CHRONTEXT · THE SYSTEM OF CONTEXT

The AI-native context layer for enterprise data.

Turn fragmented enterprise data into Chrons — chronological, entity-centric event streams. The long-term memory for enterprise AI and agents.

// one event universe -- re-centered on any entity
01Problem

Enterprise AI is starving for context

Your data is scattered across warehouses, lakes, and dozens of SaaS apps. Transformers and agents reason over sequences in time — but enterprise data lives in disconnected tables and snapshots.

So AI/ML teams rebuild the same brittle, incomplete, leak-prone timelines by hand — inconsistent, slow, and impossible to reuse. The bottleneck to AI value isn't the model. It's getting the data AI-ready.

Fragmented today
Data LakeWarehouseERPCRM BillingSupportProductSaaS Apps
02Solution | New primitive: The Chron

One universe. Any lens. Time as a first-class axis.

A Chron is a chronologically ordered, timestamped event stream centered on any entity — customer, rep, shipment, SKU, machine, agent. Store the event universe once; project it per entity. Two serving surfaces: real-time context for inference, and leakage-free, point-in-time exports for training.

// store once → project per entity

Entity-lens projection

The entity is a variable. The same universe re-centers on whichever entity you choose — Chrons stay consistent, with no copies to drift.

// delta_prev · interval

Cadence is signal

The gaps and acceleration between events are captured natively, not discarded. Shrinking deltas are a “vibe shift” that AI transformers can attend to.

// as_of — point-in-time

Leakage-free truth

Every attribute as it was, when it was. No future facts bleed into training or inference — reproducible, audit-ready serving.

// bitemporal · append-only

As-of-correct history

Best-effort backfill, as-of-correct from connection forward. We mark unknown rather than fabricate the past — and Chrons sharpen as we accumulate.

03Interactive demo

See the timeline a transformer sees

The Entity Lens viewer is an interactive product feature, live. Pick an entity and watch one event universe re-center as that entity's Chron — then view the same Chron three ways:

  • Timeline — the cross-system event stream, for humans.
  • JSON — the canonical Chron, for models.
  • Text — the LLM-ready context, for AI.

Drop in your email + AI context needs and it opens in a new view — no demo call required.

Open the interactive demo

One Chron, every serialization.

0/500
We'll email you only about Chrontext. No list-selling, ever.
04Beachhead Use Case · Churn → Retention

The churn you can't see coming

Customers don't churn overnight — they degrade across systems. A usage dip here, a support ticket there, sentiment slipping, billing slowing. No single tool sees it at once. It's the boiling-frog problem.

01

The Chron makes the whole cross-system timeline legible to a transformer — surfacing subtle vibe shifts well before the cancel point.

02

A thin, use-case oriented, churn-risk endpoint and app built on general-purpose Chron primitive for the technology-light enterprise buyer.

03

The value is net revenue retention — hard dollars saved, CFO-legible. By a founder who has built for such use cases multiple times before.

Acme Corp · cadence
Shrinking gaps = the vibe shift
without Chrontext with Chrontext Δ 9d Δ 3d Δ 1.5d usage_dip ticket_opened ticket_escalated ⚡ agent intercept cancel point churned retained saved
05Why now · why it compounds

Agents arrived. The data structure didn't.

Data warehouses and lakes were built for BI and dashboards — dimensional databases were never for feeding attention-based models. The open Chron spec is designed for this and adoption is the goal. Here's what compounds underneath it:

Identity graph

Bitemporal & append-only

As-of-correct entity resolution across every source — more accurate and more valuable with every event ingested.

Data moat

Your connected data is the asset

Leakage-free point-in-time truth only accumulates by being there as it happened. Every day a customer is connected widens this gap.

The unit AI retrieves

The context primitive

The Chron is what your RAG and agent stack retrieves for context. We make RAG better, not obsolete. Own the unit, own the workflow.

Own the standard

Open spec. Commercial engine.

The Chron spec and a reference SDK go open — every integration is a new seed. The accumulating identity graph, connected data and entity-Chrons stay in the engine.

open Chron spec · public release coming
06Team

Built this very thing before

AS
Anvesh Sati
Founder & CEO
ShopifyWayfairHome DepotStaples
Connect on LinkedIn ↗
  • Built Shopify's first at-scale GenAI CS agent for a 2,000-person support org — the beachhead use case, shipped.
  • At Wayfair, built ML stack behind $800M ad spend; lead scoring for a 400-person sales org and churn prevention for an 2000-person CS team.
  • 0→1 build of Home Depot's in-sourced ad-server; doubled Home Depot's ad-network revenue to $150M; tripled ad margin.
  • Multiple patents in data & ML/AI space. Technical founder with investment-banking & finance background.

“I know this buyer, this pain, and how to ship this.”

07Next

If your AI roadmap is moving slower than your ambition — let's talk.

Interested teams

Explore Chrons for your data

Build the context substrate your enterprise AI workflow will need. Let us walk you through the demo and explore next steps.

Get in touch
Enterprises & teams

Become a founding design partner

Shape the roadmap on founding-partner terms. Preferential access to the platform and direct channel to the product teams.

Design-partner conversations open·Live interactive demos·Use-case endpoints & apps