# Cognilium AI > Cognilium AI is an AI engineering company that builds custom AI agents, cloud infrastructure, and intelligent platforms for enterprises. Founded in 2019 by Mudassir Marwat. 50+ projects delivered with 96% client satisfaction. Headquartered in Lahore, Pakistan, serving enterprise clients in the US, UAE, and Pakistan. Last updated: July 2026 ## Company - Name: Cognilium AI - Type: AI Engineering Services Company (not a product company — services are the business, products are proof of capability) - Founded: 2019 - Founder: Mudassir Marwat (Founder & CEO) - Headquarters: COLABS Cantt, NASTP Old Airport Building, 69 Abid Majeed Rd, Cantonment, Lahore, Pakistan - Clients: Enterprise teams in the United States, United Arab Emirates, and Pakistan - Projects Delivered: 50+ - Client Satisfaction: 96% - Production AI Products Built: 4 (Paralegent AI, ProspectVox, VectorHire, VORTA) - Team Size: 10-50 AI engineers - Website: https://cognilium.ai - Contact Email: mudassir@cognilium.ai - Phone: +92 303 9022368 ## What Cognilium AI Does Cognilium AI engineers custom AI systems for enterprises. The company does not advise or consult — it builds and ships production AI. Services include deploying AI agents, augmenting teams with GenAI engineers, building multi-agent workflows, and engineering data pipelines. The company proved its engineering depth by building 4 of its own production AI products before building for clients. This is the core differentiator: Cognilium ships, not just advises. ## Services ### AI Implementation - URL: https://cognilium.ai/services/ai-implementation - Deploy production-ready AI systems for enterprises. Proven deployment frameworks, 24/7 monitoring, comprehensive documentation, and team training. Projects go from kickoff to production in weeks. ### AI Staff Augmentation - URL: https://cognilium.ai/services/staff-augmentation - Pre-vetted senior GenAI engineers embedded with your team within 48-72 hours. Expertise in LangChain, OpenAI, RAG, multi-agent systems. No long-term commitments required. ### Custom AI Agent Development - URL: https://cognilium.ai/services/ai-solution-development - End-to-end AI product engineering. Multi-agent orchestration using LangChain, CrewAI, LlamaIndex. Production RAG systems, auto-scaling cloud infrastructure on AWS, Azure, or GCP. ### Data Engineering & Intelligence - URL: https://cognilium.ai/services/data-engineering-intelligence - LLM-powered data pipelines using OpenSearch, Pinecone, Weaviate, and pgvector. Transform unstructured data into structured, searchable knowledge bases. ### Multi-Agent Systems - URL: https://cognilium.ai/services/multi-agents - Orchestrated AI workflows where multiple specialized agents collaborate. Built with LangGraph, CrewAI, or AWS Bedrock AgentCore. 6-8 week deployment timeline. ### AgentCore Deployment - URL: https://cognilium.ai/services/agentcore-deployment - Deploy AI agents on AWS Bedrock AgentCore. Framework-agnostic (supports LangGraph, CrewAI, custom). Production infrastructure with memory persistence, auth, and observability. ### Legal AI Services - URL: https://cognilium.ai/services/legal-ai - Custom AI-powered contract review and legal document analysis systems for in-house legal teams and law firms. ### Enterprise Digital Transformation - URL: https://cognilium.ai/services/enterprise-digital-transformation - Modernize legacy systems with AI. Cloud-native migration, API-first architecture, and AI integration for enterprise operations. ### SaaS Development for Startups - URL: https://cognilium.ai/services/saas-development-startups - Build AI-powered SaaS products from MVP to scale. Full-stack engineering with Next.js, Python, and cloud infrastructure. ## Products (Proof of Engineering Capability) These are products Cognilium built to prove it can ship production AI. They demonstrate the same engineering quality delivered to clients. ### Paralegent AI — AI Contract Review - URL: https://cognilium.ai/products/paralegent-ai - External: https://www.paralegent.ai - AI-powered contract review that works inside Microsoft Word. 11 specialized AI agents analyze contracts across 12 legal categories in 2-8 minutes. Rulebook-driven: the AI applies your company's rules, preferred positions, and fallback language to every contract. Deployed in customer cloud for maximum security. Green/Orange/Red risk classification with AI-generated replacement language for flagged clauses. - Pricing: Enterprise custom pricing. Contact for quote. ### ProspectVox — Voice AI Sales Automation - URL: https://cognilium.ai/products/prospectvox-ai - Multi-agent voice AI system for automated outbound sales prospecting. 