Predictive Strategic Intelligence: Signal Before You Search
Storytell isn't a knowledge management system—it's a predictive strategic intelligence engine that transforms how organizations anticipate, act, and adapt.
Written By DROdio
Last updated 16 days ago
Read our White paper: Predictive Strategic Intelligence
ℹ️ This is a deep dive on Storytell’s technical capabilities for those that want to fully understand how Storytell works.
Storytell.ai is the Predictive Strategic Intelligence Platform
Traditional tools make you ask questions. Storytell answers questions you didn't know to ask.
Storytell.ai transforms how organizations work by:
Enabling Predictive Intelligence: Moving from reactive "search and pull" to proactive "signal before you search"
Accelerating Decision-Making: Reducing analysis time from weeks to hours with multi-source synthesis powered by autonomous agent orchestration
Ensuring Data Integrity: Providing inline citations and attribution so you can go back to the source
Scaling Expertise: Making institutional knowledge accessible and actionable across teams through automatic knowledge graph generation
Perfect information delivered as signal, not noise, pushed to the people who need it, before they know to ask.
The Paradigm Shift: From "Human Pull" to "Agentic Push"
Read our White paper on Predictive Strategic Intelligence
Every company has 85-90% of its strategic data trapped in unstructured formats—customer calls, Slack threads, support tickets, meeting notes, emails, documents. This data contains the signals that predict churn, reveal product-market fit issues, identify revenue opportunities, and expose operational risks. But these insights remain invisible because they're scattered across disconnected systems.
Historically, humans had to know what questions to ask of the data—they had to write a query and "pull" the answers out of the data. This is an especially big problem in the enterprise, because humans work in silos and "don't know what they don't know" to ask about.
Storytell has a better approach: We use agents to "push" signal to the humans that need it before they even know to ask for it.
Storytell's Technical Foundation: Multi-Agent Orchestration with Tools and Skills
At the core of Storytell's capabilities is a sophisticated three-layer architecture that enables unprecedented analytical scale and flexibility:
The Three-Layer Architecture
1. Agents: Specialized LLM Workers
Agents are independent LLM instances, each with its own 200,000+ token context window and specific instructions. Each agent operates autonomously, making decisions about which actions to take and reporting results back to the main orchestrator.
What makes agents unique: Agents are differentiated by the tools they can access and the specific instructions that guide their behavior.
2. Tools: Executable Functions
Tools are runtime functions that agents invoke to perform concrete actions. When an agent needs to generate an image, search a database, create a chart, or find an email address, it calls a tool. The control plane executes the tool and returns results to the agent.
The principle: "Everything is a tool"—all actions agents take, including deploying other agents, happen through tool invocation.
3. Skills: Instructional Enhancements
Skills are purely instructional—they contain no executable code. Skills inject specialized knowledge and behavioral guidelines into agent contexts, modifying how agents think and respond without any runtime execution.
Key distinction: Tools execute actions (runtime), Skills modify behavior (instructions).
Storytell's Multi-Agent Orchestration System
Storytell features an advanced multi-agent orchestration system that automatically handles complex research tasks by intelligently deploying specialized AI agents. This system represents a fundamental shift in how AI assistants work—instead of a single AI attempting to process everything within limited context windows, Storytell can spin up multiple specialized agents to divide and conquer complex tasks.
Agent Types: Specialized Workers for Different Tasks
Storytell implements five specialized agent types, each optimized for specific workloads:
@general - The Main Orchestrator
Access: All available tools
Specialization: None—maximally flexible
When it's used: Default agent for standard interactions and orchestration
Role: When you interact with Storytell without explicitly requesting specialized agents, you're communicating with the General agent. It serves as the main orchestrator that deploys other specialized agents
@planner - Strategic Planning Specialist
Access: Planning and research tools
Specialization: Task decomposition and strategy formulation
When it's used: Complex tasks requiring structured planning
Role: Analyzes requests, determines optimal approaches, breaks work into subtasks, and creates execution plans
@explorer - External Intelligence Agent
Access: Web search, news, research papers, company search, social media
Specialization: External information gathering
When it's used: Market research, competitive intelligence, web-based research
Role: Exclusively searches external sources to gather intelligence from beyond your knowledge base
@librarian - Internal Knowledge Agent
Access: Knowledge base search tools
Specialization: Internal document analysis
When it's used: Analyzing uploaded documents, extracting insights from private data
Role: Operates exclusively within your private document repositories and uploaded files
@researcher - Comprehensive Research Agent
Access: Combined internal + external tools (superset of Explorer and Librarian)
Specialization: Multi-source comprehensive research
When it's used: Tasks requiring both internal and external intelligence
Role: Can simultaneously access both your internal knowledge base and external web sources. Instead of requiring two separate agents (Explorer + Librarian), a single Researcher agent can do both
Automatic Complexity Evaluation
By default, Storytell evaluates the complexity of your prompt and automatically decides whether agents are needed, and if so, how many. This happens behind the scenes without any user intervention.
