Agentic analysis across data rooms, reports and research, turning weeks of reading into decisions with sources attached.
- Document understanding across data room content
- RAG grounding with cited, checkable answers
- Validated, self-checked analysis outputs
Operating levers engineered into portfolio companies as working systems: pricing, data, cost and throughput, not recommendations.
- Pricing and commercial excellence systems
- Data harmonization powering the P&L
- Cloud and operating cost reduction builds
Portfolio company KPIs brought into one governed picture, so the fund reads performance instead of chasing it.
- Portco data integration and normalization
- Governed, AI-ready reporting models
- Board and LP report readiness
CIMs, contracts and fund documents extracted, compared and summarized at scale, with your team reviewing instead of reading everything.
- Clause and term extraction at scale
- Cross-document comparison and summarization
- Review workflows with deal-team sign-off
A structured scan of each portfolio company’s AI potential: readiness, use cases and a value case the board can sign.
- AI readiness and data assessment per portco
- Opportunity map tied to operating levers
- One prioritized use case with a signed value case
Knowledge-grounded assistants answering LP and reporting questions with citations, and a human one handoff away.
- Copilots grounded in fund documents and data
- Cited, auditable answers by default
- Human review before anything leaves the building
Decision Intelligence · Delivered in Enterprise Advisory · Agentic CIO Advisor
The Challenge
- Enterprise data scattered across many reports
- Limited access to senior-level expertise
- Manual analysis slowing strategic decisions
Our Approach
- Document understanding across unstructured reports
- RAG grounding against an enterprise knowledge base
- An orchestrator running analysis, validation and synthesis
- A reflection agent self-checking every output
Outcomes
Value Creation · Delivered in Food Services · Data-Driven Value Creation at Scale
The Challenge
- The same product described differently across every source
- Manual, time-intensive matching across catalogues and formats
- Complex pricing across millions of combinations
Our Approach
- Document processing capturing product and pricing data at scale
- Attributes normalized to a unified ontology
- A pricing engine normalizing units, contracts and rebates
Outcomes
Research AI· Delivered in Pharma · Intelligent Research Assistant
The Challenge
- Massive specialist literature across sources
- Time-consuming manual research across resources
- Regulatory grade content required
- Limited traceability and explainability
Our Approach
- Intelligent processing with OCR, parsing and summarization
- RAG with vector and semantic search
- A domain-tuned LLM for specialist language
- Agent orchestration across research sources
Outcomes
Production-Grade Delivery
Human in the Loop, Always
Value Creation as a Design Input
Production, Not Pilots
Discover
Design
Build
Run & Scale
It stays inside your environment and your NDA perimeter, with access controls and audit trails that would survive diligence on the diligence.
Scope stays narrow on purpose: one lever, one working system, then the next, so value shows against a single lever before anything scales.
The method transfers even where the stack differs: a readiness scan, one lever, a working system, repeat, with learnings carried between portcos.
Engagements are structured so the portfolio company owns what is built in its environment, and the system transfers as an asset at exit.
As engineering, not experiments: documented systems, evaluation records and audit trails that a buyer’s diligence team can walk through.
