Revenue Intelligence | Powered by DAISY
Your GTM AI Has a Data Problem.
DAISY Fixes It!
Trusted revenue signals computed from every customer interaction, written directly into Salesforce, Snowflake, and your AI stack. Automatically.
Revenue Intelligence | Powered by DAISY
Your GTM AI Has a Data Problem.
DAISY Fixes It!
Trusted revenue signals computed from every customer interaction, written directly into Salesforce, Snowflake, and your AI stack. Automatically.
The Data Exists.
The Answers Don't.
You bought the tools.
You built the workflows.
You even got the AI.
But Monday morning still starts with a Slack thread: “Does anyone know if this meeting actually happened?”
Because the data can’t be trusted.
“Which reps have real meetings this week?”, shouldn’t take hours of digging through Salesforce, Gong, and spreadsheets to answer.
Incomplete data, missing touchpoints, unclassified meetings, they don’t just limit your AI. They hallucinate it.
No flag. No alert. The renewal conversation started from behind because no one was watching the signals.
From Raw Signals to Trusted Revenue Intelligence
A signal layer that runs on your data, writing into Salesforce, feeding Snowflake, and powering every agent and forecast your team builds.
Every opportunity in your CRM gets a DAISY canvas. A full story of the deal, buying group, signals, risks, and next steps generated automatically.
No rep input required.
The companies winning with Revenue Intelligence use DAISY as the intelligence layer inside their AI agents.
$2M Expansion Revenue
$80M+ Pipeline Identified
200K+
New Activities Tracked
Mimecast built a GenAI application combining Nektar activity data, Gong transcripts, and product signals in Snowflake. AI platform adoption had been near zero. The week the Nektar data foundation went live, adoption became significant.
The data layer was the missing ingredient our entire AI investment was waiting for.
Tim Seamans
VP of Business Transformation & AI Acceleration
400+
Accounts Tracked
$80M+ Pipeline Identified
Brex’s GTM Ops team built a “narrative platform”, a single-pane app that aggregates Nektar activity data, Call transcripts, and support signals into contextualized next-best-actions for reps. Nektar powers the core data layer: buying groups, multi-threading scores, exec touchpoint recency, and meeting attendance. The CRO reviews the Nektar-powered dashboard daily.
Dylan Hughes
Head og GTM Systems
Without Nektar, we would not be able to do these things.
30+ pre-built signals that answer your GTM team’s questions, automatically.
Buyer visibility
Map and track relationships you didn’t know existed in CRM automatically.
Opportunity Contact Roles
Identify buying groups on your open and closed opportunities without any manual effort.
Persona Identification
Detect and tag contact influence such as decision maker, technical buyer, potential champion.
Meeting Type Classification
Distinguishes demos from reviews; maps buyer journey touchpoints automatically.
No-Show Detection
Distinguishes invitees from actual participants; flags relationship risks instantly.
+80% Fill Rate
2-3hrs Saved per Rep
Dan Jiao
VP RevOps & Strategy @ Signifyd
By far the most impactful tool to understand our buyers and their influence. Almost immediately we got the visibility we needed from a long time.
Deal velocity
Uncover what’s driving or slowing, deal momentum.
Multithreading and Powerthreading score
Measures breadth and seniority of buyer engagement; more power, more momentum.
Executive coverage
Tracks seller-side exec involvement; strong exec relationships can unblock deals and boost
Key Objections Summary
Summarizes objections raised in meetings; helps flag hidden blockers early.
Legal and Infosec Discussions
Tracks legal, security, and infosec discussions; strong late stage buying intent signal.
+50% Deal Velocity
+95% Data Accuracy
James Lee
VP GTM Strategy & Ops @ Writer
Nektar provides precise meeting data to optimize coverage and drive VP and C-suite conversations.
Forecast Accuracy
Use real engagement signals to validate deal health and stage progression.
Predicted deal stage
Stage prediction based on real activity; cuts through rep bias in forecasting.
Opportunity score
Rolls up signals into a health score; instantly spot which deals need attention.
Decision maker engagement ratio
Shows if decision makers are engaged or missing; key for deal progression.
Last meeting with the Champion
Tracks last touch with champion; helps understand the deal momentum.
+90% Accurate Forecast
+95% Data Accuracy
Sharath Keshava
CEO @ Sanas
Our commit deals had win rates of 50-60%, far from the expected 80-90%. We turned to Nektar to leverage AI-driven engagement insights, helping us uncover the true forecast category and identify gaps in our pipeline.
Marketing Attribution
Tie marketing efforts to pipeline creation and progression with better attribution.
Last touch before opportunity creation
Links marketing’s last touch to opp creation; know what drove real pipeline.
Last touch before meeting booked
Links marketing’s campaign to a new business meeting; tighten lead-to-meeting flow.
Number of marketing touches with Economic Buyer
Counts marketing touches with economic buyer; understand how marketing builds influence.
Number of marketing touches with Technical Buyer
Counts marketing touches with technical buyer; understand how marketing builds influence.
+90% Accurate Forecast
+95% Data Accuracy
Gar Smyth
VP of Revenue Marketing & Operations @ Mimecast
Nektar lets us tie marketing activities to real movement in the deal cycle, not just lead creation.
Exec Engagement
Don’t lose deals because reps didn’t engage with authority
Upcoming Exec-Level Meeting
Flags the next meeting with VP/C-level; ensures exec momentum isn’t lost before close.