When Storytell Automatically Uses Agents:
Deep research tasks requiring extensive information gathering
Complex tasks that would exceed context window limits
Prompts that require exploring multiple information sources simultaneously
Tasks that benefit from parallel processing
For simpler tasks, Storytell works in "single intelligent agent mode"—processing your request directly without spinning up additional agents. Most standard tasks continue to work in the traditional single-thread manner.
How the Multi-Agent Orchestration Works
When you ask Storytell to analyze dozens of companies, research complex topics across multiple sources, or synthesize information from hundreds of documents, the system automatically operates through three sophisticated stages:
Stage 1: Master Planning & Task Decomposition
Main orchestrator agent (General agent) analyzes user request
Determines optimal approach and breaks complex tasks into smaller, manageable subtasks
Identifies required tools and data sources for each subtask
Creates execution plan determining how many specialized agents to deploy
Each subtask is assigned to a dedicated agent with specific responsibilities
Stage 2: Parallel Agent Deployment & Execution
Agent Independence: Each deployed agent operates as a dedicated entity with its own context window (200,000+ tokens) and specialized toolset
Specialized Execution: Agents are purpose-built for their assigned tasks—research agents focus on specific data subsets, librarians search internal docs, explorers scan external sources
Autonomous Tool Selection: Each agent independently selects and executes appropriate tools from its vocabulary
Parallel Processing: Multiple agents execute simultaneously, dramatically reducing wall-clock time
Progress Reporting: Agents report back incremental progress and summaries as they complete their work
Stage 3: Result Aggregation & Synthesis
Main orchestrator agent collects results from all deployed agents as they complete tasks
Synthesizes information from parallel research streams
Generates comprehensive final answers integrating all findings
Produces detailed reports in requested formats (tables, summaries, presentations, etc.)
Context Window Management: Solving the Token Limitation Problem
Traditional AI systems struggle with context window limitations—attempting to hold all information in a single conversation quickly exhausts available tokens.
Storytell's solution: Offload 90% of context to specialized agents
Main agent orchestrates with minimal context
Specialized agents each get 200,000+ tokens for deep analysis
Sub-agents return summaries rather than full results to main agent
Parallel workers handle the cognitive load
This architecture enables Storytell to process datasets and conduct research that would be impossible in single-context systems.
User Control Options
Storytell gives you complete control over agent usage:
Automatic Mode (Default): System intelligently decides if/when agents are needed
Force Agent Usage: Add "use agents" to any prompt to explicitly trigger agent mode
Specify Agent Types: Use @mentions to select specific agents (@explorer, @librarian, @researcher, @planner)
Disable Agents: Turn off agent mode entirely through settings or explicit instruction ("do not use agents")
Visual Feedback
When agents are working, you'll see:
Progress Messages: "Agents are working on your request" notification
Agent Icon Indicator: Visual confirmation that agents were used
Expandable Progress Sections: Real-time view of agent activities, tool calls, and task status
Collapsed Task Cards: Organized view of completed agent work
Storytell's Tool Ecosystem
Tools are the executable functions that agents invoke to accomplish tasks. Every action Storytell takes—from generating images to searching databases to deploying more agents—happens through tool invocation.
Core Communication Tools
answer: Delivers final responses to users
reasoning: Displays reasoning processes in the UI (what you see when Storytell "thinks")
ask_user: Prompts users for clarifications
progress: Enables agents to communicate status updates
Content Generation Tools
dall-e: Generates images using OpenAI's DALL-E model
gemini-imagen: Generates photorealistic images using Google's Imagen
banana: Alternative image generation tool
chart: Creates data visualizations and charts
presentation: Generates slide-based presentations
report: Produces downloadable documents in Markdown and PDF formats
Research & Knowledge Tools
knowledge_base: Searches internal uploaded documents
web_search: Searches external web sources with category-specific intelligence
web_page: Fetches and analyzes specific web pages
scout: Finds professional email addresses and contact information
Orchestration & Meta Tools
task: Deploys and orchestrates specialized agents (agents are deployed via this tool)
discover: Dynamically retrieves tool specifications on-demand to reduce token usage
summary: Generates condensed summaries (used by agents internally)
skills: Loads skill instructions into agent context
Dynamic Tool Discovery
Storytell implements an intelligent tool discovery mechanism to minimize token consumption. Rather than loading all tool descriptions into every context (which previously consumed 3,500-4,000 tokens), agents now:
Load only 6-7 essential tools by default (answer, reasoning, etc.)
When additional capabilities are needed, call the discover tool
Receive specific tool specifications dynamically
Invoke the newly-discovered tools
This reduces baseline token consumption by approximately 90% while maintaining full capability access.
Skills: Behavioral Modification Without Code
Skills represent the third layer of Storytell's architecture—instructional enhancements that modify agent behavior without any runtime execution.
What Skills Are
Skills are pure text instructions injected into agent contexts. Unlike tools (which execute code), skills contain no runtime execution, no API calls, and no code.
