An Omni Demo: AI-Native BI For Private Equity Portfolio Analytics

Private equity firms have invested in portfolio monitoring tools like Allvue, iLevel, and Dynamo, but those platforms were built as data stores and calculation engines, not for reporting, exploration, or AI. The workaround is familiar: pull data into Excel, clean it, build dashboards manually, and field every ad-hoc request by hand. That workflow is slow, fragile, and impossible to self-serve. In this article, we walk through how we built an AI-powered portfolio monitoring dashboard in Omni, using natural language querying, a governed semantic layer, spreadsheet-style calculations, and automated financial reporting to replace the Excel-and-email loop that most PE teams still run today.

The problem: portfolio monitoring is still manual and error-prone

Tracking portfolio company performance across funds is fragmented, and understanding why performance changes is slow and manual.

Most PE teams still run on the same stack: iLevel, Chronograph or Allvue as the system of record, Excel for transformation and analysis, and PowerPoint or PDF for quarterly reporting. The workflow has not changed in a decade: pull data, rebuild models, analyze, present. Repeat next quarter.

This creates real costs. Board reporting takes days instead of hours. Insights arrive weeks after the data does. Portfolio and analytics teams spend most of their time assembling numbers, fixing excel calculations and polishing formatting rather than interpreting numbers. And every ad-hoc question from an investment committee member triggers another manual pull.

Manual processes also create more opportunities for error. Without standardized definitions, different team members can produce different numbers for the same metric. Spreadsheet references are brittle, and formulas can break with even small changes in upstream data. Debugging becomes a mess when you have 25 intermediate spreadsheets and complex Excel transformations. Finally, merging and reconciling that data with other sources (CRM, fund admin, 3rd party market data, etc.) can be painful.

The shift: A data warehouse, semantic models, BI and AI

Recognizing the above problem, many fund CTOs have implemented (or at least considered) a centralized data warehouse to break the silos. Data flows from different systems, gets transformed in the warehouse and then gets served through BI or exports.

Yet, even the more technically mature who have done it, still struggle at the serving layer.

• Portfolio monitoring still takes too long. Recurring critical reports still live in brittle and chaotic Excel workbooks that need to be manually exported and tuned every quarter.
• Second, definitions drift between teams and across portfolio companies- especially for ad-hoc requests. What "Revenue" includes for Company A is different than Company B.
• Third, as the pressure to deploy AI on top of internal data has been mounting - very few seem to have figured it out.

To solve the data serving problem, you need faster and smarter access to what you already have.

The data structure PE firms use is well established: funds, portfolios, companies, with IRR, MOIC, NAV, cost basis, fair value, and cash flows tracked at every level. That structure does not need to change. What changes is what sits on top of it.

With AI-native BI, the same iLevel-style data supports natural language queries ("which sectors drove NAV growth this quarter?"), excel-like interface on top of live data, AI-generated performance summaries, and interactive exploration, all governed through a semantic layer that ensures everyone is working from the same definitions. The data stays where it is. The access layer gets dramatically better. That’s what makes Omni the perfect fit for financial services.

This is a working demo built on synthetic data that mirrors a real iLevel ETL, designed to show what AI-native BI looks like on top of portfolio monitoring data.

At Soal Labs, we build data engineering, analytics, and AI solutions for private equity, credit, and fund operations teams.

We are an Omni implementation partner, which means we help firms deploy Omni as the analytics and AI layer on top of their existing data infrastructure. We are an independent  partner and receive no commissions, referral fees, or financial compensation from Omni. Our recommendations are based purely on what we have seen work for our clients.

The goal of this piece is to show, concretely, what becomes possible when you layer AI-native BI on top of that data: interactive portfolio monitoring, automated reporting, natural language exploration, and drill-down from portfolio to fund to company, all governed through a single semantic model.

Here's what that looks like across five core workflows.

1. AI Summaries: the "so what" layer

Use Case: AI doesn't just answer questions. It proactively explains portfolio performance and flags what matters.

Omni’s AI Summary Visualization  is powerful.

It is a native visualization type, like bar charts, that generates insights directly from a prompt and the data. It stays current as the data refreshes, and produces fully traceable insights linking directly to the semantic layer.

It runs across every level of the dashboard: portfolio, fund, and companies. The goal of having it upfront is to give a plain-text, bird-eye view summary of how the business is doing.

At the platform level, you get an overall performance summary: total NAV, cost basis, average MOIC, top and bottom performers, and the funds driving each. A sector concentration analysis breaks down where exposure is heaviest and which sectors are delivering relative to that exposure. Top performers are called out by NAV and MOIC. Bottom performers and write-down risks are flagged with the specific funds and sectors they span.

Omni’s AI is grounded in the semantic layer, meaning every generated insight uses the same definitions as dashboards, reports, and calculations. This ensures outputs are consistent, auditable, and directly tied to underlying metrics, which is critical in financial workflows where accuracy matters.

