. Every project compounds, each automation feeding the next. He treats AI not as a feature but as firm infrastructure, a foundation that scales deal flow, not just dashboards. The result is leverage that multiplies over time.