When an agent invokes a skill:
System retrieves skill's text instructions
Instructions are injected into the agent's context
Agent's behavior is modified according to those instructions
No code runs—purely prompt engineering
Skills vs. Tools: The Critical Distinction
Example Skills
Storytell has implemented several example skills:
Fact Checker: Instructions for validating claims against source material
Skills Creator: Meta-skill for generating new custom skills
Summarizer: Guidelines for condensing information effectively
Skills as Personas
Skills can function as personas—providing behavioral guidelines that make agents adopt specific interaction styles, domain expertise, or professional roles.
Core Platform Architecture: Key Innovations
1. Automatic Concept Extraction System
Revolutionary Knowledge Graph Technology
Storytell's most distinctive feature is its automatic Concept extraction engine—you never manually tag or categorize content. As files are uploaded, Storytell's AI automatically identifies and creates "Concepts"—recurring themes, entities, people, companies, and ideas that appear across your documents.
How Concept Creation Works:
Entity Detection: AI scans for meaningful recurring terms - company names, product lines, key people, abstract themes like "product-market fit" or "churn risk"
Relevance Engine: Evaluates context to determine significance - names appearing across multiple documents become Concepts
Knowledge Graph Formation: Automatically forms interconnected graph showing relationships between topics
Living Intelligence: The graph evolves continuously as new data arrives, discovering hidden relationships that manual organization would miss
Dual Views: Toggle between Assets View (traditional file list) and Concepts View (automatically extracted insights)
No data warehouse required. No manual tagging. No setup required. The system builds and maintains the graph automatically.
Benefits:
Discover hidden relationships that manual organization would miss
Work with Concepts as queryable objects
Subscribe to Concepts for proactive monitoring
Common use cases include:
Product teams organizing customer feedback by account
Sales teams tracking competitive intelligence
Finance teams analyzing data by business unit
2. Actions: Proactive Signal Delivery
Concepts + Actions = Strategic Intelligence at Machine Speed
Storytell's ability to create Concepts that help users make sense of otherwise incompatible unstructured data is very valuable, but is only part of Storytell's value. Pairing Concepts with Actions results in strategic intelligence at machine speed.
What Actions Do:
Actions are natural language instructions that proactively monitor your data and external data and trigger responses when important patterns emerge.
Actions allow the right people, teams, or systems to be alerted when Concepts change or new patterns appear. This means that instead of constantly checking for updates, intelligence finds the user when it matters.
Action Capabilities:
Alert Mechanisms: Send alerts via email, text, Slack when patterns detected
System Integration: Write updates directly to systems of record (e.g., Jira auto-updates)
Workflow Triggers: Trigger workflows before problems become crises
External Data Monitoring: Monitor both internal data and external web sources for pattern changes
Real-World Action Examples:
The Result: Strategic Intelligence at Machine Speed
A product manager's Jira ticket auto-updates with critical user feedback from 37 seconds of a sales call—before the product manager even knows the conversation happened
A Customer Success team receives churn risk alerts the moment CSAT patterns shift—days or weeks before customers express dissatisfaction
An executive team gets texted when revenue forecasts change based on real-time deal flow analysis—enabling immediate course correction
A compliance officer is notified of regulatory risks detected in contract language—before documents are signed
An HR team identifies flight risk employees from performance reviews, changes in calendar patterns, Zoom camera on/off metadata, and communication patterns—allowing proactive retention strategies
This is the future of strategic intelligence: perfect information delivered as signal, not noise, pushed to the people who need it, before they know to ask.
SmartChat: Intelligent Conversational Interface
SmartChat is Storytell's natural language interface that transforms complex data analysis into simple conversations. Unlike traditional chatbots, SmartChat leverages the multi-agent orchestration system to handle sophisticated research requests.
Key Capabilities:
Natural language querying across all uploaded documents and external sources
Automatic citation and source attribution for every claim
Multi-turn conversations with full context retention
Support for complex analytical requests that spawn multiple specialized agents
Real-time progress visibility when agents are deployed
Eight Specialized Web Search Engines
Storytell functions as an intelligence synthesis platform that combines your internal knowledge base with precisely targeted external intelligence from across the web. Using category-intelligent search powered by semantic understanding, Storytell can orchestrate sophisticated multi-source analysis workflows that were previously impossible.
Each category leverages optimized search logic tailored to that information type:
1. People Search - Human Capital Intelligence
What it finds: Employees, researchers, executives, professionals by role, title, or expertise Optimized for: Finding individuals within organizations or fields Example queries:
"AI researchers at OpenAI"
"Engineers who work at Amazon"
"Who is the CTO of Tesla?" Why this matters: When conducting competitive intelligence or market research, knowing who the key players are is half the battle. Storytell can now identify decision-makers, technical experts, and organizational leaders—then cross-reference their backgrounds against your hiring plans, partnership strategies, or competitive positioning.