Last Exec-Level Meeting
Captures the last C-level engagement; adds confidence in late-stage forecast.
In-person meeting with Exec
Surfaces last in-person exec meeting; adds confidence in late-stage forecast.
Economic Buyer Alignment
Ensures the decision maker is actively engaged.
+28% Win Rate
+60% In-person Meeting
Nidhish Patni
Head of Revenue Operations @ Whatfix
We've seen an average improvement in win rate is 28 ppts by tracking in-person meetings with execs
Rep Performance
Uncover how your reps sell and where they can improve.
Types of persona engaged
Breaks down which personas reps are meeting; are they talking to power?
Types of activities done
Shows what types of activities reps are doing; balance between strategy and effort.
Number of touches before first meeting
Measures effort to secure first meeting; useful for messaging and targeting refinement.
Number of touches before opportunity creation
Tracks effort needed to create pipeline; helps diagnose early-stage conversion gaps.
+17% Deal Velocity
+95% Data Accuracy
Alex Dyson
Senior Manager, Revenue Ops @ Signifyd
We have strong engagement metrics to identify key opportunities worth auditing. Nektar ensures accurate, polished data in Salesforce to drive better decisions.
Churn Risk
Track key stakeholder engagement, and moments that impact renewal and expansion.
Last Meeting with the Champion
Tracks last touch with champion; helps prevent churn from silent accounts.
Last Engagement with the Economic Buyer
Tracks last touch with economic buyer; helps prevent churn from silent accounts.
Last Business Review Meeting
Checks when the last QBR was held; no QBR, no expansion conversation.
Persona engaged in Business Review Meeting
Identifies who joined the QBR; exec presence = high renewal likelihood.
100% SE time attribution
+95% Data Accuracy
Archana Ranjan
Director, Sales Business Applications @ EnterpriseDB
In <30 days we were able to track quality and history of engagement in high churn risk accounts.
Presales ROI
Understand how SEs spend their time and which activities drive revenue.
Product positioned
Identifies which product is being discussed in meetings; helps align SE focus and forecast product-level interest.
Time spent on customer meetings
Measures how much time SEs spend with customers; optimize for top deals.
Time spent on high value deals
Shows if SEs are focusing on big deals; helps align resources with revenue.
Time spent on different activity types
Breaks down SE time across tasks (like Demo, RFP); know where to cut or double down.
+80% SE time attribution
1hr per SE per week saved
Alex Dyson
Senior Manager, Revenue Ops @ Signifyd
We are able to align SE bandwidth with real pipeline priorities.
Define your own logic in plain language, DAISY computes it across your entire interaction history and writes it into Salesforce.
Here’s what our customers have built:
0 Manual Tagging
430 Reps Tracked
Nektar gives me the critical GTM telemetry to reduce rep ramp time and grow exponentially.
Parm Uppal
Chief Revenue Officer
Deal velocity
Chainguard defines a qualifying NBM with five specific criteria, computed automatically from GTM Telemetry data.
- AE & SE both present in the meeting
- Opportunity is Stage 1 or Stage 2
- Minimum 3 buyer contacts in attendance
- Specific Buyer titles present (configured)
- Minimum meeting duration met
CRO reviews this signal every Monday for 430 reps. Zero manual tagging. Rep coaching decisions made from data, not intuition.
400+ Accounts Tracked
$80M+ Pipeline
Nektar's telemetry data became core KPIs reviewed daily by our CRO and Head of CSE.
Dylan Hughes
Head of GTM Systems
Last CSE touchpoint with Economic buyer
For every customer account, Brex tracks when a CSE last had a meaningful interaction with a CFO or head of finance.
- Identifies economic buyer persona automatically (CFO, Head of Finance)
- Computes most recent CSE-to-economic-buyer touchpoint date
- Writes date field to account record in Salesforce
- Updates continuously as new interactions are captured
Managers use it to identify CSE coverage gaps across 400+ accounts without opening a single report. One field, continuous insight.
100% From Transcripts
0 Manual Tagging
Nektar is helping us cut weekly logging hours while enhancing forecast accuracy and sales speed.
Abhishek Bhat
Director of Operations
Decision Delay Reason
Kognitos uses DAISY to capture why deals are getting delayed; distinguishing rep-driven delays from customer-driven delays, extracted from transcript context.
- Analyzes call transcripts for decision delay signals
- Distinguishes "rep hasn't followed up" from "customer has a reason"
- Writes reason text to opportunity-level long text field
- Tracks across all open opportunities continuously
Finally know why deals are actually stalling classified from every customer interaction automatically.
Daisy delivers what you need to know, automatically, in real-time, and straight into your workflow.
See How Daisy Uniquely Bridges the Gap Between GTM Execs and Scattered Data
Isolated emails and calls don’t tell the full story.
Daisy connects the dots across every touchpoint, giving GTM leaders the complete picture—highlighting moments that matter, without any noise.
Here’s how we do it.
Loved by revenue leaders everywhere
Trust their words, not just ours
See How Daisy Uniquely Bridges the Gap Between GTM Execs and Scattered Data
Isolated emails and calls don’t tell the full story.
Daisy connects the dots across every touchpoint, giving GTM leaders the complete picture—highlighting moments that matter, without any noise.
Here’s how we do it.
