Coworker is an enterprise AI platform that routes each task to the right model with the right context. Teams get frontier-quality outputs at about 82% lower cost per task, and run roughly 5x more inference per dollar than they would on direct Claude or OpenAI consumption.
Overview. Coworker is built for companies whose AI usage is growing faster than their AI budget. As frontier model adoption has scaled, customers face a tradeoff between slowing down adoption and cutting headcount to control spend. Coworker takes a different approach. It routes across open and frontier models and pairs each task with persistent organizational context, which cuts cost per task without giving up output quality. Coworker works alongside the tools teams already use. Customers connect their stack with no-code OAuth and Coworker starts building organizational context right away. About a minute after sign-up, users see a summary of their work, a map of their key collaborators, three to five role-tuned agents, and a suggested first task.
Key Features. Intelligent Model Routing sends each task to the model that can handle it most efficiently. Meeting summaries go to lower-cost models like Kimi K2.6 (about $1.71 per million tokens), while data analysis and other complex work go to frontier models like Claude Opus 4 (about $10 per million tokens). Cost per task is fully visible to customers. OM1 Organizational Memory is a persistent memory layer that learns how each customer's company operates: people, projects, customers, processes, and workflows follow every task, no matter which model runs it, and that context is portable across models, so customers don't have to re-onboard when new models ship. Chat Mode is a fast, conversational interface for questions, lookups, summaries, and exploratory work, with simple visualizations rendered in a text canvas. Build Mode turns any chat thread into formatted deliverables like PDFs, spreadsheets, dashboards, and interactive artifacts, keeping full context from Chat Mode so customers can go from exploring an idea to a finished deliverable without starting over. Agent Builder sets up role-tuned agents at onboarding, and customers can build additional agents to automate recurring work across their connected systems. Self-Serve Onboarding lets you sign up, OAuth your tools, and get value in about a minute, with no sales-led implementation required.
Use Cases. Sales teams use Coworker for account and prospect research, deal health, context-aware follow-ups, objection handling, and call coaching grounded in CRM and meeting data. Customer Success teams get health scoring across product usage, tickets, and meeting sentiment, plus early churn signals, automated QBR prep, and less repetitive CS workload. Engineering and Product teams get customer feedback and bug themes pulled together, release notes drafted for them, PR review help, and docs, FAQs, and incident summaries sourced from GitHub, Jira, Slack, and internal docs. Leadership and Operations get real-time visibility into team priorities, project status, blockers, and people-management context without chasing updates across five tools. For Internal Knowledge, Coworker answers policy and process questions from the customer's actual Notion, Drive, Confluence, and Slack history.
Integrations. Coworker connects to existing tools with no-code OAuth, including Slack, Gmail, and Google Calendar for communication; HubSpot, Salesforce, and Gong for revenue and CRM; GitHub, Jira, and Linear for engineering and product; Google Drive, Notion, and Confluence for docs and knowledge; and Zendesk and Intercom for support. The more sources customers connect, the richer OM1 gets and the better Coworker's outputs become.
Pricing. Coworker offers a self-serve plan with usage-based pricing, plus team and enterprise tiers for larger deployments. See coworker.ai for current pricing.
Security and Compliance. Coworker is SOC 2 Type 2, CASA Tier 2, and GDPR compliant, independently audited across 193 tests and 20 controls. Customer data is partitioned at the network level and Coworker does not train models on customer data. The platform respects the underlying permissions of every connected app, so users only see what they already have access to in the source system. Admins can whitelist non-sensitive data to specific groups when it makes sense to break down silos.
Why Customers Choose Coworker. About 82% lower cost per task than direct frontier model consumption, roughly 5x more inference for the same monthly AI spend, persistent organizational memory across all models, self-serve onboarding with value in about a minute, per-task cost visibility, SOC 2 Type 2, CASA Tier 2, and GDPR compliance, and works alongside existing tools with no migrations required.
Average Rating: 4.9/5.0
Total Reviews: 11
Who Is the Company Behind Coworker?
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Seller: Coworker.ai
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Company Website:
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Year Founded: 2024
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HQ Location: San Francisco, US
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Twitter: @coworkerapp
497 Twitter followers
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LinkedIn® Page: www.linkedin.com
30 employees on LinkedIn®
Who Uses This Product?
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Company Size: 64% Medium, 36% Small