2. Company Search - Market Intelligence
What it finds: Businesses, startups, organizations, company information Optimized for: Corporate research, competitive analysis, market mapping Example queries:
"AI startups in healthcare"
"Companies building autonomous vehicles"
"Competitors to Stripe in payment processing" Why this matters: Combine external company intelligence with your internal competitive analysis documents. Ask Storytell to identify emerging competitors in your space, validate market sizing assumptions, or assess partnership opportunities—all while grounding findings in your internal strategic context.
3. Research Paper Search - Academic & Technical Intelligence
What it finds: Academic papers, scientific studies, peer-reviewed research Optimized for: Scholarly sources, citations, technical depth Example queries:
"Transformer architecture papers"
"Latest NLP research on retrieval-augmented generation"
"Clinical trials for mRNA vaccines"
Why this matters: When writing whitepapers, grant proposals, or technical documentation, Storytell can now surface the latest research, extract methodologies, and validate claims against the academic literature—then synthesize everything alongside your internal R&D notes or product specs.
4. News Search - Real-Time Context Engine
What it finds: Current events, breaking stories, press releases, journalism Optimized for: Timeliness, editorial content, news cycles Example queries:
"Latest AI regulation developments in the EU"
"Recent tech layoffs at major companies"
"Breaking news about quantum computing breakthroughs"
Why this matters: Layer live news over your internal planning documents. Ask Storytell to analyze how recent regulatory changes impact your roadmap, or how competitor announcements validate (or invalidate) your strategic assumptions. The platform now maintains temporal awareness of external events while reasoning over your private data.
5. GitHub Search - Technical Intelligence & Open Source Monitoring
What it finds: Code repositories, open source projects, programming libraries, frameworks Optimized for: Developer tools, technical implementations, code examples Example queries:
"Best Go web frameworks"
"Python ML libraries for time series forecasting"
"Open source alternatives to Kubernetes"
Why this matters: Technical teams can now evaluate open-source dependencies, assess technical approaches, or monitor competitor technology choices. Storytell can analyze external codebases and technical documentation alongside your internal architecture decisions, helping validate technical direction or identify implementation risks.
6. Tweet Search - Social Sentiment & Early Signals
What it finds: Twitter/X posts, social media discussions, real-time reactions Optimized for: Public sentiment, trending topics, community discourse
7. Personal Site Search - Practitioner Knowledge & Lived Experience
What it finds: Blog posts, personal websites, tutorials, developer experiences Optimized for: Individual perspectives, practical experiences, how-to content Example queries:
"Developer experiences migrating to Rust"
"Personal blog posts about remote work productivity"
"Startup founder reflections on product-market fit"
Why this matters: Academic papers tell you what should work in theory. Personal blogs tell you what actually works in practice. Storytell can now harvest practitioner wisdom from individual experts and synthesize it with your internal best practices or lessons learned.
8. Financial Report Search - Corporate Intelligence & Due Diligence
What it finds: SEC filings, 10-K reports, 10-Q reports, earnings reports, investor documents Optimized for: Financial statements, regulatory disclosures, corporate governance Example queries:
"Apple Q4 2025 earnings report"
"Tesla SEC filings about autonomous vehicle liability"
"Meta 10-K risk factors related to AI regulation"
Why this matters: Financial analysts, corporate development teams, and investors can now pull structured financial intelligence from public filings and cross-reference against internal financial models, due diligence materials, or investment theses.
Multi-Source Intelligence Orchestration: The Power of "Both/And"
The real power emerges when you combine internal knowledge with multiple external sources simultaneously, orchestrated by Storytell's multi-agent system.
Example Workflow: Risk Assessment with Agent Orchestration
Scenario: You're evaluating cybersecurity risks for your SaaS platform.
How Storytell executes this:
General agent receives request and deploys specialized agents
@explorer agent searches News for regulatory changes and security incidents
@explorer agent searches Tweets for early warning signals
@explorer agent searches Financial Reports for competitor risk disclosures
@explorer agent searches Research Papers for technical threat analysis
@librarian agent searches your internal risk registers and compliance docs
Main orchestrator synthesizes all findings into comprehensive risk assessment
The outcome: An intelligent early warning system that surfaces external risks and validates them against your internal risk framework—all executed automatically through parallel agent deployment.
Enterprise-Grade Platform Infrastructure
Comprehensive Data Enrichment
Rather than requiring separate tools to perform OCR, transcription, and metadata extraction for data lakes, Storytell's platform includes built-in enrichment pipelines. These automatically convert scanned PDFs to text, transcribe recordings, and tag content with relevant metadata—ensuring that raw data is immediately ready for AI analysis.
API-First Design
Storytell is built with an API-first architecture to seamlessly integrate with existing enterprise workflows, automating content transformation and insight delivery.
Robust Security & Compliance
Storytell is SOC2 Type 2 certified, HIPAA and GDPR compliant, and provides solid infrastructure that prioritizes data safety, sovereignty, trust, and security through several key measures:
Multi-Tenancy: Storytell offers a multi-tenant structure, allowing global organizations to manage multiple organizational units under a single umbrella, with tailored settings and permissions for each unit. This streamlined governance also helps manage billing practices efficiently, providing top-level visibility across all assets and utilization metrics.
