Coworker AI

What Coworker AI does

Coworker.ai builds enterprise AI agents that deeply understand your work and company by creating a context graph. It optimizes tasks by pairing them with the right models and context, aiming to achieve 5x the work for the same spend. The platform offers conversational AI, artifact generation, frontier coding, and long-running agents with triggers across various systems. It features a portable context layer, an intelligent routing layer for models (open and closed), and enterprise-ready open-source models hosted in the US. Coworker.ai integrates with over 50 connectors, supporting both read and write operations, and inherits existing permissions. The platform is designed for enterprise scale, offering deployment, monitoring, and optimization of AI agents with features like Command Center for approvals and evaluations, Orchestration, Learned Context (OM1 Organizational Memory), and robust infrastructure with enterprise-grade security. It aims to solve challenges in reliability, scale, state management, cost reduction, governance, and rapid deployment for AI agents.

Products

• ChatConversational AI that works across tools, reasons through complexity, and takes action with approval. It pulls permissioned context from over 50 apps, cites sources, and offers a Deep Work mode for multi-step research and analysis.
• CoworkGenerates polished artifacts on demand, such as decks, docs, dashboards, financial models, branded PDFs, and interactive apps. These are editable, shareable, and exportable.
• CodeEnables frontier coding in a cloud sandbox, offering repo-aware multi-file edits, sandboxed execution, and full organizational context.
• AgentsLong-running, no-code agents that can be built in plain English with triggers across various systems like Slack, CRM, calendar, and email. They work across tools, reason through complexity, and await approval before acting.
• PlatformA comprehensive platform for deploying, monitoring, and optimizing enterprise-grade AI agents at scale. It includes Command Center, Orchestration, Learned Context (OM1), and multi-model, multi-cloud infrastructure.
• Organizational Memory (OM1/OM2)A knowledge graph that learns across connected data sources, shared skills, and artifact templates. It provides agents with persistent, structured, permission-aware recall for reasoning about business logic.
• Intelligent RoutingRoutes tasks to the optimal model (open or closed, frontier or fast) balancing cost, latency, and quality, ensuring automatic updates to better models without migration.
• Meeting notetakerProvides live transcription, summaries, and action items from Zoom, Meet, and Teams, syncing them to CRM and documents. Offers varying retention periods based on plan.
• Coworker MCPAllows any MCP-compatible AI tool (e.g., Claude Code, Cursor) to access the Coworker organization, read context, run agents, and write back to the stack.
• Customer Intelligence (CI)AI-enriched customer data for health scoring, expansion signals, churn risk, and auto-generated follow-up actions from CRM, support, and product signals.
• Fast ChatProvides instant answers within the Chat product.
• Deep WorkA feature within Chat that produces canvas documents with charts, tables, and structured analysis for multi-step research.

Key capabilities

• Context graph that deeply understands work and company
• Intelligent routing to the right model for every task
• Support for 50+ connectors (read and write)
• US-hosted models (open and closed)
• SOC 2 Type II certified
• Conversational AI with 50+ out-of-the-box connectors
• Polished artifact generation (decks, docs, dashboards, financial models, PDFs, apps)
• Frontier coding in cloud sandbox with multi-file edits and org context
• Long-running agents with triggers (Slack, CRM, calendar, email)
• Portable context layer
• Optimized Context (OM2 knowledge graph)
• Open-source frontier models made enterprise-ready
• Ability to pick models or let Coworker pick automatically
• Automatic updates to new models
• Agents for every function
• Free trial available
• Credit-based pricing model
• Custom polling frequency for triggers
• Tool-specific memory + Skills
• Cloud Sandbox for secure execution
• AI-enriched customer data (health scoring, expansion signals, churn risk)
• MCP (Multi-Cloud Platform) compatibility
• Triggers/Polling for connected tools
• Meeting notetaker with live transcription, summaries, action items
• Meeting retention (7, 30, 90 days)
• Organizational Memory (OM1) for persistent knowledge graph
• Individual Memory for user preferences and context
• Command Center for approvals, monitoring, evaluation, optimization
• Orchestration across channels, applications, workflows
• Learned Context (permissioned context, skills, memory)
• Multi-model, multi-cloud infrastructure
• Enterprise-grade security (HIPAA, GDPR, CASA Tier 2, OAuth 2.0)
• Reliability (99.7% output accuracy, 85%+ unstructured data handled)
• Scale (10K+ concurrent workflows, millions of rows processed)
• State management (persistent context retention, continuous learning)
• Cost reduction (60-80% cost reduction, 12+ model providers)
• Governance (100% audit coverage, configurable approval gates)
• Deployment (days to deploy, zero integration effort with Ambient Learning)
• Sub-second graph traversal for structured recall
• Context compounds over time for agents
• Guardrails, approvals, credit limits, escalation logic
• Source citations on every answer
• Deep Work mode for structured analysis
• Role-based access controls (RBAC)
• Enterprise SSO (SAML 2.0, OIDC)
• Multi-factor authentication
• AES-256 encryption at rest, TLS 1.3 in transit
• Zero-trust network architecture
• Regular penetration testing
• Vulnerability disclosure
• No training on customer data
• Regional data residency
• Data retention policies
• Full data processing agreements (DPA)
• 99.9% uptime SLA
• Multi-region, multi-cloud infrastructure
• Horizontal auto-scaling
• Disaster recovery (<1hr RPO, <4hr RTO)
• Human-in-the-loop approval gates
• Complete audit trails
• Real-time monitoring dashboards and alerting
• Configurable guardrails, rate limits, kill switches
• Cloud, private cloud, or on-premise deployment
• VPC peering and private endpoints
• Air-gapped environments
• Bring your own model (BYOM) support

