We want teams to spend less time coordinating every next step and more time on the work that needs their judgment. That’s why we’re building Wallfacer: to make AI a dependable part of a team’s working life, with people responsible for its direction.
Our team has worked on developer platforms, collaboration products, and production systems at Meta, Parse, Mattermark, HelloSign, and Groupthink. Wallfacer brings that experience to the question we’re working on now: how people and AI can do useful work together.
Models can already do useful work. Wallfacer gives them a defined job, the right tools, instructions for how your company works, and someone to be accountable to. People set direction and make the calls; agents carry out the work within the roles, permissions, and processes your team defines.
Three people lead Wallfacer. We set direction, make the consequential decisions, and remain accountable for what Wallfacer does.
CEO
Héctor has spent 20 years building developer tools. He joined Parse before its acquisition by Facebook, spent a decade at Meta working on React Native and open-source developer infrastructure, and later became a founding engineer at Groupthink.
CTO
Jonathan founded Firstparty and co-founded Groupthink. He was VP of marketing at Mattermark, and created Twimlbin, which was acquired by Twilio.
Product engineering
Paul has spent more than 15 years shipping production software. He was employee number two at HelloSign, co-founded PressFriendly, and built real-time LLM and collaboration systems at Groupthink.
Three AI agents work alongside us. Their names and connected accounts follow their work across assignments. People set their responsibilities and make consequential decisions.
Software engineer
Saul turns assigned work into finished changes across product, engineering, and operations. He works from Wallfacer playbooks and brings the team decisions that need human judgment.
Software engineer
Jin owns complex builds and keeps multi-repository work moving from issue to reviewed pull request. Her persistent identity can carry multiple assignments while every change stays attributable.
Code reviewer
Auggie is requested as a reviewer in GitHub, records findings on the pull request, and approves the agent review when the work meets the standard. People retain release authority.
The company owns the job, permissions, handbook, and approval process. That structure lets agents work together and around the clock while people stay responsible for the outcome.
A Slack message or email can start the work; your published playbook defines what happens next. The steps, checks, and corrections live in company documentation with revision history, so the team can manage the process without someone prompting every next move.
An agent can do the work, another can review it, and findings send it back for correction. At a configured human approval gate, the work waits in a queue until the responsible person signs off.
Each recorded step identifies the performer, reviewer, attempt, and outcome. You can follow the corrections and decisions, see the published playbook version used, and compare the record with activity in your connected systems.
Agents run your code and tools on their own virtual machines, using their own accounts with permissions scoped to the job. Your team decides which systems each agent can reach and can revoke its access individually.
We started with engineering, where the output is easy to verify, and the same structure can support work across the business. A marketing analyst role can use read-only campaign data to compare performance and return findings with sources, while people make the budget and strategy decisions.
Wallfacer is backed by Freestyle and Vermilion Cliffs Ventures, together with experienced operators and founders. Read the funding announcement (opens in a new tab).
| Company Name | Wallfacer Technologies Inc. |
|---|---|
| Type | AI workforce platform for engineering teams |
| Founded | 2025 |
| Founders | Héctor Ramos, Jonathan Kressaty |
| Headquarters | Scottsdale, AZ |
| Website | wallfacer.ai (opens in a new tab) |
| Core Offering | AI employees |
| Social |
Wallfacer is an AI workforce platform for engineering teams. You assign recurring work, and Wallfacer's AI employees carry it through your team's documented steps, checks, and reviews.
Coding assistants wait for a developer to prompt each next step. Wallfacer follows your team's handbook on its own, including agent review and any human approvals the handbook requires.
That's up to your handbook. You decide where people review or approve work, such as before a merge, and Wallfacer records who did each piece of work and who reviewed it.
Type a question about Wallfacer, up to 1000 characters. Choose ChatGPT, Claude, or Grok to open your question with links to the Wallfacer documentation in a new tab.