End-to-End Encryption: All data is encrypted from the moment it leaves the user's device until it reaches Storytell's servers, ensuring that no one can intercept or read the data during transfer.
Data Encryption at Rest: Data is also encrypted when stored on Storytell's servers, preventing unauthorized access to readable data even if storage is breached.
Provenance Chain: Storytell maintains a detailed provenance chain, tracking the origin of data, who accessed it, and how it was used. This includes tracking the initial entry point, individuals or entities that have interacted with the data, the context in which the data was referenced, and which LLMs the data was distributed to, ensuring a robust audit trail with no blind spots.
No LLM Training with Your Data: Storytell does not use user data to train large language models (LLMs). Storytell uses enterprise APIs with LLMs, ensuring that user data remains private and is not shared or used in unintended ways.
Audit Logs: Storytell can inspect and audit the provenance chain manually, providing an additional layer of security and accountability. These logs show who accessed the data and when, following SOC2 Type 2 protocols to determine data access permissions.
Solving the "Dark Data" Problem
The Challenge
Organizations face an unprecedented challenge: The vast majority of their most valuable information is trapped in unstructured formats that resist traditional analysis and search methods. Traditional databases and business intelligence tools were designed for structured data and cannot effectively process or query unstructured content.
Enterprises are awash in unstructured data, with 85-90% of all information scattered across connected drives, CRMs, emails, documents, meetings, Slack or Teams messages, PDFs, and countless other sources. Enterprises have data scattered across 660 SaaS applications on average, with 72% operating in silos. Unstructured data is growing at 55-65% annually—3X faster than structured data.
Organizations are paying to store, secure, and manage massive volumes of data that provide zero value. They've built "data swamps" instead of strategic assets.
The Cost of Inaccessibility
Hard Costs:
Lost Productivity: Knowledge workers spend 20-30% of their time searching for information they need to do their jobs effectively
Duplicated Effort: Teams unknowingly repeat analysis, research, or problem-solving that colleagues have already completed
Delayed Decisions: Critical business decisions are postponed while waiting for data compilation and analysis
Inefficient Collaboration: Teams struggle to share insights and build on each other's work when information lives in isolated silos
Opportunity Costs:
Missed Insights: Patterns, trends, and opportunities remain hidden in the noise of overwhelming data volumes
Competitive Disadvantage: Slower time-to-insight means competitors reach conclusions and act first
Strategic Blind Spots: Leadership makes decisions without full visibility into operational realities captured in frontline data
Innovation Friction: New initiatives struggle to build on institutional knowledge that can't be easily discovered or accessed
Storytell's Solution
By combining agentic AI, automatic knowledge graphs, multimodal processing, and intelligent orchestration—all wrapped in an enterprise-grade platform—Storytell transforms unstructured data from a liability into your most valuable strategic asset.
Your teams get answers in seconds instead of weeks—well before they even knew to ask
Your organization builds a living knowledge graph that grows more valuable over time
Your knowledge workers spend their days on strategic thinking and execution instead of confusion and data compilation
Your leadership makes decisions with confidence, backed by comprehensive, trustworthy intelligence
Enhanced Case Studies: Multi-Agent Intelligence in Action
Case Study 1: Portfolio Intelligence at Scale
Scenario: A venture capital firm needs comprehensive research on 57 portfolio companies, analyzing market positioning, competitive threats, growth trajectory, and risk factors for quarterly board meetings.
Traditional Approach: Analysts spend 3-4 weeks manually researching each company, pulling from disparate sources, compiling spreadsheets, and writing individual reports. The process is labor-intensive, prone to inconsistencies, and often outdated by completion.
Storytell Multi-Agent Approach:
User Creates Concept: Define "Portfolio Risk Assessment" Concept to track competitive threats, market dynamics, and financial health across all portfolio companies
Agent Orchestration: User prompts: "Research all 57 portfolio companies and create comprehensive risk assessment analyzing competitive position, market trends, and growth sustainability"
Automatic Deployment: Storytell's orchestrator automatically:
Deploys 57 specialized @researcher agents—one per company
Each agent receives company-specific context and research objectives
Agents work in parallel, each with 200,000+ token context window
Parallel Execution: Each agent autonomously:
Uses knowledge_base tool to search internal documents (board decks, financial models, meeting notes)
Uses web_search tool to retrieve external intelligence (news, competitor filings, market research)
Analyzes social sentiment and industry trends
Performs financial report searches for public competitors
Synthesizes findings into structured insights
Intelligent Synthesis: Main orchestrator:
Aggregates findings from all 57 agents
Identifies cross-portfolio patterns and risks
Uses report tool to create comprehensive reports with inline citations
Generates executive summary highlighting top risks
Proactive Actions: Create Action: "Alert me via Slack when any portfolio company's Concept shows competitive threat exceeding threshold"
Result: Complete portfolio intelligence delivered in hours instead of weeks. The firm identifies 3 companies facing competitive disruption, enabling proactive board discussions and strategic pivots. Ongoing monitoring via Actions provides continuous intelligence without manual effort.
