"Agile Infoways team delivered exceptional iOS and Android apps with responsive support and outstanding problem-solving expertise."
- Rob Machado
IndustriesSocial Networking
AI for Social Networking
Social Media App Development, Built to Scale With AI
Agile Infoways is a social media app development company building real-time feeds, messaging, AI content moderation, recommendation and feed-ranking systems, and scalable platform infrastructure for communities that need high concurrency, low latency, and safer user growth.
Industry Challenges
The Pressures Facing Social Platforms at Scale
Explosive content volumes, regulatory scrutiny, advertiser brand safety demands, and a creator economy that expects data-driven monetisation tools are pushing platforms to operationalise AI or face irreversible erosion.
6 key challenges
01
Content Moderation at Scale
Platforms processing billions of posts, images, and videos daily cannot rely on human moderators alone. Manual moderation is slow, inconsistent, and exposes platforms to brand-damaging content and regulatory fines.
02
User Engagement & Retention
Competition for attention is intensifying. Without a personalised feed algorithm that surfaces relevant content, users disengage within minutes — increasing churn and reducing the DAU/MAU ratios advertisers pay premiums for.
03
Misinformation & Bot Networks
Coordinated inauthentic behaviour, state-sponsored influence operations, and synthetic bot networks undermine platform trust, distort organic engagement metrics, and attract regulatory scrutiny from multiple jurisdictions.
04
Ad Revenue Optimisation
Untargeted advertising inventory is rapidly commoditised. Platforms lacking precision audience segmentation, contextual placement intelligence, and real-time bid optimisation leave significant CPM revenue unrealised.
05
Privacy & Regulatory Compliance
GDPR, CCPA, DSA, and emerging global data protection frameworks impose consent management, data minimisation, and algorithmic transparency obligations that demand continuous compliance infrastructure investment.
06
Creator Economy & Monetisation
Creators are the content supply chain. Platforms that fail to provide transparent revenue analytics, growth intelligence, and monetisation tooling lose top creators to competitors — taking their audiences with them.
Healthcare Services
Platform Intelligence ServicesWe Deliver
End-to-end AI and platform engineering solutions built for social networks, creator platforms, and community applications — from content safety to revenue optimisation.
Content Moderation AI
Multi-modal AI moderation system that classifies text, images, video, and audio for policy violations — hate speech, CSAM, violence, and spam — in real time and at platform scale with human-in-the-loop escalation.
Multi-modal content classification (text, image, video, audio)
Policy-tunable violation severity scoring
Human escalation queue prioritisation
Appeals management and moderator tooling
Recommendation & Feed Algorithm
Personalised content ranking and recommendation engine that optimises for meaningful engagement signals — watch time, shares, comments — while incorporating diversity controls and filter bubble mitigation.
Real-time interest and affinity modelling
Engagement signal weighting and optimisation
Content diversity and serendipity controls
A/B test framework for ranking experiments
Bot & Fraud Detection
Behavioural AI system that identifies bot accounts, coordinated inauthentic networks, and fake engagement at account creation and in real time — protecting organic metrics and advertiser trust.
Account creation and onboarding bot scoring
Coordinated inauthentic behaviour network detection
Fake engagement (likes, followers, shares) identification
Real-time account suspension and shadow-ban automation
Ad Targeting & Revenue AI
Precision audience segmentation and contextual ad placement engine that maximises CPM yield through real-time bid optimisation, brand safety scoring, and lookalike audience modelling.
First-party audience segmentation and modelling
Contextual brand safety and suitability scoring
Real-time bid price optimisation
Advertiser ROI attribution and reporting dashboard
Privacy Compliance Automation
Automated consent management, data subject request (DSR) processing, and algorithmic transparency reporting infrastructure that keeps platforms continuously compliant with GDPR, CCPA, and DSA obligations.
Consent management platform (CMP) integration
Automated data subject access and deletion request handling
Algorithmic transparency report generation
Data minimisation and retention policy enforcement
Creator Analytics Platform
Data-rich creator dashboard that surfaces audience growth intelligence, content performance benchmarking, revenue attribution, and monetisation opportunity signals — retaining top creators on platform.
Audience growth and demographic analytics
Content performance benchmarking vs. category peers
Revenue stream attribution and earnings forecast
Optimal posting time and format recommendations
Use Cases
Real Platform Outcomes from Social AI
How social networks, creator platforms, and community applications used our AI to improve trust, engagement, revenue, and safety.
01Case Study
99.1%Moderation Accuracy
Content ModerationComputer VisionNLP
Content Moderation Accuracy at Scale
The Challenge
A mid-size social video platform processing 4.2M uploads daily with a human moderation team achieving only 81% policy violation detection accuracy and an average review lag of 18 hours for flagged content.
The Outcome
Multi-modal AI moderation system deployed with human escalation for edge cases. Policy violation detection accuracy reached 99.1%. Average review lag reduced to 34 minutes. Moderator workload fell 74%.
