Build an
Intelligence Platform
that delivers
phData turns data into decisions and action for humans and AI agents through a governed, scalable Intelligence Platform built into production.
Preferred Partner
7× Partner of the Year
2025 Design Partner of the Year
The infrastructure is there. The outcomes aren't.
The data infrastructure is there. The dashboards are live. But business leaders want agents, copilots, and real-time decisions. The gap is a platform problem.
Four patterns show up constantly:
The agent gave three different answers
Finance, Marketing, and Sales arrive at the quarterly review with three different revenue numbers. The first 45 minutes are spent arguing whose data is right. No decisions get made.
Symptom · semantic drift
The AI use case that never launched
An AI use case gets approved with an 8-week estimate. Four months in, the team is still reconciling source systems and discovering that “customer ID” means different things everywhere.
Symptom · foundation debt
The AI tool nobody approved
Someone deploys a customer-facing AI tool. Legal finds out three weeks later. The model was trained on data it shouldn’t have accessed. Nobody knows who approved it.
Symptom · ungoverned AI
The stack that can’t support an agent
Over five years you’ve licensed a BI tool, a data catalog, a quality tool, an ML platform, and an AI gateway. None talk to each other. More vendors than results.
Symptom · stack fragmentation
Recognize any of those patterns? Start a conversation.
What is an Intelligence Platform?
An Intelligence Platform helps companies make smarter decisions and take actions by connecting their data, knowledge, and processes for both human teams and AI systems. It does that through three layers, each one building on the last.
The three layers
Foundation
All your data in one place: clean, modeled, and ready. The base every other layer depends on.
Connectivity · Ingestion · Compute · Transformation · Storage · Access controls
Knowledge
Data made understandable to humans and machines alike. A semantic layer defines metrics once, consistently, everywhere. Most enterprises underinvest here and it’s why AI agents produce outputs nobody trusts.
Semantic layer · Ontology · Metrics · Feature store · Data catalog · Lineage · Vector storage · Shared context layer
Intelligence
What the business sees and acts on: analytics, ML, GenAI, autonomous agents, and AI copilots. Every use case feeds learning back into the platform, so the next one is cheaper and faster.
Reporting · Analytics · Data science · Operational ML · GenAI · Agents · AI evals · Observability
Infrastructure & governance
Security, lineage, cost management, and AI guardrails built in from day one. Not added later.
Blueprint
Building the enterprise Intelligence Platform
Get the reference architecture, layer-by-layer capability map, and the delivery playbook phData uses to take Intelligence Platforms from first sprint to production scale. Includes governance & AI guardrail patterns.
The advantage that compounds
Without a platform
Every project starts from scratch.
- TCO increases as the stack sprawls and tech debt compounds
- Conflicting data creates inconsistent answers
- Governance bolted on after the fact
- Months to deploy each use case
With intelligent systems
Shared infrastructure for every build.
- Cost drops with each use case
- Governance built in from day one
- Weeks to deploy because the foundation is already there
- Patterns and accelerators reused across initiatives
Ready to start? phData’s 3-2-1 GO framework gets your first AI use case into production fast, then builds the platform beneath it.
How phData builds Intelligence Platforms
10-plus years delivering Intelligence Platforms into production. Every engagement starts with the business decision and designs backward from there.
Methodology
phData Forge™
phData’s AI-native delivery methodology. It brings a centralized library of reusable Agent Skills and pre-built connectors for Anthropic Claude, Amazon Bedrock, and Snowflake. Every sprint ships something in production.
Team model
Forward deployed engineering
Business, data, and engineering talent in one team, working directly in the client environment. No handoffs. No phase two that never starts.
The AI roadmap
A proven path from first use case to production-scale intelligence.
Define the Intelligence Platform strategy
Identify where intelligence creates competitive advantage. Prioritize the highest-value use cases by industry.
Prove economic value fast
Ship one tangible outcome quickly. Use that win to validate the approach and fund the next phase.
Build the platform and industrialize
Harden the proven use case and build the Intelligence Platform beneath it. Every subsequent initiative gets cheaper and faster.
Scale intelligence as the operating model
Replicate the playbook across business units. Intelligence becomes how the enterprise runs.