Case Study 2: Predictive Churn Prevention
Scenario: A SaaS company's Customer Success team struggles to identify at-risk customers before they churn. Signals are scattered across support tickets (Zendesk), product usage (Mixpanel), sales calls (Gong), NPS surveys, and contract renewal dates (Salesforce).
Traditional Approach: CS managers manually review dashboards weekly, often missing early warning signs. By the time churn risk is identified, it's too late for effective intervention.
Storytell Multi-Agent + Concepts + Actions Approach:
Concept Creation: Storytell automatically identifies "Predictive Churn Prevention" Concept by analyzing patterns across:
Support ticket sentiment and frequency
Declining product usage metrics
NPS score deterioration
Contract renewal timelines
Sales call sentiment from transcripts
Knowledge Graph Formation: The Concept automatically links to related sub-concepts:
"Customer Support Churn Mitigation"
"Churn Variability Retention Needs"
"Engagement Cliff Indicators"
Action Configuration: CS leader subscribes to "Predictive Churn Prevention" Concept with trigger: "Alert me when customer churn risk exceeds 12%"
Multi-Agent Monitoring: Behind the scenes, Storytell agents continuously:
Monitor internal data sources for pattern changes
Search external sources (social media, review sites) for customer sentiment
Correlate signals across disparate systems
Update Concept status in real-time
Proactive Alerting: When churn risk threshold is breached:
CS manager receives SMS alert with specific customer details
Alert includes inline citations to source data (specific support ticket, usage drop date, NPS response)
Jira ticket auto-creates with recommended intervention strategies
Continuous Intelligence: Actions deliver ongoing signal:
Weekly Slack digest of customers approaching risk threshold
Monthly executive summary of churn patterns and prevention effectiveness
Integration with CRM to flag at-risk accounts for sales team
Result: Complete portfolio intelligence delivered in hours instead of weeks through parallel agent execution. The firm identifies 3 companies facing competitive disruption, enabling proactive board discussions and strategic pivots. Ongoing monitoring via Actions provides continuous intelligence without manual effort.
Case Study 2: Competitive Intelligence Automation
Scenario: A product marketing team at a B2B SaaS company needs to maintain current competitive battle cards across 12 major competitors, tracking product launches, pricing changes, feature updates, customer wins, and market positioning.
Traditional Approach: Marketing analysts manually monitor competitor websites, SEC filings, press releases, social media, and review sites. Battle cards become outdated within weeks. The team spends 15+ hours weekly on competitive research.
Storytell Multi-Agent + Concepts + Actions Approach:
Concept Definition: Product marketing manually creates Concepts for each competitor:
"Salesforce Competitive Intelligence"
"HubSpot Competitive Intelligence"
"Microsoft Dynamics Competitive Intelligence"
(repeat for all 12 competitors)
Multi-Source Agent Deployment: User prompts: "Research Salesforce competitive positioning, recent product announcements, and customer sentiment"
Storytell orchestrator automatically:
Deploys @explorer agents to search external sources
Agents use multiple web_search tool calls with different categories:
Company Search: Finds Salesforce corporate announcements
News Search: Identifies recent press coverage
Financial Report Search: Analyzes SEC filings for strategic initiatives
Tweet Search: Gauges customer and market sentiment
Research Paper Search: Finds technical analysis of their platform
Personal Site Search: Surfaces practitioner experiences and comparisons
Knowledge Graph Enrichment: As agents return findings, Storytell:
Automatically links discoveries to relevant Concepts
Identifies relationships between competitor strategies
Surfaces patterns across multiple competitors (e.g., "All competitors investing in AI features")
Action Configuration: Product marketing creates Actions for proactive monitoring:
"Alert marketing team via Slack when any competitor Concept shows new product launch"
"Update Salesforce battle card in Confluence when pricing information changes"
"Send weekly email digest of competitive intelligence across all 12 competitors"
Continuous Monitoring: Storytell agents continuously:
Monitor external sources for competitor activity using web_search tool
Update Concepts with new information
Trigger Actions when patterns emerge
Maintain citation chains for credibility
Automated Battle Card Updates: When Action detects Salesforce price change:
Sends Slack alert to product marketing with inline citation to source
Auto-updates battle card in Confluence with new pricing details
Flags related Concepts (e.g., "Competitive Pricing Strategy") for review
Result: Competitive intelligence transforms from reactive manual research to proactive automated monitoring through multi-agent orchestration. Battle cards stay current automatically. The team reallocates 60 hours monthly from data gathering to strategic positioning and messaging. Sales teams have confidence in battle card accuracy.
Case Study 4: M&A Due Diligence Acceleration
Scenario: A private equity firm evaluates a software company acquisition. The diligence process requires comprehensive analysis of: target financials (3 years of models), customer contracts (200+ PDFs), sales pipeline (CRM exports), product roadmap, management presentations, technical documentation, and market research. Traditional diligence produces 80-page reports over 4-6 weeks.
