Our client is a premier consultancy that partners with private equity (PE) firms to strengthen and scale portfolio companies. They specialize in the critical fundamentals of business growth: finance, accounting operations, and capital strategy. By moving rapidly to repair broken processes and implement scalable frameworks, they ensure that newly acquired companies are built for long-term resilience and investor transparency.

In the world of private equity, speed-to-insight is everything. One of the client's most important offerings is the monthly management report model, or MMR, used to bring newly merged or acquired companies into a PE firm’s reporting structure and give investors clear, unified visibility.

Building that report is where things slow down, as there's a heavy reliance on human expertise. To bring a newly merged company into a PE firm’s reporting structure, analysts had to manually transform raw trial balances into consolidated income statements, balance sheets, and cash flow reports (three-statement reporting). This process was:

• Slow: The process took approximately three weeks per client.
• Inconsistent: Methods lived in individual analysts' heads rather than a shared knowledge base.
• Unscalable: Growth was limited by headcount; adding more clients required hiring more expensive expert labor.
• Manual: A single trial balance mapping and statement generation exercise typically consumed five full business days.

We worked closely with the client to build an automated MMR platform that fits directly into how their teams already work. The goal was simple: take the most time-consuming, repetitive parts of MMR creation off analysts’ plates without sacrificing accuracy.

Rather than a generic AI tool, our team engineered a tailored intelligence solution that automates the core model foundation: account mapping and three-statement reporting. This addresses roughly 75% of the manual effort in a typical MMR build. What used to take weeks now starts with a consistent, repeatable baseline.

The solution is built around four technical pillars:

• 5-layer intelligent classification: To ensure financial-grade accuracy, Turing implemented a priority pipeline. The system first searches existing firm knowledge and "gold standard" mappings. Only when a match isn't found does it escalate to advanced semantic matching and LLM-based inference. This "fact-first" approach maximizes accuracy while controlling costs.
• Human in the loop accuracy and feedback-based learning: Every AI-generated mapping is accompanied by a confidence score and clear AI reasoning. This allows analysts to act as "editors" rather than "authors," reviewing only the 20% of accounts flagged as complex or low-confidence that require additional context.
• Adaptive intelligence: The platform features a pre-built vector index for instant semantic search. Every manual adjustment made by an analyst is fed back into the system, continuously refining a proprietary, industry-specific database that grows more intelligent with every engagement. Every analyst update makes the system smarter.
• Built-in validation and conflict detection: A multi-step validation process checks for inconsistencies and flags issues before they become problems. Analysts know exactly where to focus their attention instead of hunting for errors.

This work tackled one of the hardest areas to automate in consulting: building financial statement models for monthly management reports. The process is usually slow, inconsistent, and highly manual, which makes it difficult to scale. By combining ML and GenAI in a practical, controlled way, we were able to automate the core of this workflow without sacrificing accuracy.

Key results:

Model creation time reduced by more than 50%.

Trial balance mapping and financial statement build time reduced from 5 days to 3 hours through:

• Automated parsing with AI-assisted column detection 
• Batch processing of 1000+ accounts in <5 seconds
• Parallel LLM calls (up to 50 concurrent workers)
• Pre-built vector index for instant semantic search
• Auto-generated Excel output with formulas for IS/BS/CF statements

Manual effort is now focused on reviewing accounts that require contextual fine-tuning and additional customer-specific input, representing only ~20% of all accounts.

Analyst capacity expanded without adding headcount, freeing teams to focus on higher-value work instead of repetitive modeling.

Beyond speed, the platform captures institutional knowledge as it’s used. That means every engagement strengthens the standard going forward, turning what was once disjointed knowledge into a repeatable, scalable capability.