Why In the Race for AGI, We’re Choosing to Chase Real Domain Knowledge (RDK)? 

How Espalier.ai Thinks About AI, Strategy & Enterprise Decision-Making

At Espalier.ai, we are deeply focused on the intersection of AI, Strategy, M&A, Private Equity, and Sustainability. Over the last few years, as AI has evolved at a breathtaking pace, we’ve been doing three things relentlessly:

• Reading an extraordinary amount of research, analysis, and technical content
• Listening to podcasts and expert perspectives across disciplines
• Thinking deeply about how AI can fundamentally transform enterprise strategy & decision-making

The volume of content is overwhelming. The opinions are contradictory. The hype cycles are relentless. There is so much being written and said that staying grounded in what we believe requires active, disciplined reflection.

We are asked frequently: “How will AI truly impact enterprise strategy and decision-making?”

We’ve asked ourselves the same question repeatedly. So, we decided to write down our belief system—not only to clarify our own thinking, but to invite critique, debate, and dialogue. And if the SEO gods are kind to us, we wouldn’t mind generating some new business along the way.

What’s Happening Around Us?

We are living in a confusing yet fascinating AI landscape:

• Some experts believe we are 5–10 years away from AGI; others say we’re in an AI bubble.
• Every week a new LLM claims to be “the closest step yet” to AGI—while Richard Sutton, the father of modern reinforcement learning, believes LLMs are a dead end.
• AI innovation is accelerating at an unprecedented pace, with massive investment—yet most enterprises still struggle to show measurable revenue or operational impact.
• Consulting firms publish wave after wave of AI frameworks—but we still haven’t seen a seminal playbook on how AI should be applied to strategy and management consulting at scale.

Amid this noise, we chose to focus on what is real and useful.

What We Believe About AI & Enterprise Decision-Making?

Our belief system has been shaped by years of experimentation, client work, platform building, and immersion in both the academic and practical worlds of AI. We have seen what works, what fails, what scales, and what doesn’t—and these beliefs represent the foundation of our approach.

1. Context is Everything.

Enterprise decisions are only as strong as the context behind them.
We ask:

• What data, intelligence, and knowledge is being used?
• How credible and comprehensive is it?
• How current and complete is it?
• Does it embed domain expertise, past experience, and organizational memory?

AI cannot fix poor context; it can only amplify it. This is why we obsess about capturing, refining, and structuring context before deploying any downstream AI.

2. AI Will Reshape Enterprise Revenues—and Reshape Competitive Landscapes Even Faster

AI is not simply a revenue growth tool. It is a competitive accelerant.
For every enterprise that deploys AI to grow faster, there are competitors—incumbents or new entrants—using the same tools to disrupt, replicate, or outperform.

We have experienced this firsthand: AI has helped us increase our revenues and scale faster, but it has also empowered our clients to do what previously required our direct intervention.

In an AI-first world:

• Business models change quickly
• Operating models become obsolete faster
• Knowledge-intensive work transforms rapidly
• Competitive advantages become increasingly temporal

This reinforces our belief that the future winners will be those who continuously learn, adapt, and restructure their intelligence systems—not those who simply deploy AI tools.

3. The Path to Impact Isn’t AGI — It’s the Disciplined Use of AI Across Domains

We don’t know when AGI will arrive. No one does. But we do know this:

Enterprises don’t need AGI to unlock transformational value. They need a thoughtful combination of:

• Large Language Models
• Graph-based intelligence
• Reinforcement learning
• Traditional machine learning
• Expert systems
• Domain ontologies
• Knowledge engineering

LLMs are a breakthrough, but they are not sufficient alone for enterprise-scale decision-making.
Real transformation comes from integrating multiple AI methods, each playing to its strengths.

4. The Fusion of External Intelligence + Domain Intelligence is the Real Unlock.

When you combine:

• Growing external signals
• Domain expertise
• Enterprise memory
• AI across multiple disciplines

…you create decision systems that are truly transformative.

5. Real Domain Knowledge (RDK) Will Matter More Than AGI for the Next Decade

AI without domain expertise is merely a fast, confident, and often wrong storyteller.
Enterprises don’t need generic intelligence—they need domain-specific intelligence, deeply informed by their industry, competitive environment, regulatory context, and operating model.