The practical impact: instead of manually drafting a “Weekly Performance Email” or spending half a day clicking into every portfolio company to flag anomalies, you get ready-made summaries.

You can also ask a question in natural language through Blobby, or let the AI surface what it finds notable. Either way, the insight is grounded in the data and is auditable.

Example questions across levels:

• What drove NAV growth this quarter? (portfolio)
• Summarize this fund for LP reporting. (fund)
• Why did EBITDA margin contract last quarter? (company)

2. Portfolio monitoring: what's happening across the fund?

Use Case: Get a real-time view of fund performance and instantly identify which investments are driving returns or dragging them down.

You start with a portfolio-level snapshot: total NAV, aggregate IRR and MOIC, and portfolio composition broken out by strategy and geography. This is standard iLevel-style aggregation, but now it's interactive. That interactivity comes from Omni’s semantic layer and AI interface. Instead of pre-building every cut of the data, users can dynamically slice metrics like NAV, IRR, and MOIC while Blobby handles query generation behind the scenes through Omni’s AI query interface →. You can filter by sector, vintage year, or fund without rebuilding anything.

Omni also introduces a workbook layer →, where users can build and modify analyses directly on top of governed data. This enables true self-service analytics: business users can create reports, test hypotheses, and iterate without relying on data teams for every request.

Then you move into performance. A NAV bridge chart shows value creation over time, breaking out contributions, distributions, and valuation changes. A scatter plot maps return distribution across deals, sized by investment cost basis and colored by sector, so outliers are immediately visible.

From the same view, you can identify top and bottom performers without switching tools. Top performers are ranked by NAV and MOIC. Bottom performers, those with MOIC below 1.0x, are flagged as potential write-downs along with the funds and sectors they belong to. A covenant headroom chart shows which sectors are operating close to their thresholds, giving deal leads and operating partners an early warning before a breach shows up in the quarterly report.

Instead of exporting data to answer follow-up questions, you ask them directly: What drove NAV growth this quarter? Which sectors are underperforming relative to plan? Where is risk concentrating?

This is the view a CIO or portfolio manager needs at the start of any IC meeting or portfolio review: the full picture, filterable, explorable, and current.

3. Fund performance and LP reporting

Use Case: Automate LP and board reporting using the same fund metrics you already track, with AI-generated summaries that cut prep time from days to hours.

At the fund level, the dashboard surfaces standard PE reporting fields: committed capital, called capital, distributions, and net MOIC. Then you break it down by company in a holdings table showing cost basis, current fair value, realized and unrealized value, weight, and MOIC for each position. Instead of Excel rollups rebuilt every quarter, you get a live, explorable view that updates as data flows in.

Cash flow and performance trends are built in. A cash flow timeline tracks contributions against distributions over time, while an IRR chart shows fund-level returns by vintage year and by quarter. These are the exact views IR teams need for LP quarterly letters and fund controllers need for board decks.

This is where Omni’s AI layer becomes practical for IR and FP&A teams. Instead of manually compiling quarterly narratives, you can ask: Which companies are driving returns this quarter? What changed vs. last quarter? Summarize this fund for LP reporting.

Because these outputs are generated on top of the semantic model (docs.omni.co), they stay consistent with reported metrics and can be traced back to underlying data—something that’s typically missing from standalone AI tools.

Omni also supports embedded analytics, controlled sharing, and granular access controls, making it possible to deliver tailored reporting experiences to LPs, operating partners, and internal teams without exporting data or duplicating dashboards.

With row-level security and user-specific permissions, firms can control exactly what each stakeholder sees while still working from the same underlying data model. This ensures that every view—whether internal or external—remains fully connected to live data and governed definitions, without creating fragmented versions of the truth.

Omni calculations → | Spreadsheet-style modeling →

4. Portfolio company diagnostics

Use Case: Understand why a company is performing the way it is by connecting financial results, valuation trajectory, and operating metrics in one view.

This is the section that matters most to operating partners and deal leads. At the company level, you get three layers of detail in a single connected workspace.

Financial performance. Start with the company snapshot: revenue, customer count, and NPS as headline metrics with period-over-period comparisons. Then move into core financials: revenue, EBITDA, and net income over time, alongside EBITDA margin trends that show whether profitability is expanding or contracting.

Valuation and investment performance. At the individual investment level, you compare entry enterprise value against current enterprise value, track fair value trends over time, and see how value creation accumulates quarter by quarter. A premium/discount chart shows whether the current valuation sits above or below cost basis, and by how much. This is the analysis that informs three decisions that come up in every IC meeting: hold, follow on, or prepare for exit.

Operating metrics. Cash flow from operations vs. CapEx shows whether the company is self-funding growth or burning through capital. Budget vs. actual comparisons surface execution gaps before they compound. Employee headcount and headcount as a percentage of total sit alongside revenue and margin data, so you can immediately see whether growth is being fueled by hiring or by efficiency gains.

