Inside StandIn
We do not rely on magical inference.
We rely on structured, declared context.
Declared State vs. Observed Behavior
Most productivity AI attempts to infer context by observing behavior (tracking keystrokes, reading every DM, watching screen time). This creates noise, privacy anxiety, and hallucination.
StandIn rejects the "magical observer" pattern. We optimize for Declared State. We only know what a user explicitly publishes via a Wrap or a linked system of record (Jira, GitHub).
The Observer Model
Ingests everything. Guesses intent. High noise. Low trust.
The StandIn Model
Ingests only specific commits. Takes distinct orders (Wraps). Zero guessing. High trust.
Why integrations come first
A standalone destination app creates a new silo. StandIn is architected as an infrastructure layer that lives where the work happens.
Chat as Interface
We treat Slack and Teams as the operating system. StandIn is a headless service that renders its UI purely through channel messages and modals.
System of Record
We do not try to replace Jira or GitHub. We index them. StandIn acts as the connective tissue between the raw data (the PR) and the human context (the Wrap).
Representative Architecture
We do not use a single monolithic model. We use scoped "Representatives."
Personal Representative
Constructed from a single user's Wraps and assigned tasks.
Auth: Personal
Function: Handoff
Project Representative
Constructed from Jira tickets, PRs, and docs associated with a specific project key.
Auth: Team Level
Function: Status
Routing Representative
A lightweight graph that maps topics to owners. It doesn't answer; it redirects.
Auth: Public
Function: Triage
Why constraints scale trust
Adoption of AI tools usually stalls due to privacy concerns ("Is this training on my data?"). StandIn removes this friction by architectural constraint.
Ephemeral Context
Wraps decay. Information from 3 weeks ago is weighted significantly lower than information from today.
No Training on Customer Data
We use RAG (Retrieval Augmented Generation) on your specific index. We do not train base models on your proprietary code.
What this enables long term
When you decouple presence from context, you get resilient systems.
Sustainable Global Scaling: Hire the best person, regardless of timezone, without isolating them.
Higher Quality Meetings: When status is solved asynchronously, sync time is reserved for complex decision making.
Institutional Memory: Wraps create a searchable narrative history of your company that auto-generated logs cannot match.