Buried on arrival
Data teams estimate the majority of enterprise data is never read again after it lands. Every silo is another headstone: the lake, the warehouse, the SaaS exports — graveyards with a catalog.
You paid to land every byte — into the lake, the warehouse, and a dozen SaaS silos. They became data graveyards: none of it can answer a question. Not for your analysts, not for your copilots, not for the AI agents you're deploying this year. XERJ.AI activates enterprise data where it lives — one unified search layer inside your data pipeline. No migration to a search cloud. No copy of your data in someone else's account. And nobody — including us — charges you to store your own data.
The lake was supposed to democratize data. What it actually did was democratize storage: the modern data stack stores everything and answers nothing. Petabytes land every day — and become write-only. The warehouse answers the SQL you planned for last quarter; nothing answers the question your incident channel is asking right now. And the moment someone says "can we search it?", the answer is a six-month project: ETL pipelines, a managed search cluster in someone else's cloud, per-GB pricing on a second copy of data you already own.
Data teams estimate the majority of enterprise data is never read again after it lands. Every silo is another headstone: the lake, the warehouse, the SaaS exports — graveyards with a catalog.
Your copilots and agents can see the internet, but not your own Parquet, your Salesforce history, or last night's logs. Enterprise AI is blind exactly where it should be smartest.
Cloud search services charge you to move your data, then to store it again, then per node to query it. You pay three times to ask your own data a question.
Snowflake separated compute from storage and rebuilt the warehouse around that idea. XERJ.AI separates search from storage and rebuilds enterprise search around it: the activation layer deploys inside your pipeline — your VPC, your on-prem racks, even air-gapped — and indexes data where it already lives. The index sits beside the data. The data itself never moves.
No ETL-to-search pipelines. No second copy in a vendor cloud. Your storage bill stays your storage bill — S3, HDFS, disk, wherever the bytes already rest.
The activation layer is a stage in your data pipeline, not a destination you export to. Land data as you do today; it becomes searchable as a side effect.
You're charged for what XERJ.AI does — activation and answers — never for bytes at rest. Storing your own data is not a billable event.
Point XERJ.AI at what you have: lake buckets, warehouse exports, file shares, databases, log streams — shipping today. Managed connectors for Salesforce, Workday, ServiceNow, and SharePoint are the first wave of the early-access program.
Zero-config discovery does what your ingestion team used to: content-sniffs every format (never trusts extensions), infers types and date encodings, detects cross-dataset join keys, and writes a self-describing data map. In an adversarial evaluation on a 1,995-file mixed corpus it passed 80 of 81 ground-truth checks — with zero per-corpus configuration.
One API for keyword, semantic, vector, and hybrid search plus built-in agent memory — Elasticsearch wire-compatible, so the dashboards, clients, and muscle memory your teams already have work unchanged. Your AI agents orient themselves via the data map and machine-readable docs; no human briefing required.
Every enterprise AI initiative hits the same wall: the model is capable, but the data is dark. XERJ.AI was designed with the AI agent as the primary user — not an afterthought. Agents get a data map that answers "what is in here?", retrieval that works without an external embedding service, durable memory across sessions, and documentation written to be read by machines. Activation is what turns "we have a copilot" into "our copilot knows our business."
Most search architectures make governance hard because the data moves. XERJ.AI makes it tractable because it doesn't: the index lives beside the data, inside your boundary. What crosses the wire is answers — not your corpus.
Deploys in your VPC, on-prem, or fully air-gapped. One static binary, no JVM, no phone-home. If your network can't reach us, XERJ.AI still works.
API-key and basic auth, TLS, per-namespace isolation for agent memory, and an insecure-by-explicit-flag-only posture for dev.
RBAC with field-level controls, SSO/SAML, audit trails, and connector-level governance (respecting source-system permissions for Salesforce & co.) are being built with design partners — not announced after the fact.
XERJ.AI is built on the open-source XERJ engine (Apache-2.0) — a from-scratch Rust engine, not an Elasticsearch fork. Its benchmarks are published wins and losses both, with root causes; its durability claims are enforced by adversarial crash testing; its docs state what is lexical vs neural, exact vs approximate. You can read every line of the engine your data runs on. That is the level of honesty your procurement and your security team should demand from anyone touching your data.
The early-access program is deliberately small: teams with a real dark-data problem — a lake nobody can search, an AI initiative starved of enterprise context, a search bill that scales with bytes instead of value. We activate one of your real datasets, in your environment, in the first session. Design partners shape the connector and governance roadmap.