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Give people and AI the same business language

Knowledge Graphs & Ontologies

Palmetto designs ontologies and knowledge graphs that turn fragmented data into one shared model teams and AI can both use, tracking the entities, rules, and provenance a decision depends on.

Discuss a knowledge problem →See the delivery path ↓

When to call

Recognize the operating
signal.

Start with the condition you can observe, the consequence it creates, and the person accountable for changing it.

Where it shows up

Concrete uses,
not AI theater.

We apply these patterns to the workflow, systems, decision rights, and measures that drive your business.

01

Connected customer, product, and risk views

Resolve entities and relationships across source systems so teams can understand context without flattening away provenance.

02

Policy, control, and evidence traceability

Connect obligations, policies, controls, owners, systems, and evidence to make coverage and gaps easier to examine.

03

Graph-grounded AI and search

Give assistants and agents explicit relationships, definitions, and sources that complement document retrieval.

04

Data and integration semantics

Create reusable business meaning for APIs, data products, analytics, migration, and system integration decisions.

The work product

What gets delivered.
What keeps it controlled.

Core deliverables

  • Priority decisions and competency questions the knowledge layer must support
  • Domain ontology, terminology decisions, examples, and model documentation
  • Graph architecture, source mappings, identity strategy, and provenance model
  • Ingestion, query, API, GraphRAG, or application pattern exercised with representative data
  • Governance workflow, quality checks, change process, and adoption roadmap

Production and governance controls

  • Source-level provenance, confidence, effective dates, and lineage where the use case requires them
  • Access controls aligned to source-system entitlements and intended users
  • Ontology and mapping versioning with reviewable change decisions
  • Data-quality checks, reconciliation paths, and explicit handling of contradictory facts

Delivery path

Move from decision
to production.

Discovery begins with the decisions and questions that matter, then expands the model and platform around those uses.

  1. 01

    Diagnostic

    Scope-dependent

    Define the operating problem, baseline the current state, test feasibility, and make the value, risk, and decision path explicit.

    Typical outputDecision brief, baseline, target state, and sequenced recommendations

  2. 02

    Production-shaped pilot

    Scope-dependent

    Exercise the hardest assumptions with representative data, real users, appropriate controls, and an architecture that can survive beyond a demo.

    Typical outputWorking pilot, evaluation evidence, operating lessons, and go-forward decision

  3. 03

    Scale & rollout

    Scope-dependent

    Harden what works, integrate it into the operating environment, expand adoption, and transfer the knowledge required to run and improve it.

    Typical outputProduction capability, rollout evidence, runbooks, and ownership handoff

Measures & team

Measure the change.
Build with the owners.

Measures that show progress

  • Coverage and accuracy against the priority business questions
  • Entity match, duplicate, reconciliation, and exception rates
  • Time required to find, explain, and validate an answer
  • Reuse across workflows, integrations, analytics, or AI applications

The team we build with

  • A business owner who can prioritize the decisions and users the model must support
  • Domain experts empowered to resolve terminology and relationship questions
  • Data owners who understand source quality, identifiers, access, and lineage
  • Application, platform, governance, and security participants matched to the intended use

Ownership

Build a capability
your team can own.

The ontology, mappings, quality rules, and governance playbook are documented and handed to your team, platform choices included.

Questions buyers ask

Before we
begin.

What is the difference between a knowledge graph and an ontology?

An ontology defines the concepts, relationships, and rules of a domain. A knowledge graph uses that meaning to connect real entities and facts. They usually work best together.

Do we need a new graph platform before we begin?

Not necessarily. Early work tests the model against representative data before any platform decision. Existing databases and tools get evaluated before we recommend anything new.

Connected disciplines

Start with the operating problem

Bring the context.
We will define the next step.

Share the workflow or decision, who owns it, what it costs today, and what makes the timing matter. We turn that context into a practical path with clear deliverables, measures, timing, and commercial terms.

Discuss a knowledge problem →

Or email [email protected].