We believe the race that will define competitive advantage is not the race to AGI, but the race to codify, connect, and operationalize Real Domain Knowledge (RDK).

6. The Future Belongs to Networks of Networks

Enterprises do not operate in isolation. Strategy requires understanding:

• Competitors
• Supply chains
• Regulations
• Markets
• Technology ecosystems
• Economic systems
• Customers
• Talent pools

Each of these domains has its own intelligence graph. The real power comes when these are interconnected — when enterprises can see how decisions ripple across markets, industries, and geographies.

We believe that networked knowledge systems will redefine strategy, scenario planning, forecasting, and M&A.

7. The Best Builders of These Knowledge Networks Will Define the Next Era of Strategy

The next era of strategy and consulting will be shaped not by those with the best frameworks or presentations, but by those who can build and continuously refine:

• Domain-specific knowledge systems
• Interconnected graphs
• Adaptive intelligence pipelines
• Context-aware decision engines

These will become the new operating system for enterprise decision-making.

Knowledge graphs form a very important part of our belief system and for this post we will expound further on that.

Why Knowledge Graphs Are Foundational to Our Approach?

Search Google and you’ll find a standard definition:
A knowledge graph represents real-world entities and their relationships, transforming raw data into meaningful, connected intelligence.

But for enterprises, knowledge graphs answer a more important need:
They provide comprehensive, connected, contextualized intelligence for strategic and tactical decisions.

We have chosen knowledge graphs as a foundational pillar for one simple reason: they preserve and expand context better than anything else.

1. They preserve enterprise context that LLMs alone cannot.

LLMs can generate content but do not inherently retain structured memory.
Knowledge graphs store the evolving domain context permanently.

2. They connect intelligence across silos.

Internal data + external data + expert knowledge — all integrated in a unified semantic layer.

3. They enforce domain structure.

Every domain has rules, hierarchies, and constraints.
Graphs enforce these, ensuring intelligence is consistent, complete, and logically sound.

4. They make downstream AI more reliable.

Better inputs → Better analytics → Better decisions.

5. They evolve continuously.

Every new signal, fact, or event updates the graph, making it a living, adaptive knowledge system.

Knowledge Graphs Bridge the Gap Between Raw Data and Strategic Intelligence

Modern enterprises have unprecedented access to data—news, filings, websites, presentations, earnings calls, images, videos, structured feeds, internal documents, etc.
But this data is:

• Fragmented
• Unconnected
• Uncontextualized
• Lacking domain interpretation

Knowledge graphs solve this by converting raw data into:

• Entities (companies, industries, commodities, locations, people, facilities)
• Attributes (facts, signals, relationships, time-based variables)
• Relationships (who is connected to what, and how)
• Structures (industry taxonomies, domain ontologies, hierarchies)
• Contextual meaning

This makes them the ideal backbone for strategy, M&A, competitive intelligence, risk assessment, and forecasting.

How We Build Domain-Specific Knowledge Graphs?

Our first question is always: “What questions should this knowledge graph answer?”

That leads to six core steps:

1. Identify the sources of intelligence (internal, external, human, machine-generated). We map everything needed:
   • Public data
   • Proprietary data
   • Internal documents
   • Third-party APIs
   • Websites, News, Filings, Reports etc.
   • MCP servers
   • Machine-generated content
   • Videos & Images
   • Human-fed insights
2. Extract the intelligence needed from each source – Facts, Signals, Events, Concepts, Metadata, Relationships
3. Condition it to be domain-ready – clean, normalize, classify, de-duplicate, map, structure, and align it to domain logic. 
4. Enrich& organize it with domain expertise.
   • Embed known relationships, rules, heuristics, industry constraints etc.
   • Translate domain related conceptual hierarchies into taxonomy structures
   • Structure and organize with ontology logic
5. Continuously updatethe graph to keep it current
   • Continuous monitoring of information sources e.g. news, filings, third-party APIs, regulatory updates, websites etc. for new facts, relationships, signals making it a continuously learning knowledge system. 

We’ve built our platform and methodology to execute these steps at scale—and to ensure the knowledge graph continuously expands and learns.

