The opinionated layer between your customer data and your business
Most companies don't have a data problem — they have a context problem. Birdie sits between your data lakes, CRM, support tools, and AI stack — with an opinion on how every customer signal should connect to move the business.
You have the data. You still can't move the business.
Enterprises sit on more customer data than ever — data lakes, CRM, support tools, product analytics, surveys, reviews, transcripts. The gap is no longer collection. It's turning that data into a connected, opinionated, business-ready signal that anyone in the company can act on.
Data without context
Surveys in one system. Calls in another. Tickets in a third. Behavior in the warehouse. Each tells a fragment of the story, and stitching them together is a multi-quarter analyst project — that drifts the moment requirements change.
Context without an opinion
Even when data is centralized, raw aggregation is not intelligence. Without a persistent taxonomy and a strong opinion on how signals connect, every team — and every LLM — reinterprets the same data and produces a different conclusion.
Opinion without action
And even when there's signal and opinion, the last mile is what kills ROI: getting the right insight to the right team, owned, in the right system, at the right horizon, with the impact measured at the end. Most "AI for CX" stops at slide 1.
A platform with an opinion on how customer signals should connect
Birdie is not another data aggregator. It's the context layer between your existing systems and your business outcomes — built on a persistent taxonomy, cross-source joins, and a strong point of view on how every customer signal should map to a decision someone can own.
Data lakes & systems
Snowflake
BigQuery
Salesforce
HubSpot
Zendesk
Intercom
Qualtrics
App reviews
Calls / transcripts
Product analytics
Surveys / NPS
+ 80 sources
Context platform with opinion
Persistent taxonomy
Cross-source joins
Root-cause model
Business-impact attribution
Action ownership
Governed AI
Auditable trail
Decisions & outcomes
Roadmap evidence
Coaching plans
Process fixes
AI agent context (MCP)
Executive briefings
Retention models
QBR & board reports
How Birdie turns raw customer signals into decisions, actions, and outcomes.
A platform, not a stack of applications. Every layer was designed to do one thing well and to compound with the layer above it. Step through each stage to see what sits inside the box.
The Company
Where signals originate, and where outcomes return.
Your Organization
Customers
Prospects
Internal Teams
External Systems
Data Sources
Raw signals from across the business — every system that holds a piece of the customer truth.
Customer Interaction
What customers and prospects say. Tickets, chats, calls, emails, reviews.
Zendesk
Intercom
SF Service Cloud
Customer Behavioral
What customers do. Product events, cohorts, feature usage, drop-offs, errors.
Amplitude
Mixpanel
Google Analytics
Customer Profile
Who the customer is. Account info, plan, region, ARR, lifecycle stage.
Salesforce
HubSpot
Stripe
Company Events
What happened on our side. Incidents, outages, releases, experiments.
PagerDuty
Datadog
GitHub
Company Initiatives
What we intentionally did. Bug fixes, feature improvements, agent coaching, process changes.
Jira
Linear
Asana
Company Knowledge
Who the company is and how it operates. Products, goals, team, policies, processes, internal language.
Notion
Confluence
Google Drive
Company Metrics
How success is measured. Churn, retention, NPS, revenue, cost-to-serve.
Looker
Tableau
Snowflake
External Market
What's happening outside the company. Competitor news, regulatory changes, market events.
Google News
Gov & Regulatory
G2
Extractions
Structured understanding extracted from raw data — the first pass that makes everything below it possible.
Predefined Extractions
Standard, reusable enrichments applied to every signal.
PII detection & removal
Transcription (audio/video → text)
Language detection & translation
Summarization
Noise & duplication removal
Intent classification
Entity extraction
Makes raw data safe, readable, and structurally usable.
Flexible Extractions
Customer- & business-specific meaning.
Themes
Opportunities
Areas
Journeys
App reviews
Custom categories & signals
Adapts to how each company understands its customers. AI with human in the loop.
Customer Decision Graph
The system of record for customer context and decision-making. Where every signal becomes a connected, ownable, prioritized decision.
Unified Customer Context
Relationship Mapping
Impact Attribution
Prioritization Engine
Temporal Reasoning
Relationships modeled:
Customers × Interactions × Behavior × Events × Initiatives × Metrics
API
MCP
SDK
Consumption Layer
Where context becomes action. Two ways anything in your stack — human or AI — can plug in.
