Recommendation engines, audience scoring, and content systems. Attention turned into revenue.
We build the systems that personalize content, monetize audiences, and scale distribution.
Media companies compete on personalization, distribution speed, and monetization efficiency. We build recommendation engines that keep users watching, content management systems that scale, and ad tech that maximizes revenue per impression.
Why AI in Media is different.
Attention is the currency, and it is getting harder to earn. 80% of what people watch on streaming platforms comes from algorithmic recommendations. If your recommendation engine is generic, your catalog is invisible. Long-tail content sits unwatched while users churn because they cannot find what they want.
Content production costs keep climbing while audiences fragment across platforms. The media companies pulling ahead are using AI to reduce content localization costs by 40-60%, automate metadata tagging across petabyte-scale libraries, and personalize monetization strategies per user segment.
The technical challenge is unique: real-time recommendations at millions of concurrent users, rights management that varies by region and time window, and ad insertion that maximizes yield without degrading experience. This is not a generic ML problem. It requires media-specific infrastructure.
The Problems That Slow You Down
Content discovery is broken
Users scroll past 90% of your catalog. Recommendations show popular content, not relevant content. Long-tail inventory goes unwatched.
Content production can't keep up with demand
Audiences consume faster than teams can produce. Localization, formatting, and platform adaptation multiply the workload.
Ad monetization leaves money on the table
Fill rates, CPMs, and targeting are all suboptimal. Programmatic is set and forgotten, not continuously optimized.
Rights management is a legal minefield
Content rights vary by region, platform, and time window. One mistake means takedowns, fines, or worse.
Systems for Media
Recommendation Engines
Content recommendations that balance relevance, discovery, and business goals, not just "users who watched X also watched Y."
View service →Content Production AI
Automated transcription, subtitle generation, content summarization, and format adaptation across platforms.
View service →Audience Analytics
Behavioral segmentation, engagement scoring, and churn prediction to understand your audience at individual level.
View service →Streaming Infrastructure
CDN architecture, adaptive bitrate, offline playback, and multi-platform delivery, built for millions of concurrent users.
View service →These aren't pitch deck scenarios. Every use case maps to a system pattern we've built and deployed in production.
What We Build in Media
Content Recommendation Engine
CPO at OTT/streamingCold browse problem. Users churn when they don't find content fast. 80% of streams come from recs.
Hybrid recommender (matrix factorization + sequence transformers on viewing/metadata/context), powering home-screen rails.
Dynamic Subscription Pricing
VP GrowthFixed tiers leave money on table; churned users not targeted with optimal offers, ARPU stagnates.
Regression + RL on demographics, device, engagement, billing, producing willingness-to-pay estimates and individualized offers.
Ad Inventory Targeting
Head of Ad ProductsBroad campaigns waste impressions; under-deliver on CPM/CPC across OTT/CTV/digital.
Audience segmentation + uplift modeling on behavioral/demographic/contextual data, LLMs for copy variants.
Newsroom Content Copilot
Editor-in-ChiefEditorial teams spend hours on research, summaries, headlines, social copy, versioning.
LLM copilot drafts outlines, summaries, headlines, social, SEO metadata, localization, integrated with CMS.
Automated Metadata Tagging
CTOPetabytes of video/audio with inconsistent tags; editors waste time searching, content under-monetized.
Multimodal models (ASR, vision CNNs, LLMs) generate transcripts, detect faces/objects/scenes, assign standardized tags into MAM.
Rights & Contracts Document Agent
Head of LegalThousands of contracts with rights windows/obligations; manually checking what can show where/when is slow and risky.
LLM ingests rights agreements, extracts structured metadata (territories, languages, platforms, windows), natural-language queries.
Audience Social Listening
VP InsightsManual review of social/reviews/comments; no fast quantitative sentiment by show/genre/region.
NLP sentiment/topic modeling + clustering, LLM-generated insight summaries per segment.
Building the system is half the job. Growing the business around it is the other half.
How We Grow Media Brands
AEO for Content Tool Queries
Head of GrowthMedia SaaS loses 49% of "best video editor for YouTube" to ChatGPT; zero-clicks drop trials 30%.
Schema tutorials/templates, creator FAQs, Perplexity tracking to freemium sign-ups.
LinkedIn Ads for Production Software
Demand Gen DirectorLinkedIn CPL $65-105 for editing tools; under 12% SQL.
Producer targeting, demos, HubSpot nurture.
HubSpot Lead Scoring for Creator Tools
Marketing Ops Lead4K leads/quarter; 66% unqualified.
HubSpot scoring (uploads, views), sequences (tutorials), billing sync.
How We Work With Media
Machine Learning Solutions
Prediction systems that run 24/7. Not proof-of-concepts gathering dust.
Learn more →Generative AI Integration
Custom LLM applications, fine-tuned models, and content systems, architected for production.
Learn more →Data Engineering & Analytics
Your data is scattered across 15 tools. We wire it into one system that answers questions.
Learn more →Cloud & Infrastructure
Cloud architecture, migration, Kubernetes, monitoring: the foundation under everything else.
Learn more →Frequently asked questions
Same principles: collaborative filtering, content-based, and contextual signals. We tune for your specific catalog, audience size, and business model (ad-supported vs subscription vs hybrid).
For operational content, yes: summaries, captions, metadata, social clips. For editorial content, AI generates drafts, your editorial team decides what ships.
We architect for millions of concurrent viewers. CDN strategy, edge caching, and adaptive bitrate configured per your geographic footprint.
AI moves fast. Stay ahead.
No spam. One actionable email per week on AI systems and growth.
Your content library is underperforming. We build systems that fix that.
Start with an audience and content audit. We'll identify where personalization and automation create the most value.