AI automation

AI context and memory for workplace automation

Wayfile · Private work memory your AI agents can read and you can review

WatchingDraft scoreEstablished market · since 2020 · AI estimate

21PWAS score

Confidence

Based on 9 of 9 factors

Overview

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem: Knowledge workers struggle to automate repetitive tasks because AI agents lack context about what people actually do across their applications throughout the day. Existing solutions require manual context selection, painful integrations, or static configurations that don't capture the dynamic reality of work.
Solution concept: A system that continuously records screen activity and audio locally, indexing it into a searchable, AI-friendly memory that agents can access independently. Rather than requiring users to specify integrations or manually import context, the system automatically captures what is happening across all applications and makes that context available to AI agents to understand work patterns, find automation opportunities, and act autonomously without micromanagement.
Who has it: Knowledge workers and teams seeking to automate repetitive tasks; companies looking to discover and implement automation opportunities across their workflow.
How they handle it today: Today, people either manually feed context to AI agents through tool-calling integrations and MCP protocols, which require technical setup and ongoing micromanagement, or they manually select and import sources into persistent knowledge systems. Agents cannot independently understand what people are doing across different applications in real time.
Why now: Recent advances in model context windows, agent autonomy (skills, MCP), and on-device inference make it practical to store and query continuous work data locally. The shift from tool-calling to autonomous agents that maintain persistent knowledge creates new demand for automatic context capture rather than manual selection.
How it could charge: Freemium model with free tier for personal non-commercial use, paid plans for commercial users, and enterprise plan that includes services to help companies discover automation opportunities and handle data storage/hosting choices.

Score breakdown

A model sorted each factor into a rubric bucket; code turned buckets into numbers. Unknown factors are left out of the score and lower confidence instead.

Market validation

Confidence 95 · 2 evidence · Scattered interest shown by Hacker News engagement (88 points, 67 comments) and enterprise customer interest, but no established paid competitors in this specific niche of autonomous context capture for agents.

Revenue evidence

Confidence 70 · 1 evidence · Paid commercial licensing model exists with free tier for personal use, but no public claims of paying customers or revenue figures.

Growth

Confidence 95 · 1 evidence · The Hacker News discussion shows modest engagement and founder's prior work (Obsidian Ava plugin) demonstrates category interest, but insufficient evidence of fast-growing adoption or momentum.

Problem severity

Confidence 70 · 2 evidence · Knowledge workers lose productivity regularly due to inability to autonomously automate tasks without manual context setup, and user feedback identified multiple critical concerns (security, CPU, data quality, agent autonomy).

Economic proximity

Confidence 30 · Judged from the description · The product directly enables automation discovery and workflow optimization, which lead to cost savings and efficiency gains, placing it close to business value creation.

Distribution

Confidence 30 · Judged from the description · The product is a personal/internal tool for individual knowledge workers and companies to use internally; usage does not expose it to non-users outside the organization.

Buildability

Confidence 70 · 1 evidence · Requires complex integration across multiple applications, screen activity capture, indexing, and agent API access; the evidence notes that naive continuous recording with OCR is resource-intensive and insufficient.

Differentiation

Confidence 70 · 2 evidence · Clear gap identified by users: existing tool-calling, MCP, and knowledge systems all require manual context selection or micromanagement, while autonomous continuous capture solves this named problem.

Retention and timing

Confidence 30 · Judged from the description · The product enables recurring automation workflows and context retrieval, fitting into daily work patterns; timing aligns with growing adoption of AI agents in enterprises.

Risk penalties

Questionable data accessThe product continuously records screen activity and audio, capturing potentially sensitive company data, personal information, and proprietary content. User feedback explicitly flagged recording consent and local security as critical concerns.−15
Heavy regulationContinuous screen and audio recording of knowledge workers raises significant data protection, privacy, and potentially employment law concerns depending on jurisdiction and company policies.−9

Score history

21 · confidence 62 · config v1First score · 10 Oct 2026

Evidence · 9 items

Market validation

Hacker News post received 88 points and 67 comments
Verified
Hacker NewsRetrieved 10 Oct 2026
The product enables use cases including adding context to agent chats, retrieving task information, maintaining personal wikis, creating automations, and identifying automation opportunities.
AI inference
The model stated this without a quote found in the source.
Hacker NewsRetrieved 10 Oct 2026
Enterprise customers can use the product to discover automation opportunities across their work.
Public claim
We have a free tier, and other plans, including Enterprise which helps companies find automation opportunities.
Hacker NewsRetrieved 10 Oct 2026

Revenue evidence

Commercial use of the product requires a license; free tier available for personal non-commercial, nonprofit, educational, and research use.
Public claim
We have a free tier, and other plans, including Enterprise which helps companies find automation opportunities.
Hacker NewsRetrieved 10 Oct 2026

Problem severity

AI agents need context about what people do on computers across applications to work autonomously without constant micromanagement.
AI inference
The model stated this without a quote found in the source.
Hacker NewsRetrieved 10 Oct 2026
Prior user feedback highlighted recording consent, local security, CPU usage, signal-to-noise, and whether agents could act on the data as critical concerns.
Public claim
The most useful criticism concerned recording consent, local security, CPU usage, signal-to-noise, and whether agents could act on top of the data.
Hacker NewsRetrieved 10 Oct 2026

Differentiation

Existing tool-calling and MCP approaches for agent integration are insufficient: tool-calling is not autonomous enough and requires micromanagement, MCP is too static and difficult for non-technical users.
Public claim
Then there was tool calling so that AI can access software you use but still kinda not autonomous enough. needing micro management. Then MCP came, but it felt too static, and non technical users struggled to build and use MCP.
Hacker NewsRetrieved 10 Oct 2026
Persistent knowledge systems like LLM-maintained wikis still require manual source selection and importing rather than autonomous capture of what people do.
Public claim
There is still no way AI can know what you and your company are doing every day, across apps, not just inside of apps.
Hacker NewsRetrieved 10 Oct 2026
Naive continuous video recording with OCR is resource-intensive, creates duplicate data, and loses OS structure information.
Public claim
But that creates duplicate data, consumes substantial resources (it basically turns your computer into a space heater!), and discards structure the operating system already knows.
Hacker NewsRetrieved 10 Oct 2026

AI context and memory for workplace automation

Overview

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Verdict

Score breakdown

Risk penalties

Score history

Validation gate

Evidence · 9 items

Market validation

Revenue evidence

Problem severity

Differentiation

Market

Existing products

Build

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Community