Agents
Agents consume context, not raw data.
Applications
Outcome-oriented products built on top of the platform.
Continuous Learning Loop
Decisions and actions feed back into the business — and back into the graph — creating continuous learning loops the next cycle inherits.
Key Platform Principles
Platform ≠ Infrastructure
Decision Graph is the core
Apps own the outcomes
APIs expose decisions, not data
See Birdie in action.
See how Birdie turns customer signals into retention, expansion, and adoption decisions. 30 minutes. Live demo with outcomes.
Two macro use cases today. Anything you can imagine on top
Birdie's customers run Voice of Customer and Agent QA as their first two use cases — both powered by the same context layer. Through MCP and our API, the same platform supports every custom use case your team builds next.
Voice of Customer
Turn every customer signal into ranked, owned, and quantified product, process, and people decisions — across every channel and every cohort.
faster feedback analysis cycle
reduction in contact rate (Patreon)
Agent QA & AI Quality
Score 100% of human and AI interactions against what actually matters to customers — and turn QA from a sampling exercise into a real-time coaching and governance system.
interaction coverage, human + AI
reduction in detractors (Neon)
Whatever you build next
The same context layer that powers VoC and Agent QA is exposed to your AI agents, copilots, and data team — so they can build use cases tailored to your operation, not bound by our roadmap.
process once, reuse everywhere
use cases your team can build on top
A few of the use cases already running on Birdie.
Every example below is a real workflow customers run on the platform today — across CX, Product, Ops, CS, and the exec floor. None of them required new infrastructure. All of them ride on the same context layer.
Diagnose why NPS dropped this month
Identify the 3–5 root causes behind a CSAT/NPS move across every channel — ranked by volume and business impact — in minutes, not days.
Quantify demand behind feature requests
Stop debating roadmap based on the loudest customer. See which requests carry the most revenue, the most ARR at risk, and the most detractor signal.
Detect churn signals in customer feedback
Surface accounts whose feedback is trending negative before health scores catch up — with the evidence ready for the team that needs to act on it.
Audit AI agent and chatbot quality
Catch your AI agents resolving tickets that customers re-open with a human. Score AI interactions against customer outcomes, not deflection rates.
Detect emerging issues before they escalate
Catch a small complaint trending into a 200-ticket fire at 20 tickets, with the root cause and the owner already identified.
Detect upsell and expansion signals
Customers hit a limit and tell support. Now your sales team knows — automatically — with the context and the conversation ready in their CRM.
Generate board-ready customer narrative
Walk into the QBR with the customer story told in data — what moved, what didn't, what's at risk, and what the platform is recommending next.
Find your highest cost-to-serve ticket types
When leadership says "cut support costs 15%," know exactly which ticket types are driving cost — and which fix at the source removes them entirely.
Coach agents with customer-outcome evidence
Stop coaching against a generic scorecard. Coach against the specific behaviors that customers tell you broke the experience — with the evidence attached.
Mine real customer quotes for marketing
Need quotes for the case study, the launch deck, or the website? Birdie surfaces real customer language — sourced, attributed, and ready to use.
Diagnose why NPS dropped this month
Identify the 3–5 root causes behind a CSAT/NPS move across every channel — ranked by volume and business impact — in minutes, not days.
Detect churn signals in customer feedback
Surface accounts whose feedback is trending negative before health scores catch up — with the evidence ready for the team that needs to act on it.
Audit AI agent and chatbot quality
Catch your AI agents resolving tickets that customers re-open with a human. Score AI interactions against customer outcomes, not deflection rates.
Detect emerging issues before they escalate
Catch a small complaint trending into a 200-ticket fire at 20 tickets, with the root cause and the owner already identified.
Find your highest cost-to-serve ticket types
When leadership says "cut support costs 15%," know exactly which ticket types are driving cost — and which fix at the source removes them entirely.
Coach agents with customer-outcome evidence
Stop coaching against a generic scorecard. Coach against the specific behaviors that customers tell you broke the experience — with the evidence attached.
Quantify demand behind feature requests
Stop debating roadmap based on the loudest customer. See which requests carry the most revenue, the most ARR at risk, and the most detractor signal.
