SOLUTIONS
The data layer beneath every AI-ready revenue command center
Most CRMs are missing 80% of customer interactions and the data was never built to be trusted by software making decisions on revenue.
Nektar is the layer that fixes it. Not another UI. Not another tool sitting next to Salesforce. The infrastructure that makes Salesforce safe enough for AI to act on.
Find your way in
Start with what you already know: the outcome you need, the team you're on, or the stack you run.
01
By Outcome
You have a named problem. Forecast is off. Buying groups are invisible. Agents are hallucinating. Jump to the use case.
02
By Team
You're the one being asked to fix it. RevOps. CRO. CAIO. Head of CSOps. Each page is written from your seat.
03
By Stack
You're already running something. Find out where Nektar fits, what it replaces, and what it makes stronger.
By Outcome
Six problems revenue teams hire Nektar to solve.
Buying Group Intelligence
Surface the entire buying committee from real engagement, not from form fills or guesses.
AI Agents That Don't Hallucinate
Fill the CRM data gaps with complete, current, correctly mapped activity data your AI agents can actually trust.
Capture every email, calendar invite & meeting and write it to the right Salesforce object. Zero rep adoption required.
Surface every influenced contact and tie inbound source to the opportunity, so attribution reflects what actually happened, not what got logged.
By Team
Five roles we hear from most. Each page is written from their seat, not ours.
PRIMARY BUYER
RevOps
You're the one who knows the data is broken. You're also the one who'll get blamed if AI rollout fails because of it.
ECONOMIC BUYER
Sales Leadership
Your forecast accuracy, pipeline coverage, and rep coaching all live or die on activity data. If AI for revenue is on your roadmap, this is the prerequisite.
PRIMARY BUYER
AI & Data Teams
You're being asked to put agents on top of CRM data nobody trusts. Or told to "use Snowflake and an LLM" without anyone solving the data layer first.
ADJACENT BUYER
MarOps
You're attributing campaigns to opportunities where the contact you nurtured isn't even on the opp. We fix the missing layer of attribution.
ADJACENT BUYER
CSOps
Renewal forecasts, health scores, and churn risk built on CSM activity nobody manually logs. The same telemetry sales gets, applied post-sale.
By Stack
You already run something. Here's where Nektar fits: what it replaces, what it augments, and what it makes stronger.
Salesforce + Snowflake
The modern enterprise stack. Nektar is the activity and engagement layer that bridges them: telemetry written to both, in real time.
Salesforce + Gong
Gong captures the call. Nektar captures everything else: emails, calendars, attendees, and ensures it all writes to the right opportunity.
Salesforce + Clari
Clari forecasts. Nektar makes sure Clari is forecasting on complete data instead of the ~20% of activity that gets manually captured.
Salesforce + Salesloft / Outreach
Cadence tools capture their own touches. Nektar captures everything they miss, dedupes the overlap, and makes Salesforce the actual source of truth.
Snowflake-first (headless)
No UI. No rep adoption layer. Just the telemetry, structured and written to your warehouse. For teams building their own GTM apps and agents.
Building it yourself?
An honest cost-and-tradeoff comparison for teams considering Snowflake + LLM in-house. Where it makes sense, where it breaks, and what we'd build first.
How to know if you're ready for Nektar
The companies that get the most out of us share a profile. If three of four apply, let's talk. If not yet, we'll tell you honestly, and point you at what's actually useful.
You have a named AI or revenue-data initiative on your roadmap
Agentforce. Command center. Revenue copilot. GTM super-app. It's funded, it has an exec sponsor, and someone (maybe you) is on the hook to make it work.
You've already tried activity capture before.
EAC, Gong sync, Clari Autocapture, People.ai. One of them is in your stack today and the data still isn't where it needs to be. You're not exploring, you're replacing.
Your data team has built or considered building this in Snowflake.
You know what it would take. You'd rather not. It's 12–18 months of infrastructure work before you get to the thing you actually wanted to build.
You're 500+ employees on Salesforce, with a real RevOps function.
Smaller teams can use Nektar, but the value compounds with stack complexity and headcount.
What's underneath all of this
The matching engine. The self-healing architecture. Five years of training data on enterprise GTM activity. The Salesforce-native writeback. SOC2, ISO 27001, CASA Tier 2 Security.
These aren't features. They're why every outcome above is possible.