How phData approaches Intelligence Platform services differently
The differentiator is the platform phData builds around the technology.
Decision and use case first
Every engagement starts with the business decision and its economic value. The platform designed backward from the outcome.
End-to-end ownership
One team spans problem framing, engineering, and activation into live workflows.
A learning delivery system
Forge’s Agent Skills library enables AI development 60% faster than custom builds. Each use case costs less than the one before it.
Built for speed
Delivery architecture designed for modern decision workflows. Working capability in production, every sprint.
Industry-proven patterns
Codified blueprints by vertical – see the bar below for representative outcomes.
saved per provider
Healthcare
less manual review
Financial Services
inventory cost reduction
Retail
downtime reduction
Manufacturing
See how this maps to your industry.
Partner depth
Deep platform expertise and strategic relationships that accelerate AI transformation.
Frequently asked questions
What’s the difference between an Intelligence Platform and a data platform?
A data platform stores and moves data for human consumption. An Intelligence Platform is what enterprises build on top of it to make AI, analytics, and agents reliable. It adds the semantic layer, governance infrastructure, and AI application layer that convert raw data into trusted business decisions. Most enterprises already have a data platform; what they’re missing is the knowledge layer (semantics, governed metrics), the intelligence layer (AI applications, agents), and the governance infrastructure that connects them.
What is a semantic layer, and why does it matter for AI?
A semantic layer gives business data a shared, machine-readable meaning. It defines metrics, entities, relationships, and context so that “revenue,” for example, means the same thing across every dashboard, model, and AI agent. Without this consistency, AI systems rely on conflicting definitions and produce answers people cannot trust. Despite being one of the most overlooked parts of the enterprise data stack, the semantic layer is foundational to reliable AI.
What is an ontology, and how does it fit into an Intelligence Platform?
An ontology is a structured map of the concepts, entities, and relationships that define a business domain. Think “customer,” “product,” “contract,” and how they connect to each other. Where a semantic layer standardizes how metrics are calculated, an ontology standardizes what things are and how they relate. Together, they give AI agents the context to reason about business data the way a domain expert would, rather than treating every query as an isolated lookup.
What is phData’s approach to building enterprise Intelligence Platforms?
phData builds Intelligence Platforms using phData Forge™, an AI-native delivery methodology with a centralized library of reusable Agent Skills and pre-built connectors for Anthropic Claude, Amazon Bedrock, and Snowflake. Every engagement starts by identifying the highest-value business decision the platform needs to support, then designs the architecture backward from that outcome. Forward deployed engineering puts business, data, and engineering talent in one team inside the client environment.
What does an enterprise Intelligence Platform include?
An enterprise Intelligence Platform includes three core layers: a foundation layer (unified data storage, ingestion, and transformation), a knowledge layer (semantic definitions, governed metrics, ontology, feature store, and shared context), and an intelligence layer (analytics, ML models, GenAI applications, and autonomous agents). It also includes infrastructure and governance capabilities — security, lineage, cost management, and AI guardrails — built in from day one.
Customer stories
Technology
How a K-12 Edtech Company Built a Personalized Learning Platform for Millions of Students
Discover how one of the largest K-12 edtech companies built a production-grade personalized learning platform with Claude on AWS Bedrock and Snowflake, saving $1M+ annually.
Technology
How Order.co Used Agentic AI in Procurement to Automate Vendor Ordering
See how phData used agentic AI in procurement to automate Order.co’s vendor ordering in 6 weeks, with a 100% success rate using Anthropic Claude on AWS.
Retail & CPG
How a Fast-Casual Restaurant Chain Built an AI-Powered Sales Assistant in Eight Weeks
Find out how phData built an AI-powered sales assistant on Snowflake Cortex for a fast-casual chain, translating 200+ BI measures in 8 weeks.
Insights
I Turned on AI for Everyone at My Company. Here’s What It Cost.
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Accelerating Innovation: How phData and Snowflake Are Bringing Enterprise AI to Life
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Why Traditional ERP Systems Fail to Track the Patient Journey in Life Sciences
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Get started
Start with a conversation
phData builds enterprise Intelligence Platforms. Tell us where your data is today and where AI needs to take it.