Detect churn signals in customer feedback
Surface accounts whose feedback is trending negative before health scores catch up — with the evidence ready for the team that needs to act on it.
Coach agents with customer-outcome evidence
Stop coaching against a generic scorecard. Coach against the specific behaviors that customers tell you broke the experience — with the evidence attached.
Find your highest cost-to-serve ticket types
When leadership says "cut support costs 15%," know exactly which ticket types are driving cost — and which fix at the source removes them entirely.
Coach agents with customer-outcome evidence
Stop coaching against a generic scorecard. Coach against the specific behaviors that customers tell you broke the experience — with the evidence attached.
Detect upsell and expansion signals
Customers hit a limit and tell support. Now your sales team knows — automatically — with the context and the conversation ready in their CRM.
Mine real customer quotes for marketing
Need quotes for the case study, the launch deck, or the website? Birdie surfaces real customer language — sourced, attributed, and ready to use.
Generate board-ready customer narrative
Walk into the QBR with the customer story told in data — what moved, what didn't, what's at risk, and what the platform is recommending next.
You could build this in-house. Most don't reach expected ROI.
A pre-structured, opinionated context layer is not a feature you bolt onto an LLM. It's infrastructure — and one that gets structurally harder, not cheaper, as your signal volume grows.
Voice of Customer
Turn every customer signal into ranked, owned, and quantified product, process, and people decisions — across every channel and every cohort.
Every query re-interprets your customer signals from scratch. Same question, different answer each run.
The model spends its tokens opening files and normalizing formats instead of reasoning.
3–8 join operations per question. Different result every time.
Taxonomy drift, accuracy decay, model cost — none of them get cheaper at scale.
build timeline to a working in-house solution
people required — still likely to fall short
signal threshold where DIY architectures start to break
of internal AI builds miss expected ROI*
The infrastructure your AI was missing
A pre-structured context layer purpose-built for customer signals at enterprise scale. Process every signal once, with opinion — then use it everywhere.
A persistent, adaptive taxonomy trained on your domain — every signal classified before any LLM touches it.
Because structuring is handled in advance, your LLM arrives at a pre-prepared context and spends its budget on actual analysis.
Birdie pre-joins Support, CRM, NPS, calls, reviews, and product usage — deterministic answers across 100% of your data.
The platform is built for the 500K+ signal volumes where in-house architectures fail.
to first structured layer, not 12 months
infrastructure your team has to staff and maintain
data coverage on every query, not 10% samples
process once — used by every app, team & agent
One context layer. Every team and every AI agent that touches the customer.
The same opinionated layer powers humans and AI alike. Each team gets the queue they need, in the format that drives their decisions.
Built for enterprises that can't afford to get it wrong
Security & Compliance
Birdie is built to the standards of regulated fintech and healthcare environments, anywhere in the world. Your customer data is encrypted, access-controlled, and audit-logged.
Accuracy & Transparency
We publish F1 scores. We show you model cards. We're explicit about accuracy limitations and edge cases. You know exactly what works, what doesn't, and why.
Availability & Support
99.9% uptime SLA. Dedicated enterprise support. Your decisions don't stop because your platform stopped. When you need us, we're here.
From signal to execution in one workflow.
Birdie connects to the systems where signals originate and the tools where work happens. Signals flow in from Zendesk, Slack, surveys, and reviews. Birdie diagnoses them. Decisions flow out to Jira, Asana, and your AI agents — with full context.
See IntegrationsWhat makes Birdie different from other VoC tools?
Most tools give you insights. Birdie gives you a system connecting signals to decisions, decisions to actions, and actions to measurable impact.
How does Birdie prioritize what to fix first?
Birdie ranks opportunities based on their impact on churn, revenue, and cost, so teams focus on what actually moves the business.
How long does it take to start seeing results?
Most teams start seeing measurable impact within weeks, not months, as the system begins identifying and prioritizing key issues.
Does Birdie integrate with our existing tools?
Yes. Birdie connects with the tools where signals originate and where teams operate, ensuring insights flow directly into action.
How does Birdie handle AI accuracy and customization?
Models are transparent, explainable, and trained on your business context with the ability to validate and refine outputs over time.
See Birdie in action.
See how Birdie turns customer signals into retention, expansion, and adoption decisions. 30 minutes. Live demo with outcomes.
