Nektar + In-house LLM
You build the intelligence.
Nektar delivers the context.
Snowflake & an in-house LLM can power a great GTM agent. Here's where Nektar fits underneath it.
Your business logic should be yours.
The data plumbing doesn’t have to be.
Your qualification criteria, competitive intelligence, escalation rules, and agent workflows are unique to your business. Your team should own them.
You build:
- Models
- Agents
- Workflows
- Business logic
But capturing every customer interaction, resolving identities, removing duplicates, and keeping millions of records current is not a one-time engineering project. It is permanent infrastructure.
Nektar provides:
- Complete Data
- Structured context
- Identity resolution
- Continuous maintenance
Building the agent is only half the job.
Feeding it is the hard part.
Incomplete history
Emails, meetings, calls, and buyer activity remain scattered across systems. Your agent sees fragments of the relationship instead of the complete customer journey.
Broken identities
One buyer can exist across multiple records, domains, accounts, and opportunities. Without reliable identity resolution, your agent cannot tell who did what or where they belong.
Silent data decay
Contacts change roles. Accounts evolve. Opportunities move. A record that was accurate last month may already be misleading today.
Endless maintenance
Connectors break, schemas change, duplicates return, and sync jobs require constant monitoring. Your best engineers end up maintaining pipelines instead of improving the product.
Give your LLM the Customer Context it is missing
How Nektar fits in your stack
Capture every interaction
Bring emails, meetings, calls, transcripts, and other customer activity together without depending on reps to manually update the CRM.
Connect every relationship
Match each interaction to the correct contact, account, opportunity, and buying group so your agent understands the full relationship graph.
Structure the context
Transform raw communication data into usable signals such as engagement, stakeholder coverage, deal momentum, relationship strength, and risk.
Keep it updated
Continuously correct, enrich, and maintain records as customer relationships change. Your data stays usable long after the first sync.
Deliver it where you build
Send structured customer context into Salesforce, Snowflake, your data warehouse, or directly to internal agents through APIs and MCP.
What Teams Get Building on Nektar
Complete customer context
Your agents reason across the full history of each account and opportunity, not a handful of recent interactions.
More reliable outputs
Complete, structured data gives the model fewer gaps to fill with assumptions and reduces misleading recommendations.
Faster development
Engineers stop rebuilding ingestion, identity resolution, deduplication, and maintenance infrastructure from scratch.
One trusted data foundation
Salesforce, Snowflake, analytics, and internal agents all operate from the same continuously maintained customer context.
Built on Nektar. Trusted by AI-native companies
The companies winning with Revenue Intelligence use DAISY as the intelligence layer inside their AI agents.
Is Nektar the Right Fit?
Nektar fits if:
- You're building internal GTM agents or LLM applications
- Your models depend on Salesforce and customer interaction data
- Data quality is limiting the accuracy of your AI
- You need context across accounts, opportunities, contacts, and buying groups
- Your AI or data team exists for governance, not GTM data capture
You may not need Nektar yet if:
- Your AI does not depend on customer or CRM data
- Your dataset is small enough to maintain manually
- Data completeness and freshness are not affecting agent performance
Hear from our Customers
