KORD
Problem
How it works
Validation
BUILD IN PROGRESS
Turn company knowledge into workflows AI can execute safely. Every time.
A live context graph + runtime that compiles workflows in deterministic bounded configs.
The Problem
Companies know how they work. Their software does not.
Critical knowledge is scattered
across CRMs, Slack, documents and people’s heads
Workflows need that knowledge
Which clients need attention? How are exceptions handled?
AI agents retrieve context → handoff to LLMs
= uncertain, un-auditable, untraceable output
The Engine
Live graph + workflow runtime
Live Context Graph
Updates continuously. Model actors, entities, typed edges, temporal state, confidence and evidence.
Workflow Configs
Workflows compile into bounded configs over graph levers -- which then become first class nodes.
Hybrid Execution
Deterministic when bounded. LLMs run inside configs when judgement is needed.
Input
Client had new life event
Graph
client → life_event
client → plan
advisor → client
Config
client_drift_detection_v1
Output
Flagged Client
Evidence
Followup
Validation
Kobble: Context Graph Validation
Challenge
To validate the engine before enterprise rollout, we needed real-world data that was continuous, explicit, unstructured, and workflow-triggering.
Dating mapped closely: users constantly express who they are, what they want, and how their preferences change.
~50k+
datapoints created
100k+
User/entity edges created
Outcome
We built Kobble, an interaction-first dating app, on top of the engine.
Every meaningful action created graph evidence; user preference inputs, profile interactions, and agent questions updated actor→entity edges and triggered configs for matching, filtering, and gap-filling.
5k+
workflows
80 → 900
organic user growth