Enterprise & Knowledge Work

Enterprise AI agents fail not because they lack capability, but because they lack context. They don’t know how your team works, how your experts think, or what your standards are. mem[v] gives enterprise agents personal and organizational intelligence - so they learn team culture, capture expert knowledge, and adapt to real workflows instead of generic templates.

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Why Enterprise Agents Fall Short

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Use Cases

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1. Capturing Expert Knowledge Before It Walks

Your VP of Product Strategy, Maria, is leaving for a competitor. She’s made the last 8 successful product launch decisions. She knows which customer segments to prioritize, which metrics actually matter, and how to position against competitors. Standard approach: Exit interview, maybe some documentation. Most of her expertise evaporates. mem[v] approach: For 6 months, mem[v] has captured how Maria evaluates launch opportunities - her comments in docs, her questions in strategy reviews, her edits to positioning decks, her Slack reactions to campaign ideas. This becomes an “Maria expert model.” When your new PM asks, “Should we prioritize SMB or enterprise for this launch?” - the agent can reason through the decision using Maria’s captured patterns, referencing the 3 launches where she made similar calls and explaining her reasoning framework. What mem[v] delivers:

• Expert decision pattern extraction from documents, edits, and comments
• Reasoning framework capture (not just facts, but how experts think)
• Historical precedent linking (this situation resembles Case X where Expert Y decided Z)
• Cross-format knowledge synthesis (Slack + Docs + Meetings + Presentations)

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2. Team-Specific Agent Personalization

Your company has 4 regional marketing teams. EMEA team uses formal tone, prioritizes compliance, and runs 6-week campaign cycles. APAC team is experimental, ships weekly, and optimizes for viral reach. LATAM team focuses on influencer partnerships. North America team is data-driven, A/B tests everything. A generic “marketing agent” serves nobody well. Each team needs an agent that understands their workflow, their metrics, and their voice. What mem[v] delivers:

• Team-specific language model adaptation (formal vs. experimental tone)
• Workflow-aware suggestions (6-week EMEA cycles vs. weekly APAC sprints)
• Metric prioritization matching team KPIs (compliance score vs. viral coefficient)
• Tool integration reflecting actual team stack (different dashboards, approval systems)

Example in action: EMEA team asks: “Draft campaign brief for Q2 product launch”

• Agent uses formal tone, includes compliance checklist, structures for 6-week timeline, references past EMEA successes

APAC team asks the same question:

• Agent uses conversational tone, suggests 3 fast-test variations, optimizes for shareable moments, references recent viral wins

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3. Strategy Memos That Sound Like Your Best Strategist

Your CEO needs a strategic analysis: “Should we expand into the healthcare vertical?” A generic LLM produces surface-level analysis with standard frameworks (TAM, competition, SWOT). It reads like a consultant deck, not your company’s voice. mem[v] has learned from 40 strategy memos written by your Chief Strategy Officer over 3 years. It knows:

• She always starts with customer pain validation before market size
• She references specific competitive moves, not just “Porter’s Five Forces”
• She ties recommendations to your company’s actual capabilities, not theoretical strengths
• She includes a “Why Now?” section citing market timing signals
• She uses first-person plural (“we”) and company-specific terminology

The agent-drafted memo reads like her work. It’s 80% usable with light edits instead of a blank-page rewrite. What mem[v] delivers:

• Writing style transfer from top performers to agent outputs
• Company-specific reasoning patterns and frameworks
• Internal terminology and acronym usage accuracy
• Structural templates learned from best-performing documents
• Contextual decision criteria matching organizational priorities

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4. Omnichannel Support Memory

Your customer, Acme Corp, has contacted support 14 times in the past 6 months. They’ve talked to 6 different agents across email, chat, and phone. Each time, they repeat their setup, explain their use case, and re-describe their integration challenges. Agent 7 opens the ticket. Without memory: “How can I help you today?” With mem[v]: The agent sees Acme’s complete history - not just ticket text, but the context. They’re a $400K/year enterprise customer. They’re using a legacy API version. Their technical contact, James, prefers detailed documentation over video calls. They’ve escalated twice when given surface-level troubleshooting. They’re in renewal discussions next quarter. The agent’s first message: “Hi James - I see this is related to the webhook timeout issues you mentioned last week. I’ve pulled the logs from your production environment and identified three potential causes. Given your setup, I’d recommend…” What mem[v] delivers:

• Cross-channel conversation memory (email → chat → phone continuity)
• Customer context beyond tickets (contract value, renewal timing, escalation history)
• Contact-specific preferences (communication style, technical depth)
• Historical issue patterns (recurring problems, past solutions that worked/failed)
• Early warning signals (satisfaction trends, repeat escalations)

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Cross-Platform Intelligence

mem[v] builds unified knowledge graphs across your entire tool stack:

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Context Graphs: Organizational Memory That Compounds

Traditional knowledge bases are filing cabinets. mem[v] builds living context graphs where organizational memory grows exponentially over time through semantic connections, not linear storage. How Context Graphs Differ: Standard wiki or doc repository: Search returns individual documents. You read them one by one, making connections manually. mem[v] context graphs: Search returns interconnected knowledge clusters. Maria’s strategy memo automatically surfaces the 3 customer interviews that informed it, the Slack debate where the team challenged her assumptions, the prior quarter’s competitive analysis she referenced, and the subsequent product decisions that validated her thesis. Memory Compounding in Action: Month 1: Your product team discusses entering the healthcare vertical. mem[v] captures the conversation, links to participants, and indexes key concerns raised. Month 4: Sales team has a healthcare prospect call. The agent surfaces the product team’s earlier analysis without anyone manually connecting them. Month 7: A healthcare competitor announces a feature. mem[v] links this to both prior discussions, identifies gaps in your original analysis, and surfaces relevant team members to consult. Month 12: New PM asks “Should we build for healthcare?” The agent provides a complete decision context built from 12 months of compounding organizational memory - not just isolated documents, but the full reasoning chain, evolving perspectives, and updated market signals. What makes context graphs compound: The Compounding Effect:

• Year 1: 1,000 conversations captured → 3,500 semantic connections formed
• Year 2: 2,400 conversations captured → 12,000 semantic connections formed (includes connections to Year 1)
• Year 3: 3,800 conversations captured → 34,000 semantic connections formed

Knowledge value grows exponentially. New information connects to existing context, enriching both. Every strategic discussion benefits from the full history of related thinking, not just what people remember to search for.

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Enterprise-Grade Security & Governance

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Business Impact

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Getting Started

Talk to Founders

Build enterprise agents that actually understand how your organization works.