Private Equity AI Value Creation — AI Factory Methodology for Portfolio EBITDA Growth

Traditional AI consulting delivers strategy decks. Salyant delivers running systems. Our AI Factory methodology was built specifically for the PE lifecycle: compressed timelines, multi-company complexity, and the need for auditable value attribution. We don't advise — we build, deploy, and operate.

Each engagement follows a fixed three-phase structure: Opportunity Mapping (weeks 1-4), Factory Deployment (weeks 5-12), and Value Realisation (month 4+). This structure maps directly to PE investment committee gates and reporting cycles, giving Operating Partners clear visibility on capital deployment and return trajectory.

• Production-first: every use case targets live deployment, not pilot
• Portfolio-scale: single governance model across multiple portfolio companies
• PE-lifecycle aligned: phases map to DD, 100-day plan, hold, and exit
• UK-based senior architects: no junior offshoring, direct partner access
• ISO 27001 / SOC 2 ready: compliance built in, not bolted on

We conduct a rapid, data-driven assessment of AI automation potential across each portfolio company. This is not a generic maturity model — it is a quantified opportunity map tied to specific P&L levers: revenue uplift, cost reduction, working capital release, and risk mitigation.

For pre-acquisition due diligence, we deliver a Commercial AI DD report within 10 business days: identified use cases, implementation complexity scoring, data readiness assessment, and a conservative-to-aggressive EBITDA impact range. This gives deal teams a defensible AI value creation thesis for IC memos.

• Use case prioritisation by EBITDA impact, feasibility, and time-to-value
• Data readiness audit: quality, accessibility, governance gaps
• Build vs buy vs partner recommendation per use case
• Risk register: regulatory, IP, vendor lock-in, talent dependency
• 10-day turnaround for deal-cycle DD support

The Factory is our standardised deployment engine: reusable MLOps pipelines, feature stores, model registries, monitoring dashboards, and governance workflows — all pre-configured for UK regulatory environments (FCA, ICO, sector-specific). We deploy this infrastructure into each portfolio company's cloud tenant (AWS, Azure, GCP) within weeks, not quarters.

During the critical 100-day plan, we prioritise 2-3 high-impact use cases per company — typically document intelligence (KYC, invoice processing, contract review), demand forecasting, or customer churn prediction. Each goes live with full observability, rollback capability, and business-owned KPI dashboards.

• Standardised MLOps stack deployed per portfolio company
• Reusable component library: connectors, validators, explainability modules
• Automated model retraining, drift detection, and compliance logging
• Business stakeholder dashboards: no data science translation layer needed
• Shadow IT elimination: centralised AI governance from day one

Post-deployment, we shift to value realisation: tracking actual vs projected EBITDA impact monthly, expanding use case coverage, and upskilling internal teams via our embedded 'AI Factory Lead' model. This creates a self-sustaining capability that survives our exit — a key diligence point for trade buyers or secondary sponsors.

For exit preparation, we package the AI Factory as a transferable asset: documented architecture, runbooks, vendor contracts, and a quantified value track record. This transforms AI from 'key person risk' into a documented, auditable intangible asset that supports higher exit multiples.

• Monthly value realisation reporting aligned to board packs
• Embedded AI Factory Lead per portfolio company (6-12 month secondment)
• Internal team upskilling: MLOps, prompt engineering, AI product management
• Exit data room pack: architecture, runbooks, ROI evidence, IP register
• Post-exit transition support: 90-day knowledge transfer included

We offer three engagement models calibrated to PE fund economics and portfolio needs. All models include senior architect leadership, UK-based delivery, and fixed-phase pricing — no open-ended T&M.

The AI Factory Partnership is our flagship model: a retained embedded team across the fund's portfolio, priced as a platform fee plus per-company deployment costs. This aligns our incentives with fund-level IRR and enables cross-portfolio learning effects.

• Project-Based: Fixed-scope DD or single-company deployment (£75k-£200k)
• AI Factory Partnership: Retained multi-company platform (£30k/mo platform + £150k-£400k per company deployment)
• Exit Acceleration: 90-day intensive value capture pre-sale (£120k fixed)
• All models include: Senior architect lead, ISO 27001 compliance, knowledge transfer
• No hidden costs: cloud infra, licences, and third-party APIs quoted transparently

PE-backed firms operate under heightened regulatory scrutiny. Our AI Factory bakes in governance from architecture: model cards for every deployed system, automated bias and drift monitoring, full audit trails for FCA Consumer Duty and ICO accountability requirements, and data lineage mapping for GDPR Article 30 compliance.

We maintain a shared risk register across the portfolio, reviewed quarterly with Operating Partners. This covers model performance risk, vendor concentration, key person dependency, and regulatory change impact — giving the fund a single view of AI risk exposure.

• Model cards & model risk management (MRM) framework per SR 11-7 / SS1/23
• Automated drift, bias, and performance monitoring with alerting
• Data lineage & DPIA automation for GDPR compliance
• Vendor risk management: SLA tracking, exit clauses, IP ownership
• Quarterly portfolio AI risk review with Operating Partner