So How Is Synapse Different from Palantir?

Today, an SVP at a mid-sized enterprise asked me how Synapse is different from Palantir, and where the overlaps are. A genuine, curious question. Not the first time I’ve been asked this either. There seems to be an assumption floating around that Synapse is doing something close to what Palantir does.

I get why. Both companies are in the business of making enterprise data useful to AI. But that’s a bit like saying Tesla and Uber are in the same business because they both move people around. The how matters. A lot.

I felt the need to write my thoughts down. So here goes.

What Palantir Actually Does

Palantir has four main products: Gotham (defense and intelligence), Foundry (enterprise data platform), Apollo (deployment infrastructure), and AIP (AI layer that sits on top of the other three). The centerpiece of it all is their Ontology, a semantic layer that models an entire organization’s data, logic, actions, and security into a single coherent structure.

In practical terms, Foundry connects to your enterprise systems and models them into their Ontology deciding how every object, relationship, and action is represented inside their platform. With their newer MMDP (Multimodal Data Plane) architecture and Virtual Tables, the raw data can stay in your existing systems like Snowflake, BigQuery, or Databricks. Palantir reads it in place. But the Ontology modeling itself the work of mapping your business into their semantic layer still happens inside Foundry. Once everything is modeled, AIP lets large language models interact with that structured representation.

It works. Palantir generated $1.63 billion in revenue in Q1 2026 alone, up 85% year-over-year. Companies like Hertz, GE Aerospace, and Kirkland & Ellis have built serious workflows on it. I’m not here to argue that Palantir doesn’t deliver value. It clearly does.

But the architecture carries a cost that most people don’t talk about until they’re deep into an implementation.

The Cost of the Ontology

Here’s what doesn’t change even when data stays in place: someone has to model your business into Palantir’s Ontology. Every object, every relationship, every action, every security rule. That modeling effort is the real investment, and it compounds in both directions. Model it well, and every subsequent use case ships faster. Rush it (and under pressure, teams always rush), and workarounds become permanent.

Palantir has been moving toward faster onboarding through AIP Bootcamps, and some customers have converted to production agreements in weeks rather than months. That’s real progress. But the Ontology modeling commitment remains you’re still deciding how your entire business is represented inside their platform, and those decisions have long-term consequences.

I’ve spent 27 years inside enterprises, 13 of them at Ford. I’ve seen every flavor of “model everything into a single structure first, then build on top.” Data lakes. Data warehouses. Data meshes. Each generation promised that if you just organized your data into the right structure, the applications would follow.

Sometimes they did. Often they didn’t, because the effort of modeling the business exceeded the patience (and budget) of the business teams waiting for results.

That’s the real tension: by the time you’ve modeled the 50th system into the Ontology, the first 10 have changed.

Enterprise data is alive. It doesn’t sit still while you build the perfect model of it.

What Synapse Does Differently

At Synapse, we start from a different belief: the people who use enterprise systems are the best judges of how those systems should work. Our job isn’t to model their business into a new platform. Our job is to build a bridge between their existing enterprise knowledge and the AI agents that need access to it.

Here’s the thing most people miss about enterprise knowledge. It doesn’t live in one place. It’s scattered across three distinct layers, and each layer requires a different approach to unlock it.

Knowledge locked in systems. Your ERP knows what’s in inventory. Your MES knows what’s happening on the production line. Your CRM knows the last conversation with a customer. This is structured, transactional knowledge that lives inside codebases and APIs. Synapse reads these codebases and generates production-ready MCP servers that give AI agents direct access to these systems. The data stays where it is. The systems stay untouched. No Ontology modeling required.

Knowledge buried in documents. Every enterprise has a second brain made of PDFs, SOPs, engineering specs, regulatory filings, training manuals, and tribal knowledge captured in SharePoint folders that nobody remembers exist. This knowledge is unstructured, scattered, and practically invisible to AI without deliberate effort. Synapse’s Agentic RAG Pipelines bridge this gap. They ingest, chunk, embed, and serve document-level knowledge so that AI agents can reason over it alongside the structured data from your systems. Not a generic search index. Purpose-built retrieval pipelines designed for the specific way enterprise teams actually ask questions.

Knowledge embedded in workflows. This is the most underestimated layer. How does your procurement team actually approve a PO? What’s the real escalation path when a quality issue surfaces on the line? These aren’t documented in any API. They’re encoded in the habits, heuristics, and institutional memory of the people who do the work every day. Synapse captures these as Skills, reusable workflow definitions that teach AI agents how to follow the same processes your teams follow. Skills turn tribal knowledge into executable context.

