Building context with Elemental
Built for mission-critical workflows, Elemental builds context engines that sit between your raw data and your AI agents.
It gives your agents 1000X the investigative power — without a cost increase.
Deploying agents for serious industries is proving tough.
Every AI workflow today reconstructs its own partial view of reality. A compliance agent builds one picture. A portfolio agent builds another. A research tool builds a third. These views rarely agree, and none persist. Analysts don't need more feeds, they need better context.
Agents fail on fragmented identities.
An entity can appear under various names in every system. Without global context, agents reason from conflicting and incomplete views of the same reality.
Outputs from models cannot be trusted.
If you cannot show how a conclusion was reached and what evidence supports it, no one will act on it. The work stays manual.
Costs explode with data volume.
Stuffing documents into prompts creates a broken tradeoff: an affordable agent that is ignorant, or an insightful agent too expensive to run.
Our platform, Elemental
Ingests global data streams in real time.
Unleashing agents on your data warehouse results in poor performance. Elemental transforms raw, fragmented data into context engines that are coherent and agent-navigable. This shared context allows humans and AI agents to operate from the same understanding of the world: who is involved, what is connected, what has changed, and why it matters.
Ensures every conclusion can be traced back to source.
A context engine organizes raw data into entities, relationships, events, and evidence over time, ensuring that every conclusion can be traced back to source and lineage. Context engines make agents viable for high-stakes, real-world work, rather than one-off demos or brittle copilots.
Eliminates the latency between data acquisition and exploitation.
A context engine organizes raw data into entities, relationships, events, and evidence over time, ensuring that every conclusion can be traced back to source and lineage. Context engines make agents viable for high-stakes, real-world work, rather than one-off demos or brittle copilots.
How Elemental works
Elemental uses AI agents to learn and ingest new data formats automatically. There are no fixed schemas to configure and no manual parsing rules to maintain. Built-in auditor agents verify every extraction against the source document before anything enters the graph.
Unlike traditional ETL, this pipeline uses agents to automate data discovery and quality assurance. Independent auditor agents verify extraction accuracy against source documents, resolving discrepancies through rigorous metric verification.
< 1 day
to ingest raw data
< 1 wk
to auto-develop data extractors
0
required human interaction
How Elemental works
Elemental uses AI agents to learn and ingest new data formats automatically. There are no fixed schemas to configure and no manual parsing rules to maintain. Built-in auditor agents verify every extraction against the source document before anything enters the graph.
Unlike traditional ETL, this pipeline uses agents to automate data discovery and quality assurance. Independent auditor agents verify extraction accuracy against source documents, resolving discrepancies through rigorous metric verification.
< 1 day
to ingest raw data
< 1 wk
to auto-develop data extractors
0
required human interaction
Enriched by The YottaGraph
The Lovelace YottaGraph is a continuously maintained world reference graph, built from authoritative open sources. It compresses yottabytes of raw data into trillions of structured facts with millisecond retrieval.
The YottaGraph augments your proprietary data. Your private data is processed separately and enriched with the YottaGraph, combining proprietary intelligence with global context. Public enrichment flows in. No customer data flows out.
Elemental closes the gap
Elemental provides the data integrity and structural mapping at the foundational level, so your agents become better, faster, and cheaper.
Better
Every agent reasons from the same shared context, not its own partial reconstruction of reality. When your data is mapped into a context engine and enhanced with Lovelace's YottaGraph, conclusions are both more powerful and consistent.
Faster
Context is built once. Intelligence compounds continuously. Your agents operate under standing instructions around the clock, triggering analysis only when meaningful thresholds are crossed. What matters surfaces in minutes, not the days or weeks reconciling sources manually.
Cheaper
Token-light graph queries replace document-stuffed prompts. As your data grows, performance improves without costs spiraling. Our infrastructure pricing replaces per-seat licensing, so costs scale with value delivered, not headcount.
Deployment options
Elemental is containerized and can be implemented against the deployment option that works best for your organization. It integrates with Palantir Foundry, Azure, AWS, internal UIs, and any system that can call an API. Headless and API-first.
Private Deployment
Elemental runs entirely within your environment. Your data never leaves your boundary.
Private + YottaGraph Augmentation
Private deployment enriched with global reference data. Global context without exposing proprietary information.
Managed Cloud
We operate the infrastructure. You get immediate access and the fastest path to production.
Security and compliance
Elemental is built on Google's Security Foundations Blueprint and runs on Google Cloud with all infrastructure managed through a GitOps model. Every change is version-controlled, peer-reviewed, and deployed through automated pipelines. Manual access and configuration drift are eliminated by design.
We protect sensitive workloads within private, zero-trust clusters, guarded by Workload Identity and CMEK encryption. Core systems operate at an enterprise-grade baseline, scaling modern tooling without compromising the integrity of your private network.
YottaGraph by the numbers
Ingested to date and growing daily.
35M
Entities (people, places, and things) mapped in the graph.
100M
Relationships mapped, with source citations for verifiable provenance.
2B
Attributes extracted and linked to entities.