We help PE firms and portfolio companies develop comprehensive AI strategies that align with business objectives and drive measurable value. This includes evaluating current AI capabilities, identifying opportunities, creating implementation roadmaps, and quantifying expected ROI. We bridge the gap between technical possibility and business reality, ensuring AI investments deliver returns.
We design, implement, and optimize custom LLM solutions for enterprise use cases. This includes building RAG systems for intelligent document search, creating custom copilots for specific workflows, fine-tuning models for domain-specific tasks, and implementing secure enterprise deployments. We handle everything from architecture design to production deployment.
We identify manual, repetitive processes and transform them using AI-powered automation. This includes intelligent document processing, automated data extraction, workflow orchestration, and integration between legacy and modern systems. We focus on high-ROI opportunities where automation delivers 6-12 month payback periods.
We build predictive models that forecast business outcomes and enable data-driven decision making. This includes demand forecasting, churn prediction, pricing optimization, risk scoring, and anomaly detection. We turn historical data into forward-looking insights that drive better strategic decisions and operational improvements.
We design and implement the technical infrastructure needed to deploy AI systems at scale. This includes cloud architecture, MLOps pipelines, model monitoring, security controls, and compliance frameworks. We ensure AI solutions are production-ready, scalable, secure, and maintainable long after initial deployment.
We deliver AI solutions tailored to specific industry challenges and regulatory requirements. Our team understands the unique workflows, data structures, compliance needs, and success metrics for manufacturing, financial services, healthcare, retail, and SaaS. We don't offer generic AI—we offer industry-proven implementations.
We translate technical AI capabilities into financial metrics that PE firms care about. For every AI initiative, we model revenue impact, cost reduction, capital efficiency improvements, and timeline to value. We provide conservative, base, and aggressive scenarios with clear assumptions, helping PE firms make informed investment decisions.
We identify AI capabilities that increase company valuation and attract premium acquisition multiples. This includes building defensible AI moats, demonstrating AI-driven revenue growth, showcasing technical sophistication, and positioning AI assets for strategic buyers. We help portfolio companies become AI acquisition targets, not AI laggards.
We evaluate the hidden liabilities in AI systems—outdated frameworks, brittle architectures, missing documentation, hard-coded dependencies, and poor code quality. We quantify the cost and risk of technical debt and provide remediation roadmaps. This prevents PE firms from acquiring expensive-to-maintain AI systems disguised as valuable assets.
We assess AI systems against evolving regulatory requirements including the EU AI Act, GDPR, CCPA, sector-specific regulations (HIPAA, SOX), and emerging AI governance frameworks. We identify compliance gaps, quantify regulatory risk, and provide remediation roadmaps. We help PE firms avoid regulatory penalties and reputational damage.
We evaluate whether AI capabilities create sustainable competitive advantages or are easily replicable. We assess data moats, network effects, algorithmic advantages, technical talent barriers, and IP protection. We help PE firms distinguish between genuine AI competitive advantages and temporary technical leads.
We identify AI opportunities that deliver measurable value within 90 days—low-hanging fruit that builds momentum and funds larger initiatives. These are tactical implementations with clear ROI that demonstrate AI capability to skeptical stakeholders and finance follow-on investments.
We evaluate whether AI systems can handle 10x growth in users, data volume, and transaction throughput. We assess architecture bottlenecks, cost scaling curves, technical limitations, and infrastructure requirements. We prevent PE firms from investing in AI that works in demos but fails at scale.
We assess the strategic and financial value of proprietary datasets in M&A contexts. We evaluate data volume, quality, uniqueness, refresh rates, network effects, and monetization potential. We help PE firms understand if data assets justify premium valuations or if they're commoditized and replaceable.