In most PE reporting setups, these three views live in different spreadsheets or different systems. Here, they're unified. An operating partner heading into a board meeting can pull up one screen and see the full story: financial trajectory, valuation status, and operational health, all for the same company, all from the same governed data.

AI adds another layer. You can ask: Is revenue growth driven by headcount increases or by operational improvements? Are margins expanding because of pricing power or cost cuts? These are questions that typically require an analyst to pull data from HR systems, financial models, and operational reports.

In Omni, they're answered from a single dataset. Blobby (Omni’s AI) makes it easy to build, navigate, explore and answer all those questions as it sits directly on top of the semantic layer.

5. Drill-down: portfolio to fund to company in one click

Use Case: Go from high-level portfolio insights to company-level drivers instantly, without exports or rebuilding.

This is a core differentiator in Omni, the ability to do cross-dashboard drill-downs and deep-linking.

In a traditional PE analytics setup, each level of the hierarchy lives in a different report or spreadsheet: one for portfolio aggregation, another for fund performance, another for company-level detail. Moving between them means switching files, re-filtering, and hoping the numbers reconcile.

Here, the entire path is connected. Start at the portfolio view, see an outlier, click into the fund driving it, and drill down to the company-level financials and operating metrics. All in one session, all from the same underlying data model. No exports. No re-querying. No version-control problems.

This is the shift:

Dashboards show what happened.AI explains why it happened.Drill-down lets you verify it instantly.

In practice, this means an IC member can ask a question about a specific company, and the person presenting can navigate from the portfolio overview to the company-level answer in seconds. No "let me pull that up after the meeting." No follow-up email with an Excel attachment three days later. The answer is right there.

These drill-down paths are defined once in Omni’s semantic model, so navigation across portfolio, fund, and company levels stays consistent across dashboards, queries, and AI-generated insights. There’s no duplication of logic or risk of mismatched numbers across reports.

Why this works: it's built on Portfolio Monitoring System data

Short answer: Same data you already have. Dramatically better access.

Everything in this article maps directly to data structures PE firms already maintain: iLevel exports, portfolio monitoring spreadsheets, and quarterly reporting templates. The fund hierarchy, the performance metrics, the cash flow data, it's all the same.

That means no data migration, no retraining, and no workflow overhaul. You connect the data you already collect into Omni's semantic layer, define the business logic once, and every dashboard, AI query, and report draws from that single governed source.

What this enables (real workflows)

This directly supports the workflows PE teams run every day: portfolio monitoring, financial reporting automation, private equity analytics, board reporting, and FP&A across portfolio companies.

What makes this different

Most portfolio monitoring and BI tools stop at dashboards. This combines four capabilities in one platform:

AI-powered querying and insights that understand PE-specific context. Spreadsheet-style modeling for ad-hoc analysis without leaving the BI layer. Custom calculations like IRR, MOIC, and NAV bridges built directly into the semantic model. And a unified data layer that governs definitions across every dashboard, query, and export.

FAQ

What is private equity portfolio monitoring?

Portfolio monitoring is the process of tracking financial and operational performance across a fund's portfolio companies. It covers returns (IRR, MOIC, NAV), risk indicators, cash flows, and growth drivers, typically reported quarterly to investment committees and LPs.

How does AI help with financial reporting automation?

AI generates performance summaries, answers natural language questions about fund and company metrics, flags outliers and risks, and reduces the manual assembly work that goes into quarterly LP reports and board decks. It cuts reporting prep time from days to hours.

Do I need to change my data model?

No. This approach works directly on top of existing and widely adopted data structures. If you already track fund performance, cash flows, and company KPIs in a portfolio monitoring tool or in spreadsheets, you can connect that data without migrating or restructuring.

What's the biggest benefit?

Speed. The shift from manual reporting to automated insights, and from static dashboards to interactive, AI-powered workflows, means PE teams can answer portfolio questions in minutes instead of days.

Can I use this with data from systems other than iLevel?

Yes. The schema used in this demo mirrors iLevel's ETL output, but the same approach works with exports from Allvue, Chronograph, Dynamo, or any portfolio monitoring system that tracks the standard fund/portfolio/company hierarchy with performance metrics and cash flows.

Who is this built for?

This serves multiple personas across a PE firm: CIOs and portfolio managers for the portfolio-level view, IR teams and fund controllers for LP reporting and board decks, operating partners for company-level diagnostics, and deal leads for valuation tracking and exit analysis.

Final takeaway

You don't need more dashboards. You need faster answers, less manual work, and better visibility into what's actually happening across your portfolio.

AI turns standard private equity data into real-time portfolio monitoring and financial reporting automation, without changing your data, your tools, or your workflow. It just makes all of them work better together.

At Soal Labs, we build data engineering, analytics, and AI solutions for private equity, credit, and fund operations teams. We are an Omni implementation partner. If you want to see what AI-native analytics looks like for your fund, reach out.

Learn more about Omni's AI capabilities, financial services solutions, and data modeling approach.

‍