Use cases

• Answering questions across every system (Chat)
• Generating polished artifacts (decks, docs, dashboards, financial models, PDFs, apps)
• Frontier coding with multi-file edits and sandboxed execution
• Building long-running agents with triggers
• Summarizing Acme renewal status from Salesforce, Gong, Slack
• Pipeline Hygiene Agent (trigger on 'Negotiation' stage for 14+ days, pull call transcripts, post summary + next-best-action to Slack)
• Product: Synthesizing feedback, tracking competitors, syncing docs
• Product: Feature Analyzer (synthesizing feedback, creating Jira stories)
• Product: Feedback Synthesizer (scanning Slack, Intercom, Gong for product feedback)
• Product: Sprint Summary Generation (from Jira, PRs, standup notes)
• Product: Competitive Intelligence (surfacing trend reports from competitor mentions)
• Product: Roadmap Sync (updating Confluence/Notion when Jira epics change status)
• Product: Feature Request Tracking (mapping requests to accounts/revenue, notifying when shipped)
• Product: Release Notes Generation (drafting from PRs, Jira tickets)
• Product: Automating feedback synthesis, sprint reporting, competitive intelligence monitoring, roadmap syncing, release notes generation, feature adoption tracking, user research session analysis
• Sales: Researching prospects, auto-updating Salesforce, drafting personalized follow-ups
• Sales: Account Management agent (expansion pipeline, outreach, escalation)
• Sales: Pre-call Intelligence Brief (assembling brief from CRM, LinkedIn, past meetings, support tickets, product usage)
• Sales: Auto CRM Update (after calls, notes, action items, deal stage changes, next steps flow into Salesforce)
• Sales: Personalized Follow-Up (drafting emails referencing discussion points)
• Sales: Pipeline Health Monitoring (flagging stale deals, past-due close dates, missing next steps)
• Sales: Expansion Revenue Detection (surfacing accounts with buying signals, drafting outreach)
• Sales: Deal Risk Early Warning (flagging when champion goes silent, competitor mentioned, engagement drops)
• Engineering: Summarizing PRs, linking Jira tickets, suggesting reviewers
• Engineering: Turning Slack bug reports into Jira tickets
• Engineering: Keeping documentation up to date with API/repo changes
• Engineering: Bug Triage (creating Jira tickets from Slack reports, deduping, assigning)
• Engineering: PR Context Assembly (linking PRs to Jira, past incidents, relevant reviewers)
• Engineering: Incident Coordination (pulling runbooks, surfacing past incidents, paging on-call, starting timeline)
• Engineering: Sprint Summary Generation (from Jira, PRs, standup transcripts)
• Engineering: Doc Staleness Detection (flagging/drafting updates for outdated docs)
• Engineering: Deployment Risk Analysis (reviewing change set, flagging blast radius, checking incidents, test gaps)
• Customer Success: Synthesizing customer signals (CRM, calls, Slack, support, usage, billing) into unified view
• Customer Success: Surfacing churn risks and expansion opportunities
• Customer Success: Auto-generating QBR decks
• Customer Success: Account Management agent (expansion proposal, case study, QBR, churn intervention, upsell discussion)
• Customer Success: Post-Meeting CRM Update (after Gong call, updating Salesforce with outcomes, next steps, health score changes)
• Support: Drafting ticket responses, auto-categorizing/routing tickets, surfacing trending issues
• Marketing: Drafting campaign briefs, generating content calendars, analyzing campaign performance
• People: Auto-answering benefits/policy questions, generating onboarding checklists, summarizing engagement survey results
• Data: Answering business questions in plain English, auto-generating weekly metric reports, monitoring KPIs
• Operations: Morning briefings, automating weekly status reports, tracking project dependencies
• Financial Services: KYC & KYB automation, regulatory filing automation, audit trail monitoring, risk assessment intelligence
• Financial Services: Prospect account research, RFP & proposal response, trade settlement monitoring, client communication drafting
• Financial Services: Advisor support automation, knowledge base management, client inquiry resolution, support analytics
• Financial Services: Portfolio performance reporting, market intelligence briefings, fraud detection acceleration, regulatory change monitoring
• Healthcare: Intelligent scheduling, prior authorization automation, referral management, patient communication
• Healthcare: Claims processing acceleration, denial management, coding assistance, payment reconciliation
• Healthcare: HIPAA compliance monitoring, quality measure tracking, regulatory documentation, credentialing automation
• Healthcare: Clinical documentation improvement, care coordination, medical supply management, research protocol support
• Salesforce Integration: Post-meeting CRM updates, stale deal detection, new contact enrichment, pipeline hygiene
• HubSpot Integration: Post-meeting contact updates, lead enrichment, deal risk alerts, sequence enrollment
• Pipedrive Integration: Post-meeting updates, stale deal alerts, contact enrichment, pipeline hygiene
• Ecom Nation: Automating meeting transcription and summary generation, creating contextual connections between meeting content and business data, providing unified search capabilities, maintaining granular access controls.

