Topics
The recurring questions, answered once and properly.
Long-form explanations of the concepts that come up in every AI operating decision — governed agents, connected context, execution continuity, operating systems versus point tools — written to be read rather than skimmed.
Concept-led
Organised by the question, not by our product
Durable
Written to stay correct past a release cycle
Linked
Each topic routes to the implementation path
Introduction
What belongs here and what does not.
A topic earns a page when the same question keeps arriving from people who are not asking about us. What is a business operating system. What makes an agent governed rather than merely permissioned. Why context is the constraint rather than model quality. Those deserve one careful answer that stays true.
Everything downstream of that — how a specific workflow is built, which connector to use, what a competitor does differently — lives in the implementation, integration, and comparison families, and each topic routes there rather than trying to be all of them.
The two product families, Launch and Grow, have their own topic hubs because the questions differ in kind: building an operating surface and running revenue against it are not variations on one subject.
- Depth
- Long-form, one concept per page
- Scope
- The concept, not the configuration
- Product hubs
- /topics/launch and /topics/grow
Why this exists
Why the concept layer keeps getting skipped.
AI content overwhelmingly answers "how do I do X with tool Y" and almost never answers "what is X, and is it the right thing to want". That is fine when the category is settled. It is expensive when the category is three years old and the vocabulary is still moving.
The cost shows up as decisions made on mismatched definitions: two people agreeing to build an agent while meaning different things by autonomy, or a platform bought on a definition of context the vendor did not share.
These pages exist so that the definitional argument happens before the procurement one, where it is cheap.
You're likely here because
- Two teams are using the same word for materially different things
- A decision keeps stalling on what counts as governed or autonomous
- Vendor comparisons are being made on features rather than on the underlying model
- The same explanatory conversation is happening in every meeting
How to choose
Where to start.
If
You are building the operating surface
Start with
The Launch topic hubIf
You are running revenue and follow-up against it
Start with
The Grow topic hubIf
You want the evidence rather than the explanation
If
You are comparing us against a specific product
Start with
The comparison familyHow it works
How a topic page is built.
The same structure each time, so a topic can be entered at any point rather than read from the top.
Step 01
Define the term
Plainly, and including what it excludes. A definition that covers everything decides nothing.
Step 02
State why it matters operationally
The decision the concept changes. If nothing downstream changes, it is vocabulary rather than a topic.
Step 03
Show the system context
Which systems participate, what stays authoritative, and where the boundary sits — the part that turns a concept into something implementable.
Step 04
Give the honest limits
What the approach does not solve. This is the section that makes the rest usable, and the one most often omitted.
Step 05
Route to the implementation
Every topic ends by pointing at the build, integration, or comparison page that acts on it, so the reading has somewhere to go.
All topics
Every topic, and the two product hubs — 59 in total.
Concept pages plus the Launch and Grow topic hubs, which carry the product-specific questions.
Product topic hubs
Building, and running revenue against what was built
Product topics
Building and running the operating surface.
AI website builder
Build a working website from the business outcome, not a blank canvas.
Read the topic →AI app builder
Turn a plain-language requirement into a working business app.
Read the topic →AI CRM builder
Build the CRM workflow your team actually needs.
Read the topic →AI dashboard builder
Build dashboards around the decisions people need to make.
Read the topic →AI workflow builder
Replace spreadsheet-and-email processes with purpose-built workflow software.
Read the topic →AI internal tools
Build the internal software that never makes the roadmap.
Read the topic →AI sales automation
Automate the repetitive sales work without disconnecting it from the pipeline.
Read the topic →AI lead generation
Move from account discovery to qualified follow-up in one operating context.
Read the topic →AI email automation
Keep outbound and replies grounded in real prospect context.
Read the topic →Revenue operations
Connect pipeline execution, scheduling, attribution, and operating context.
Read the topic →CRM automation
Automate CRM work around the customer journey instead of creating more admin.
Read the topic →Sales workflows
Turn the path from prospect to meeting to opportunity into a repeatable workflow.
Read the topic →Concepts
The vocabulary the decisions are made in.
Open-weight AI
Use open-weight models as part of the operating stack, not as the entire product strategy.
Read the topic →AI agents
Move from agents that answer to agents that operate inside bounded workflows.
Read the topic →AI operators
Give AI a governed operating context, not just another chat window.
Read the topic →AI workflows
Connect AI decisions to the real systems and handoffs where work happens.
Read the topic →Multi-agent systems
Coordinate specialized AI work through one governed operating layer.
Read the topic →Operating-system comparisons
Enterprise operating-system questions, answered for the mid-market.
What is Palantir?
Understand Palantir as an enterprise operating-system architecture for connecting data, AI, software delivery, and operational workflows.
Read the topic →How Palantir works
Break down the operating model behind Palantir without reducing it to a dashboard or a single AI model.
Read the topic →Palantir AIP explained
Understand AIP as the generative-AI layer in Palantir’s broader enterprise operating architecture.
Read the topic →Palantir Foundry explained
Understand Foundry as the data and ontology foundation beneath many Palantir workflows.
Read the topic →Palantir Ontology explained
Understand why Palantir’s Ontology is more than a semantic data catalog.
Read the topic →Enterprise operating system
Understand the enterprise operating-system category that Palantir has helped make visible.
Read the topic →AI operating system
Define what makes an AI operating system different from a chatbot, model endpoint, or dashboard.