Traditional Approach: Analysts manually read hundreds of documents, extract key data points into spreadsheets, cross-reference findings, and write synthesis reports. Critical insights are missed due to information overload. Process bottlenecks deal velocity.
Storytell Multi-Agent + Concepts Approach:
Project Setup: Create "Target Company Due Diligence" Project and upload all materials (financial models, contracts, presentations, technical docs, market research)
Concept Creation: Storytell automatically identifies key diligence Concepts:
"Revenue Quality & Sustainability"
"Customer Concentration Risk"
"Product-Market Fit Signals"
"Technical Debt & Scalability"
"Management Team Capability"
"Competitive Position"
Multi-Agent Deployment: User prompts: "Analyze revenue quality, customer concentration risk, and growth sustainability"
Orchestrator automatically:
Deploys specialized @librarian agents to analyze internal documents
Deploys @explorer agents to research external market context
Agent 1: Analyzes financial models for recurring vs. one-time revenue
Agent 2: Reviews customer contracts for churn risk indicators
Agent 3: Examines sales pipeline for deal quality
Agent 4: Searches competitor SEC filings for market comparison
Agent 5: Analyzes industry research papers for growth trends
Parallel Processing: Each agent autonomously:
Selects appropriate tools (structured queries for Excel, semantic search for PDFs, web search for external context)
Performs deep analysis within 200K token context window
Reports findings with inline citations back to orchestrator
Intelligent Synthesis: Orchestrator aggregates findings:
85% recurring revenue (strong business model)
Top 5 customers = 45% of revenue (concentration risk)
28% annual customer churn (higher than industry benchmark)
Growing market (15% CAGR per external research)
Technical debt in legacy codebase (scaling concerns)
Concept-Based Queries: Analysts query Concepts directly:
"What does Customer Concentration Risk Concept reveal about revenue stability?"
"How does Product-Market Fit compare to industry benchmarks?"
Storytell synthesizes answers from multiple sources with citations
Report Generation: User prompts: "Generate investment committee memo"
Storytell produces comprehensive memo in hours:
Executive summary with key findings
Detailed analysis sections with inline citations
Risk assessment with mitigation strategies
Valuation implications based on findings
Result: Due diligence completed in 10 days instead of 6 weeks. Firm identifies customer concentration risk early and negotiates $20M price reduction with earnout protection. Investment returns 3.2x over 5 years. The firm now conducts 3X more deal evaluations with the same team.
Case Study 5: Product Roadmap Intelligence
Scenario: A product team at a fintech company needs to prioritize feature requests for Q1 roadmap. Feedback is scattered across: customer support tickets (Zendesk), sales call recordings (Gong), user interviews, NPS surveys, product usage analytics, and competitive feature analysis.
Traditional Approach: Product managers spend 2-3 weeks manually reviewing sources, tallying feature mentions in spreadsheets, and making subjective prioritization decisions without comprehensive data visibility.
Storytell Multi-Agent + Concepts + Actions Approach:
Concept Definition: Product team defines strategic Concepts:
"Customer Feature Requests"
"Churn-Related Product Gaps"
"Competitive Feature Parity"
"Revenue-Driving Capabilities"
Data Integration: Upload all data sources to Storytell:
CSV export of support tickets (10,000 tickets)
Audio files of sales calls (200+ recordings)
Word docs of user interview transcripts
PDF of competitor feature matrices
Excel of product usage analytics
Multi-Agent Analysis: User prompts: "Analyze all customer feedback to identify top requested features and their business impact"
Orchestrator automatically:
Deploys @librarian agents to analyze internal sources in parallel
Agent 1: Semantic search across 10,000 support tickets for feature requests
Agent 2: Transcription and analysis of 200 sales call recordings
Agent 3: Theme extraction from user interview transcripts
Agent 4: Quantitative analysis of product usage patterns
Agent 5: Competitive gap analysis from external research
Automated Concept Population: As agents complete analysis:
"Customer Feature Requests" Concept auto-populated with 47 distinct requests
Each request linked to source citations (specific tickets, call timestamps, interviews)
Frequency analysis shows "Multi-currency support" mentioned 247 times
Churn correlation analysis reveals "Advanced reporting" cited in 34% of churned customer exit interviews
External Validation: Deploy @explorer agents for market context:
Research Paper Search: Find academic studies on fintech feature adoption
Company Search: Analyze what competitors are building
Tweet Search: Gauge market demand for specific features
News Search: Identify regulatory trends requiring new capabilities
Action Configuration: Create proactive monitoring:
"Alert product team when any feature request exceeds 50 customer mentions"
"Weekly Slack digest of trending feature requests with business impact scores"
"Auto-update Jira epics when feature requests linked to churn risk increase"
Prioritization Intelligence: User queries Concepts:
"Which feature requests correlate most strongly with revenue expansion?"