AI approach

Coworker.ai provides enterprise AI agents that leverage a context graph (OM2) and an intelligent routing layer to pair tasks with the right models and context. They integrate with over 50 tools and offer features like conversational AI, artifact generation, code execution in a sandbox, and long-running agents. They use both open and closed models, hosting them in the US, and allow users to pick models or let Coworker choose the optimal one. Their Organizational Memory (OM1) layer provides a persistent knowledge graph for agents to learn and improve.

Tech named: LLMs, large-language models, generative AI, AI, enterprise AI, context graph, OM2, intelligent routing layer, open-source models, Anthropic Opus 4.7, Anthropic Sonnet 4.6, Anthropic Haiku 4.5, OpenAI GPT 5.5, OpenAI GPT 5, OpenAI ChatGPT 5.2, Google Gemini 3.1 Pro, Google Gemini 3.5 Flash, Moonshot Kimi K2.6, Moonshot Kimi K2.7, Z.ai GLM 5.2, OM1 Organizational Memory, Claude Code, Cursor, MCP, Llama, Mistral

Industries served

• Technology, Information and Internet
• B2B SaaS
• Financial Services
• Healthcare
• Professional Services
• Retail & E-commerce
• Government
• Telecommunications
• Marketplaces
• Mobile Gaming

What it says sets it apart

• Right model, right context, any task approach
• Context graph that deeply understands work and company
• 5x the work for the same spend by pairing tasks with optimal models/context
• Enterprise-ready with SOC 2, 50+ connectors, US-hosted models
• Integrates with every surface (Chat, Cowork, Code, Agents)
• Portable context layer, intelligent routing layer, enterprise-ready open-source models
• Routes across closed and open models, automatically updating to better ones without lock-in or migration
• Engineers/power users can pick models, or Coworker can pick
• 50+ native integrations with read and write capabilities, inheriting permissions
• Agents work across tools, reason through complexity, and wait for approval
• Organizational Memory (OM1) provides persistent knowledge graph for agents
• Addresses all dimensions of production-grade agents: Reliability, Scale, State, Cost, Governance, Deployment
• Ambient Learning allows agents to observe and replicate existing workflows without integration projects
• OM1 provides sub-second graph traversal for structured recall, unlike slow, sequential, stateless tool calling
• Context compounds over time, making agents smarter with every interaction
• Goes beyond chat to perform actions (updates CRM, creates tickets, sends follow-ups, drafts reports)
• Learns how users work in minutes by ingesting history (emails, tickets, deals)
• Unified context from 50+ apps ensures grounded, not generic, answers
• Source citations on every answer for verification
• Deep Work mode for multi-step research and structured analysis
• Guardrails, approvals, credit limits, escalation logic, full audit trails
• Lives where you work (Slack, meetings, boards), not another tab
• Built to be enterprise-ready from day one (security, privacy, compliance foundational)
• Zero data retention for connected tools
• Custom integrations via REST API and Coworker MCP server for enterprise customers
• Automates administrative work that consumes significant time for sales reps (60%)
• Reduces manual review by 70% for KYC/KYB in financial services
• Achieves 90% faster fraud detection in financial services
• Ensures 100% on-time regulatory filings in financial services
• 70% auto-processed prior authorizations in healthcare
• 50% fewer patient no-shows in healthcare
• 35% faster claims payments in healthcare
• Automates meeting transcription and summary generation, saving 2-3 hours per week per team member (Ecom Nation case study)
• Eliminates separate transcription tool costs (Ecom Nation case study)
• 30-40% less time searching for information (Ecom Nation case study)
• Supports sophisticated multilingual workflows (Huuuge case study)
• Reduced IT operations weekly inventory reconciliation from 4-6 hours to 20-30 minutes (92% efficiency gain) (Huuuge case study)
• Reduced meeting-to-policy pipeline from 8-12 hours to 45-60 minutes (90% time reduction) (Huuuge case study)
• Custom AI report generation and personalized daily intelligence delivery at scale (BobbAI case study)
• Fully operational, white-labelled AI intelligence layer embedded directly into a Scottish Government-backed entrepreneur support platform (BobbAI case study)