When you bring these three layers together inside the Synapse context engine, something interesting happens. An AI agent doesn’t just have access to data. It has access to the systems, the knowledge, and the know-how of the enterprise. That’s what we mean by “Enterprise Context Engine.” Not a data platform. A context platform.

Why We Chose This Path

This wasn’t an accidental architectural choice. It comes from a set of beliefs we hold at 2nd Brain Labs about how enterprise AI should work.

Decisions are better made by the people who use the systems. When you model a business into a platform’s Ontology, you inevitably create a layer of abstraction between the people who understand the business and the systems they rely on. A supply chain manager at a manufacturing plant knows their MES system intimately. They know the quirks, the edge cases, the unwritten rules that no ontology will capture. Synapse keeps them in the driver’s seat. We don’t ask them to learn a new platform. We bring AI to where they already work.

Existing enterprise systems should stay untouched. Enterprises have spent decades and billions building their IT landscapes. These systems work. They’re battle-tested. They have institutional knowledge baked into them. The last thing a CTO needs is another platform that requires modeling every system into a proprietary semantic layer before AI can use it. Synapse generates context layers from these systems. We don’t touch the systems themselves. We read them, understand them, and create bridges that AI agents can cross.

Enterprise context needs operational control. Once you’ve built the bridges, you need to manage them like any production infrastructure. Synapse provides administrative controls to start, stop, pause, and resume individual MCP servers, RAG pipelines, and Skills. Need to decommission a legacy system? Shut down its MCP server without affecting anything else. Rolling out a new document set for a regulatory change? Spin up a new RAG pipeline, test it, and go live without touching the existing ones. Upgrading a workflow? Pause the old Skill, deploy the new one, and resume when validated. This is the kind of operational flexibility that enterprise IT teams expect but rarely get from AI platforms.

Governance must be applied at the source, not at the consumer. This is where I think the industry is getting it backwards right now. Most agent security products are building guardrails around what agents can do after they’ve already gained access. Think of it as putting a security guard at the exit of a building rather than at the entrance.

Synapse flips this. Our governance layer creates security tokens based on the resources an agent has access to. A context layer security admin defines which MCP servers, RAG pipelines, and Skills a particular enterprise team can use. The token encodes exactly what an agent is allowed to touch. Any rogue attempt by an agent to access resources outside its token scope gets rejected at the source, before the request even reaches the system.

This isn’t just a philosophical difference. It’s a practical one. When you govern at the consumer (the agent), you’re playing whack-a-mole. Every new agent, every new model, every new prompt injection technique becomes a new attack surface. When you govern at the source (the context layer), you have one control plane that doesn’t care which agent is asking. The token is the contract. The security admin holds the keys. The enterprise keeps control.

Where the Overlap Actually Exists

I want to be honest about this. The overlap is real, but it’s narrow.

Both Synapse and Palantir are solving the problem of making enterprise knowledge accessible to AI. Both care deeply about governance and security. Both can work without moving source data out of existing systems. Both operate in verticals like automotive and healthcare where data sensitivity is non-negotiable.

But the architectural philosophies are different enough that they almost never compete for the same deal. An enterprise evaluating Palantir is usually looking for a full platform play: model everything into their Ontology, build applications on top. An enterprise evaluating Synapse is looking for a faster, lighter path to making their existing systems, documents, and workflows AI-ready without modeling anything into a new platform.

If anything, there are scenarios where the two could coexist. An enterprise might use Palantir Foundry for their core data operations while using Synapse to give AI agents access to the long tail of internal systems, document repositories, and team-specific workflows that haven’t been onboarded to Foundry’s Ontology.

The Bet We’re Making

The enterprise AI market is still early enough that architectural choices matter more than brand names. The question every F500 CTO is asking right now isn’t “which vendor do I pick?” It’s “do I model my business into a platform, or do I make what I already have AI-accessible?”

We’re betting on the second path. Not because platform modeling doesn’t work, but because most enterprises don’t have the time, budget, or organizational patience to model everything before they can start getting value from AI.

Three layers of enterprise knowledge. MCP servers for systems. RAG pipelines for documents. Skills for workflows. Operational controls to manage them. Security tokens to govern them. All sitting on top of the systems you already have, without modeling a single object into a proprietary ontology.

That’s the difference. And I think it matters.