"What features are competitors launching that we're missing?"
"Which product gaps are driving customer churn?"
Storytell synthesizes comprehensive answers with inline citations to source data
Roadmap Generation: User prompts: "Create Q1 roadmap prioritization document"
Storytell generates report with:
Top 10 features ranked by business impact (revenue potential, churn reduction, competitive parity)
Supporting evidence with inline citations
Competitive context from external research
Resource estimation based on similar historical projects
Result: Product team completes roadmap prioritization in 3 days instead of 3 weeks. Data-driven decisions backed by comprehensive evidence increase stakeholder confidence. Q1 roadmap focuses on features that drive 18% increase in expansion revenue and 15% reduction in churn. Ongoing Actions provide continuous product intelligence without manual effort.
Sample Use Cases Across Functions
Sales
Sales Leaders:
Improve revenue forecast accuracy and pipeline health
Review pipeline deals to assess quality & identify risks
Account Executives:
Research prospects and develop personalized outreach
Accelerate stalled enterprise deals
Create personalized sales proposals at scale
Enablement:
Create sales enablement materials by analyzing past sales calls and AE performance deltas
Marketing
Growth:
Identify funnel conversion bottlenecks and growth opportunities
Content:
Develop and use consistent brand voice guidelines
Create compelling customer success stories and case studies from internal data like product and engineering tickets
Write professional technical whitepapers from engineering tickets integrating explanations, visuals, and analysis
Product
Product Managers:
Prioritize feature requests using data from unstructured sources such as customer support tickets, sales team recordings, QBRs, return data, CSAT scores, qualitative survey panels
Create comprehensive PRDs by synthesizing customer requirements, technical constraints, and business OKRs
Plan product releases and create communication materials for internal teams and customers
Product Marketing:
Conduct competitive analysis by evaluating internal and external data, including SEC filings and competitor conference videos
Automatically update sales battle cards based on competitor offering updates
Customer Success
CSM:
Monitor customer health signals to proactively identify churn risk and expansion areas
Analyze support tickets to identify and proactively fix common issues
Detect upsell opportunities from customer usage patterns
Engineering
Engineering Leaders:
Establish and document engineering standards
Create detailed analysis plans for complex projects
Prioritize technical debt based on incident frequency and velocity impact
Engineers:
Create comprehensive technical docs and change logs from Jira/Linear tickets and GitHub pull requests
Analyze sprint retrospectives to identify performance patterns and improvement opportunities
Finance
CFO:
Prepare quarterly earnings communications and anticipate analyst questions
Financial Analyst:
Build and validate financial models by synthesizing historical data and assumptions
Prepare annual budgets and quarterly forecasts by synthesizing departmental inputs
Executives
C-Suite:
Prepare comprehensive board materials synthesizing company performance and strategic initiatives
Develop annual strategic plans and company OKRs
Facilitate annual strategic planning by synthesizing internal performance with external market insights
Conduct M&A due diligence synthesis for acquisition targets and plan post-merger integration
Prioritizing the User Experience
Storytell prioritizes user experiences to ensure that individuals don't need to be AI experts or master prompters to harness the power of AI. Our users literally tell us they feel like superheroes.
Most users don't even know that Storytell exists—they simply experience the predictive intelligence via alerts to text, email, Slack, and updated systems of record with the insights they need, the moment they need them.
Engineering Vision & Extensibility
"With Storytell, you can make sense of any amount of any kind of data, and that signal will find you."
Storytell has built a number of off-the-shelf tools for agents to use. For enterprise customers, Storytell uses a Forward Deployed Engineering (FDE) model to build bespoke tools that can interface with any enterprise system.
This extensibility is enabled by the "everything is a tool" architecture—new capabilities can be added as tools without modifying the core agent orchestration system.
The Bottom Line
Humans run on stories. We make strategic decisions based on how well we understand the data in front of us. Storytell.ai makes data-driven storytelling your company's competitive advantage.
The unstructured data that fills your organization isn't noise—it's signal waiting to be discovered. Customer insights hidden in support tickets. Strategic intelligence buried in competitive research. Operational wisdom captured in meeting transcripts. Innovation opportunities scattered across project documents.
By combining multi-agent orchestration, automatic knowledge graphs, intelligent tools, behavioral skills, and multimodal processing—all wrapped in an enterprise-grade platform—Storytell transforms unstructured data from a liability into your most valuable strategic asset.
Your teams get answers in seconds instead of weeks—well before they even knew to ask
Your organization builds a living knowledge graph that grows more valuable over time
Your knowledge workers spend their days on strategic thinking and execution instead of confusion and data compilation
Your leadership makes decisions with confidence, backed by comprehensive, trustworthy intelligence
The three-layer architecture—Agents (specialized LLM workers), Tools (executable functions), and Skills (instructional enhancements)—enables Storytell to tackle analytical challenges that would overwhelm traditional single-context AI systems.
This is the future of strategic intelligence: perfect information delivered as signal, not noise, pushed to the people who need it, before they know to ask.