02Case Study
2.3xSession Length Increase
Recommendation AIFeed AlgorithmEngagement
Feed Personalisation & Engagement Lift
The Challenge
A community platform with declining session length — average session time had dropped 23% year-on-year as a chronological feed failed to surface relevant content for a rapidly diversifying user base.
The Outcome
AI-personalised ranking algorithm trained on engagement signals and interest graphs deployed. Average session length increased 2.3x. DAU/MAU ratio improved from 31% to 54%. 30-day retention up 19 percentage points.
03Case Study
44%Ad Revenue Improvement
Ad TechAudience AIRevenue Optimisation
Ad Revenue Optimisation
The Challenge
A creator platform with 28M monthly active users generating CPMs 38% below industry benchmark due to broad audience segments, poor contextual targeting, and no real-time bid optimisation.
The Outcome
Precision audience segmentation and contextual placement AI deployed. Average CPM increased 44%. Brand safety scores improved, unlocking 12 premium advertiser categories previously restricted. Annual ad revenue up $9.2M.
04Case Study
97%Fake Account Detection Rate
Bot DetectionFraud AIPlatform Integrity
Fake Account & Bot Network Elimination
The Challenge
A professional networking platform discovering that 14% of reported follower counts and 21% of post engagement metrics were driven by bot accounts — eroding advertiser trust and threatening platform integrity.
The Outcome
Behavioural bot detection AI deployed at account creation and retrospectively across the active user base. 97% of fake accounts identified and removed within 30 days. Advertiser satisfaction scores rose 38 points.
Proven Impact
The Numbers Behind Social Networking AI
Measurable outcomes from AI deployments across social platforms, creator networks, and online community applications.
Top Result
99.1%
Content Moderation AccuracyMulti-modal AI across 4.2M daily uploads
2.3x
Session Length IncreaseAI feed personalisation on community platform
44%
Ad Revenue ImprovementPrecision targeting and CPM optimisation
97%
Fake Account Detection RateBot network elimination within 30 days
Ready to Transform Social Networking with AI?
Book a free 45-minute AI discovery session with one of our social networking AI specialists.
Before You Build
AI for Social Platforms
What platform teams ask before building AI — detecting deepfakes, AI versus human moderation, algorithmic fairness, meeting regulators like the DSA, whether it suits smaller platforms, and getting started.
8 questions answered
01
How do you detect deepfakes and AI-generated content?
As synthetic media floods platforms, detection has to go beyond keyword filters. We train models to spot generative artifacts in images, video, and audio, cross-check provenance signals, and flag coordinated synthetic uploads for review. These custom AI models are tuned to your content types and updated as generation tools evolve.
02
Can AI moderation replace human moderators, and how accurate is it?
AI handles the overwhelming volume — classifying text, images, and video in real time — so humans focus on nuanced, high-stakes calls and appeals. It won't replace judgment, but it cuts exposure and turnaround dramatically. We measure precision and recall continuously and route uncertain cases to people. AI automation scales moderation without abandoning oversight.
03
How do you keep moderation and feed AI fair and free from bias?
Bias creeps in through skewed training data and proxy signals, so we audit datasets for representation, test outcomes across user groups, and tune for fairness alongside accuracy. Transparent, explainable models let you see why content was ranked or removed. It starts with well-governed data engineering, not just model tweaks.
04
Will our recommendation and moderation AI meet regulators like the DSA?
Increasingly, platforms must explain how algorithms rank and remove content, prove child-safety controls, and file transparency reports under the DSA and similar laws. We build with model versioning, decision logging, and exportable transparency data baked in. Governed AI infrastructure keeps every algorithmic decision traceable and audit-ready.
05
Is this only for billion-user platforms, or can a growing app use it?
You don't need Meta's scale or budget. Cloud infrastructure and pre-trained models let growing communities deploy moderation, bot defense, and recommendations affordably, starting with the risk that matters most. It scales with your user base. Platforms of many sizes have launched with us, as our case studies show.
06
How do we get started, and what does it cost?
Start with your sharpest risk — usually moderation or bot abuse — prove it on a slice of traffic, then expand to recommendations and revenue tooling. Cost scales with scope, so a focused pilot is far cheaper than a full platform build. Many teams add dedicated AI engineers to launch fast.
07
How much does it cost to build a social media app?
Social media app development usually starts around GBP35k to GBP80k for a focused MVP with feeds, profiles, and messaging, and can exceed GBP180k for large platforms with moderation, ranking, and creator tooling. Cost depends on concurrency, media handling, and feature depth. Mobile-first products often pair the core platform with mobile app development for iOS and Android delivery.
08
How do you moderate content at scale?
We moderate content at scale by combining AI classifiers for text, image, and video with policy rules, human escalation queues, and infrastructure designed for high-throughput event processing. That keeps harmful content reviewable without slowing the product experience. Reliable moderation also depends on resilient queues, storage, and autoscaling built through DevOps services.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
Get In Touch
Let's Build Something Remarkable Together
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