Every Employee Deserves an AI That Knows Their Work
Transform organizational productivity by giving each team member a Personal Language Model that understands their projects, remembers their decisions, and learns their work patterns.
The Enterprise Knowledge Problem
Organizations invest heavily in AI assistants, but generic tools can't access the context that makes responses truly valuable.
The Irony of Enterprise AI
Companies deploy AI assistants to improve productivity, but those assistants start every interaction with zero knowledge of the employee's role, projects, or organizational context. The employee must repeatedly explain what the AI should already know.
Personal Language Models at Scale
Each employee gets their own PLM - an AI layer that captures their work context and augments any LLM with relevant personal and organizational knowledge.
Each PLM layer captures individual context while shared knowledge bases provide organizational consistency. The underlying LLM can be swapped without losing accumulated context.
Enterprise Use Cases
Personal Language Models transform how teams work across every function.
Software Engineering
Each developer's PLM knows their codebase, past PRs, architectural decisions, and coding patterns. Code review suggestions reference their actual implementation history, not generic best practices.
- Context-aware code completion
- PR descriptions that reference past decisions
- Bug fixes informed by historical patterns
- Onboarding accelerated by codebase knowledge
Sales & Customer Success
Sales reps get AI that knows their pipeline, past client interactions, and deal history. Prepare for calls with context pulled from CRM, emails, and previous meeting notes automatically.
- Meeting prep with full relationship history
- Proposal generation using past winning patterns
- Follow-up emails that reference specific discussions
- Account insights synthesized across touchpoints
Customer Support
Support agents get AI that knows the product deeply and remembers how they've resolved similar issues before. Faster resolution with consistent quality across the team.
- Instant access to relevant past resolutions
- Response templates adapted to agent style
- Escalation with full context preservation
- Knowledge base gaps identified automatically
Research & Analysis
Analysts build AI assistants that remember every report, data source, and methodology they've used. New analyses build on institutional knowledge rather than starting fresh.
- Literature review with prior research context
- Methodology consistency across projects
- Data source recommendations from history
- Report drafts using established frameworks
ROI Model
Conservative estimates based on knowledge worker productivity research.
| Productivity Improvement | Mechanism | Est. Time Saved |
|---|---|---|
| Reduced context re-establishment | PLM provides context automatically instead of manual explanation | 45 min/day |
| Faster information retrieval | Semantic search across personal knowledge vs. manual searching | 30 min/day |
| Improved response quality | Personalized AI responses require fewer iterations | 20 min/day |
| Reduced onboarding friction | New employees inherit relevant organizational knowledge | 15 min/day |
| Total | 1.8 hrs/day |
Example: 100-Person Engineering Team
At 1.8 hours saved per day × 100 engineers × 250 working days × $75/hour loaded cost = $3.4M annual productivity gain. Even at 25% of estimated savings, the ROI is substantial.
Security & Compliance
Enterprise deployments require rigorous security. Memory is designed for it.
Data Isolation
Each employee's PLM is fully isolated. Cross-user data access is architecturally impossible.
On-Premise Option
Deploy entirely within your infrastructure. No data leaves your network.
Audit Logging
Complete audit trail of all memory operations for compliance requirements.
Role-Based Access
Granular permissions for administrators, managers, and end users.
Local SLM Option
Run inference on local GPUs. Sensitive data never touches external APIs.
SOC 2 Ready
Architecture designed with SOC 2 Type II compliance requirements in mind.
Deployment Options
Choose the deployment model that fits your security and operational requirements.
Cloud Hosted
Fully managed deployment with automatic updates and scaling.
- Managed infrastructure
- Automatic backups
- 99.9% SLA available
- SSO integration
Private Cloud
Dedicated instances in your preferred cloud provider's region.
- Your VPC, your region
- Network isolation
- Custom retention policies
- Bring your own LLM API
On-Premise
Complete deployment within your data center infrastructure.
- No external data transfer
- Air-gapped option
- Local GPU inference
- Full source access
Integration Ecosystem
Connect to the tools your teams already use.
Communication
Slack, Microsoft Teams, Gmail, Outlook. Capture the context from where work actually happens.
Development
GitHub, GitLab, Jira, Linear. Code, PRs, issues, and technical discussions.
Knowledge
Notion, Confluence, SharePoint, Google Drive. Organizational documentation and wikis.
CRM & Sales
Salesforce, HubSpot, Pipedrive. Customer relationships and deal context.
Interested in Enterprise PLM?
Memory is currently an open source research project. Enterprise deployment requires additional infrastructure, security hardening, and integration work. If you're interested in exploring this for your organization, let's discuss.