This translates into three major pillars:

1. Content Search, Extraction & Pre-processing at scale
2. Intelligence Extraction Scale
3. Knowledge Organization

Pillar 1: Content Search, Extraction & Pre-Processing at Scale

Multi-Channel Content Discovery

Our platform discovers and aggregates intelligence from every relevant channel required for strategic decision-making:

• Public News Coverage: Scans 100,000+ global news sources daily across languages and geographies.
• Company Newsrooms: Continuously retrieves announcements directly from company-owned channels, customizable by sector or company list.
• Targeted Web Search: Automates large-scale web crawling to find the most relevant content aligned to the use case.
• Enhanced Media Search: Extends discovery beyond text through automated Google Maps, image, and video searches to provide richer situational context.
• Third-Party / External Sources: Integrates with government, public, and paid data sources via APIs, MCP servers, or custom extraction pipelines.

Multimodal Content Capability

Powered by advanced LLMs, our platform seamlessly ingests raw content across every modality, including:

• News articles
• Websites
• Documents and presentations
• Images
• Scanned PDFs (via multi-language OCR)
• Google Maps, satellite imagery
• Videos
• MCP server outputs

Content Pre-Processing & Indexing

We transform raw data into clean, structured, AI-ready intelligence through:

• Content Sanitization: Cleaning, normalizing, and deduplicating content for accuracy.
• Smart Classification: Categorizing content by industry, topic, entity, theme, or relevance based on the use case.
• Metadata Enrichment: Attaching extensive metadata for better searchability, traceability, and context.
• Efficient Indexing: Making content quickly searchable and retrievable across millions of documents.
• LLM Optimization: Structuring and formatting content for best possible performance with LLMs and reasoning models.

This ensures all downstream extraction, modeling, and decision workflows operate on consistent, high-quality inputs.

Pillar 2: Intelligence Extraction at Scale

Fact Extraction

We use base models with optimized prompts and fine-tuned variants to extract structured intelligence from multimodal content. These models produce complete fact profiles for:

• Companies
• Industries
• Commodities
• Locations
• Executives/ People
• Projects
• Facilities

Where needed, models are specifically trained to extract all essential facts for entities of interest.
We integrate Retrieval-Augmented Generation (RAG) for precision and provide interfaces for domain experts to create and expand tailored training datasets.

Signal Identification

Our platform detects critical signals, alerts, and early indicators from news and external intelligence streams.

• 350+ Company Signals: Growth, performance, risks, acquisitions, funding, product launches, partnerships, contracts, and more.
• 250+ Location Signals: Macroeconomics, governance, markets, natural disasters, health, population, local developments.
• 50+ People Signals: Career movements, achievements, capabilities, announcements.
• 25+ Commodity Signals: Price movements, supply–demand trends, inventory levels, production metrics.

These pre-tuned models deliver real-time situational awareness across entities, markets, and geographies.

Concept Extraction

We use specialized reasoning models to extract domain-specific concepts and structures from unstructured content.

Example: Extracting a company’s detailed product and service catalog from websites or documents.

Capabilities include:

• Comprehensive extraction of all products and services offered
• Performance across industries and languages
• Mapping companies to custom taxonomies
• Generating foundational building blocks for downstream analytics

Pillar 3: Knowledge Organization

Our knowledge organization layer uses taxonomies, ontologies, and knowledge graphs (SKOS) to create a structured intelligence backbone for all AI and analytics workflows.

Taxonomies

Taxonomies embed domain expertise into the system.
For example, building a granular industry taxonomy or market map becomes the foundation for:

• Company analytics
• Industry analytics
• Strategy and M&A evaluation
• Competitive intelligence

Taxonomies ensure intelligence is organized, comparable, and navigable across domains.

Ontologies

Ontologies provide the conceptual framework that defines how entities, facts, concepts, and relationships should be organized and interpreted.

Over years of work, we’ve built domain-specific ontologies that are:

• Comprehensive
• Flexible
• Scalable as new intelligence sources expand

Ontologies guide the continuous-learning process and enforce consistent logic within the knowledge graph.

Knowledge Graph

Graph databases are our chosen method for saving, structuring, and expanding context.

They unify intelligence from all sources – internal, external, machine-generated, and human-curated – into a single semantic layer that becomes:

The source of truth for all downstream AI, analytics, and decision workflows – AI agents, LLMs, Predictive models, Scenario planning, Risk analytics, Portfolio intelligence, Strategy workflows etc.

A living, adaptive knowledge engine powering enterprise decision-making.