Natural Language Queries
Ask questions in plain English — "What pricing changes have our top 3 competitors made this quarter?" — and get structured, sourced answers in seconds.
Know your market before your competitors react.
Traditional competitive intelligence breaks down at scale — too many sources, too little time, insights that arrive too late to act on. Our agents monitor thousands of sources continuously and route decision-ready signals to the tools your team already uses.
Outcomes
Up to 30%Reduction in research and synthesis time
McKinsey internal Lilli deployment across 45,000 professionals
76%Year-over-year increase in AI adoption among CI teams
Crayon State of Competitive Intelligence Report 2025
59%Win-rate lift with regularly updated competitive materials
Crayon State of Competitive Intelligence Report 2024
Intelligence Coverage
Every signal. Every source. Delivered where you work.
AI agents continuously monitor competitors across web, financial, social, and regulatory channels — then route structured intelligence to Slack, your CRM, or a live dashboard.
Competitor Websites
Pricing Pages
Regulatory Filings
Patent Databases
Job Postings
G2 & Capterra
News & Press
Social Media
AI Engine
Slack & Teams
CRM Battlecards
Live Dashboard
Capabilities
From raw signal to ready-to-act briefing.
Natural-language queries, trigger-based alerts, scheduled executive briefs, and configurable signal filters — built on the same continuous-monitoring engine.
Trigger-Based Alerts
Define custom triggers — a competitor hires a new CTO, drops pricing, or files a patent — and get notified instantly via Slack, email, or webhook.
Scheduled Intelligence Briefs
Weekly or daily digests assembled and delivered automatically — executive summaries, competitive snapshots, and market trend reports on your schedule.
Configurable Signal Filters
Control exactly which competitors, markets, and signal types you track. Adjust relevance thresholds to eliminate noise without missing critical changes.
Use Cases
Twelve intelligence streams ready to deploy.
Hover over any use case to see how our agents handle it.Tap any use case to see how our agents handle it.
Competitor Pricing Monitoring
Delayed responses to competitor price changes erode margins across every deal cycle. Manual monitoring across even a dozen competitors is unsustainable at scale: analysts can track roughly 15–20 sources by hand before accuracy degrades. AI-powered pricing intelligence platforms continuously scan competitor pricing pages, product listings, and promotional channels in real time, automatically triggering internal repricing workflows. McKinsey research shows companies deploying AI-driven price optimisation report profit margin increases of 5–10%.
Product Launch Detection
When a competitor ships a new feature or product, every week of delayed awareness translates directly to sales conversations where your reps are caught flat-footed. Crayon research shows teams that update competitive materials monthly see up to 59% higher win rates than those relying on stale battlecards. AI systems monitor competitor websites, app store changelogs, press wires, job postings, and engineering blogs simultaneously, surfacing structured launch alerts the moment they are detected — eliminating the information lag that costs deals.
Market Trend Analysis
Executives and product leaders routinely make roadmap and budget decisions with market data that is 6–12 months out of date, because assembling a credible trend report manually takes weeks of analyst time. AI market intelligence systems ingest earnings calls, analyst reports, trade publications, regulatory filings, and news at machine speed, producing structured trend summaries on demand. This compresses the typical research cycle from weeks to hours, freeing analysts to focus on interpretation and strategic recommendations.
Patent & IP Monitoring
IP teams at mid-market and enterprise companies face growing litigation exposure when competitors file patents in adjacent technology areas that go undetected until a product is already in development. AI-powered patent monitoring platforms reduce prior art and freedom-to-operate search time by 60–80% compared to manual methods, compressing searches from weeks to minutes. The global AI in patent and market intelligence market is projected to grow from $1.58 billion in 2026 to $5.67 billion by 2034, reflecting the urgency with which enterprises are addressing this gap.
Regulatory Change Tracking
85% of enterprises report that regulatory complexity has increased over the past three years (PwC Global Compliance Survey 2025), while financial institution fines in the first half of 2025 alone totaled $1.23 billion globally — a 417% increase from the same period (Fenergo). AI-related regulations are accelerating across every major jurisdiction, with new rules from ASIC, the EU AI Act, and agencies worldwide reshaping compliance requirements. AI regulatory monitoring systems scan thousands of national and international regulatory sources around the clock, alerting compliance teams to relevant changes and automatically generating impact summaries.
Social Sentiment Analysis
Brand and competitive signals surface on social media hours or days before they appear in formal press coverage, yet most enterprise teams lack the capacity to monitor the volume required to catch early signals. AI sentiment analysis systems process hundreds of millions of posts daily across major social channels, classifying tone, topic, and urgency at a scale no human team can replicate. These systems track share-of-voice shifts, emerging negative narratives about competitors, and viral product feedback that signals unmet market demand — giving go-to-market teams a real-time pulse on buyer sentiment.
Win/Loss Analysis
Most organisations conduct win/loss analysis inconsistently: interviews are infrequent, data is not structured, and insights rarely make it into seller workflows in time to affect live deals. The manual effort is substantial — individual reps routinely spend hours each month on ad-hoc competitor research instead of selling. AI-powered win/loss platforms automatically extract competitive themes from CRM notes, call recordings, and post-sale interviews, surfacing structured patterns across thousands of opportunities and feeding insights directly into live deal workflows.
Sales Battlecard Automation
Product marketing teams spend significant time each quarter manually updating battlecards, and those cards are typically outdated within weeks of publication — far too slow for fast-moving competitive environments. Maintaining current competitive content requires dedicated analyst time that scales linearly with competitor count. AI battlecard automation continuously monitors competitor signals and rewrites the relevant card sections in real time, pushing updates directly to sales enablement platforms like Highspot or Seismic. Crayon research shows teams that update competitive materials monthly see up to 59% higher win rates.