Read the topic →Palantir alternatives
Evaluate alternatives by the operating problem you need to solve rather than by copying Palantir feature for feature.
Read the topic →Palantir alternatives for small businesses
Translate enterprise operating-system ideas into a smaller-business buying decision.
Read the topic →Palantir for small business
Use Palantir’s operating-system concept as a framework for deciding what an SMB actually needs.
Read the topic →Palantir for mid-market companies
Frame the operating-system decision for companies that have outgrown point tools but do not want unnecessary complexity.
Read the topic →Palantir concepts for sales teams
Apply the connected operating-system model to pipeline, accounts, activity, forecasting, and next actions.
Read the topic →Palantir concepts for marketing teams
Apply enterprise operating-system thinking to campaigns, content, attribution, audiences, and revenue handoffs.
Read the topic →Palantir concepts for RevOps
Apply the enterprise operating-system pattern to the systems and handoffs that connect marketing, sales, and customer revenue.
Read the topic →Palantir concepts for business operations
Apply the operating-system model to approvals, exceptions, handoffs, capacity, and recurring operational decisions.
Read the topic →Palantir concepts for finance operations
Apply connected-data and workflow principles to planning, reporting, approvals, and operational finance without treating automation as financial advice.
Read the topic →Palantir concepts for healthcare operations
Use the operating-system pattern for non-clinical healthcare workflows, capacity, scheduling, operations, and administration.
Read the topic →Palantir concepts for pharmaceutical operations
Apply connected data, applications, and governed AI workflows to pharmaceutical business and operational processes.
Read the topic →Palantir concepts for manufacturing
Apply ontology and operational AI concepts to production, quality, maintenance, inventory, and plant workflows.
Read the topic →Palantir concepts for supply chain
Apply connected-data and decision workflows to inventory, suppliers, orders, logistics, and exceptions.
Read the topic →Palantir concepts for logistics
Use operational AI patterns for routing, scheduling, capacity, service levels, and exception management.
Read the topic →Palantir concepts for construction
Apply connected operational software to projects, schedules, crews, estimates, documents, and field handoffs.
Read the topic →Palantir concepts for professional services
Translate the enterprise operating-system model into client delivery, knowledge, staffing, pipeline, and project workflows.
Read the topic →Palantir concepts for real estate
Apply connected operations to leads, properties, transactions, scheduling, client communication, and portfolio workflows.
Read the topic →Palantir concepts for insurance operations
Apply connected-data and workflow principles to intake, service, claims operations, sales, and internal coordination without automating regulated judgments by default.
Read the topic →Palantir concepts for accounting firms
Apply a connected operating layer to client intake, document collection, status, reporting, and business-development workflows.
Read the topic →Palantir concepts for agencies
Apply operating-system ideas to client delivery, campaign data, reporting, assets, pipeline, and recurring workflows.
Read the topic →Palantir vs. AI app builders
Understand the difference between an enterprise operating system and a prompt-to-application builder.
Read the topic →Palantir vs. no-code platforms
Compare no-code application creation with a broader enterprise operating-system model.
Read the topic →Palantir vs. CRM platforms
Separate customer-system-of-record needs from broader enterprise operating-system needs.
Read the topic →Palantir vs. business intelligence
Compare reporting and analytics with systems designed to connect decisions to operational actions.
Read the topic →Dashboards vs. AI operating systems
Understand when visualization is enough and when the business needs a closed execution loop.
Read the topic →AI ontology for business
Understand the role of a shared business-object model in operational AI.
Read the topic →AI agents with enterprise data
Connect agents to governed business context without turning every data source into an unrestricted prompt feed.
Read the topic →Operational AI explained
Define operational AI as AI connected to real workflows, systems, decisions, and actions.
Read the topic →Closed-loop AI
Understand the difference between AI that produces an answer and AI that closes the business loop.
Read the topic →Human-in-the-loop enterprise AI
Design AI workflows where people remain explicit decision makers at the points that require judgment, authorization, or accountability.
Read the topic →Enterprise AI workflows
Build AI workflows around real systems, permissions, handoffs, and measurable outcomes.
Read the topic →Build a Palantir-style operating system
Use the architectural ideas behind enterprise operating systems without assuming one vendor or recreating Palantir feature for feature.
Read the topic →Palantir Pilot and AI app building
Understand Palantir’s move into natural-language application building on top of its Ontology.
Read the topic →Scope
What is deliberately elsewhere.
- Step-by-step implementation lives in the workflow, template, and build families, not here.
- Connector specifics live in the integration guides, because they change on the provider's schedule rather than the concept's.
- Head-to-head product judgements live in the comparison family, where the verdict can be stated plainly.
- Measured findings live in research, which holds a stricter evidence standard than an explanatory page needs.
FAQ
Questions about this collection.
How is a topic different from a blog post?
A topic is maintained. It is expected to stay correct and to be revised when it stops being; a post is dated and stays as written.
Why are Launch and Grow separate hubs?
Because building an operating surface and running revenue against it raise different questions, and merging them produced pages that answered neither well.
Do these pages assume we use UbiGrowth?
No. The concept sections are written to be useful regardless, and the product appears where the implementation does — clearly marked as such.
How current are these?
They are revised when a topic stops being accurate rather than on a schedule. Where a topic depends on something that moves quickly, the page says so.
Keep exploring
Related paths.
Start here
The definitional argument is cheaper than the procurement one.
Describe the decision you are trying to make. ARIA resolves which concepts actually bear on it and what the first bounded implementation would cover.