M&A Signal Detection
Identifying acquisition targets or detecting that a competitor is approaching acquisition readiness before a deal is announced gives strategic teams a measurable advantage. AI signal detection platforms continuously scan financial filings, regulatory documents, leadership changes, hiring patterns, and press releases, surfacing early indicators such as slowing growth, board changes, or strategic pivots. Deloitte’s 2025 M&A Generative AI Study found that 86% of surveyed corporate and PE leaders have now integrated generative AI into M&A workflows, with the top application areas being strategy and market assessment, target screening, and due diligence.
Industry Benchmark Tracking
Engineering and operations leaders need accurate industry benchmarks to justify headcount, tooling investment, and process improvement initiatives — but assembling credible benchmark data from public sources is time-intensive and error-prone. Teams that rely on annual benchmark reports operate with data that is 12–18 months stale by the time decisions are made. AI benchmark intelligence systems continuously extract and normalise performance metrics from public filings and industry publications, maintaining living benchmark datasets across KPIs.
Customer Review Mining
Competitor customer reviews on platforms such as G2, Capterra, and the App Store are among the richest and most candid sources of product intelligence available, yet most teams lack a systematic process for monitoring and synthesizing them at scale. AI review mining systems continuously ingest competitor reviews, classify complaints and feature requests by theme, and surface emerging patterns — such as a competitor’s new pricing tier generating backlash or a feature gap driving churn — weeks before that signal appears in analyst reports. This gives product and go-to-market teams a direct line to unfiltered buyer sentiment.
Supply Chain Intelligence
Enterprise companies face an average annual cost of $228 million from supply chain disruptions including labour strikes, extreme weather, and geopolitical risks (Interos Annual Global Supply Chain Report). Monitoring the supplier relationships, manufacturing dependencies, and logistics exposure of key competitors requires continuous surveillance of a vast and fragmented data landscape. AI-powered supply chain intelligence systems aggregate signals from port data, shipping manifests, news events, weather systems, and financial filings, surfacing disruption risk signals far earlier than traditional monitoring and giving strategic teams time to act before impact hits.
Platforms
Deploy on your preferred cloud.
FAQ
Frequently asked questions.
Everything you need to know about AI competitive intelligence — from data sources and ROI to implementation timelines.
AI-powered competitive intelligence is the use of machine learning, natural language processing, and autonomous agent systems to continuously monitor competitors, market conditions, and industry signals at a scale no human team can match manually. Where traditional CI relies on periodic analyst reviews of curated sources, AI systems ingest thousands of data points daily — from competitor websites and product updates to earnings calls, customer reviews, job postings, and regulatory filings — and surface the patterns that matter. The output is not raw data but structured, prioritised intelligence that leaders can act on immediately.
The business case for AI competitive intelligence is well-documented. McKinsey’s internal deployment of their Lilli platform showed up to 30% time savings on research and synthesis tasks across 45,000 professionals. Crayon’s 2024 State of Competitive Intelligence report found that teams maintaining current competitive materials see up to 59% higher win rates. The ROI calculation is straightforward: multiply the hours your analysts and sales reps spend on manual competitor research by their fully loaded cost, then factor in the deal-level impact of stale intelligence. For most enterprise teams, the payback period on a well-implemented AI CI system is measured in months, not years.
A properly architected AI competitive intelligence system draws from a wide array of structured and unstructured sources simultaneously. These include competitor websites and product pages, press releases and news portals, customer review platforms such as G2 and Capterra, social media channels, job postings that reveal hiring strategy, regulatory filings and earnings call transcripts, patent databases, app store updates, and third-party data providers. Advanced systems also ingest internal data — call recordings, CRM win-loss notes, and support tickets — to build a complete picture of competitive dynamics.
Traditional CI software platforms are primarily aggregation and distribution tools — they collect signals and push them to analysts who still perform the synthesis and judgment work manually. AI-native competitive intelligence systems go further by automating the analysis layer itself: classifying signal importance, identifying strategic patterns, generating summaries, and proactively surfacing insights based on business context rather than keyword alerts. Custom AI agent deployments can also be configured to monitor specific competitive dimensions unique to your business in ways that off-the-shelf platforms are not built to support.
A focused AI competitive intelligence deployment can be operational within four to eight weeks. The first phase — defining competitor sets, data sources, and intelligence priorities — typically takes one to two weeks. Agent configuration, source integration, and alert workflow setup follow over the next two to four weeks. Full integration with CRM platforms, Slack or Teams, and sales enablement tools adds another one to two weeks. Unlike enterprise software implementations that require months of IT involvement, AI agent systems are largely configuration-driven and do not require significant infrastructure changes.
Well-architected AI competitive intelligence deployments address data security through several layers: all monitored data is sourced from publicly available information, eliminating exposure of internal proprietary data to external models by default. Agent systems can be deployed within your own cloud environment — GCP, AWS, or Azure — ensuring all AI processing stays in your cloud. Access controls, audit logging, and role-based permissions govern which teams see which intelligence outputs. Compliance with SOC 2 Type II and GDPR requirements is achievable and should be a baseline expectation for any enterprise vendor.
Modern AI competitive intelligence systems are designed around integration-first architectures. Standard integrations include CRM platforms such as Salesforce and HubSpot, Slack and Microsoft Teams for real-time alert delivery, and sales enablement platforms such as Highspot or Seismic for battlecard distribution. For engineering and product teams, integrations with Jira and Confluence allow competitive signals to flow directly into roadmap planning workflows. Custom AI agent deployments can also expose intelligence through internal APIs, enabling teams to query competitive data programmatically as part of broader decision-support systems.
The next step
Pick a time. Let's talk.
A 15-minute introductory call — no pitch deck, no obligation. We'll tell you straight whether AI agents are the right fit for what you're trying to do.