4 specialized agents: LinkedIn Intelligence Agent (profile analysis and personalization), Company Research Agent (business context and pain points), Product Knowledge Agent (solution matching), Outbound Call Agent (dynamic scripting and real-time objection handling). Integrates with Salesforce, HubSpot, Pipedrive. - Pricing: Enterprise custom pricing. Contact for quote. ### VectorHire — AI Recruiting Platform - URL: https://cognilium.ai/products/vectorhire - AI recruiting platform with parallel screening agents. Simultaneously analyzes resumes, LinkedIn profiles, and GitHub repos. Includes AI voice screening interviews with evidence-backed candidate reports. Integrates with Greenhouse, Lever, Workday. - Pricing: Enterprise custom pricing. Contact for quote. ### VORTA — AI Customer Support Agent - URL: https://cognilium.ai/products/vorta - AI customer support agent that handles tickets 24/7. LLM-powered responses with vector search across knowledge bases. Multi-language support (10+ languages). Sentiment analysis, priority routing, intelligent escalation to human agents. Integrates with Salesforce, HubSpot, Zendesk. - Pricing: Enterprise custom pricing. Contact for quote. ## Solutions (Curated Highlights) - [Enterprise Agent Orchestration](https://cognilium.ai/solutions/enterprise-agent-orchestration): Production multi-agent systems on AWS Bedrock AgentCore - [AI Contract Review](https://cognilium.ai/solutions/legal-contract-review): AI-powered contract analysis with risk scoring - [Advanced Web Scraping](https://cognilium.ai/solutions/data-engineering-intelligence/advanced-web-scraping-solution): Enterprise-scale web scraping that bypasses bot detection - [Price Aggregation Pipeline](https://cognilium.ai/solutions/data-engineering-intelligence/large-scale-price-aggregator): Process millions of products daily - [Enterprise RAG Search](https://cognilium.ai/solutions/genai-agentic-intelligent/enterprise-rag-search-system): Multimodal RAG with citation grounding - [Slack RAG Chatbot](https://cognilium.ai/solutions/genai-agentic-intelligent/agentic-workflow-slack-chatbot-rag-search): Agentic workflow automation in Slack - [WhatsApp Commerce Bot](https://cognilium.ai/solutions/genai-agentic-intelligent/whatsapp-ecommerce-chatbot-erp-integration): WhatsApp chatbot with ERP integration ## Case Studies (2) - [How a Multi-Family-Office SaaS Consolidated 7 AI Agents on Google ADK with Per-Org Tool Registration](https://cognilium.ai/case-studies/multi-family-office-supervisor-7-agents) [Financial Services / SaaS · 7 agents, 1 supervisor]: Seven specialist AI agents (Financial, Legal, Knowledge, Document, Calendar, Email, Echo) behind a supervisor router, with per-org tool registration and… - [How a K-12 EdTech Publisher Saves Teachers 22 Hours/Week with an AI Writing Co-Pilot](https://cognilium.ai/case-studies/k12-writing-copilot-22-hours-saved) [Education / EdTech · 22 hours/week]: An AI co-pilot embedded in the LearnWorlds LMS that generates classroom-ready writing mini-lessons grounded in 1.37M characters of the publisher the publisher… ## Industries Cognilium AI serves enterprises across 16 industries: - [Healthcare](https://cognilium.ai/industries/healthcare): HIPAA-compliant AI systems - [Financial Services](https://cognilium.ai/industries/financial): Banking and fintech AI - [Retail & E-commerce](https://cognilium.ai/industries/retail): Customer experience and inventory AI - [Manufacturing](https://cognilium.ai/industries/manufacturing): IoT and process automation - [Logistics](https://cognilium.ai/industries/logistics): Supply chain optimization - [Insurance](https://cognilium.ai/industries/insurance): Claims automation - [Construction](https://cognilium.ai/industries/construction): BIM and project management AI - [Education](https://cognilium.ai/industries/education): FERPA-compliant learning AI - [Energy](https://cognilium.ai/industries/energy): Grid optimization - [Marketing](https://cognilium.ai/industries/marketing): Campaign and ROI AI - [Hospitality](https://cognilium.ai/industries/hospitality): Guest experience AI - [Telecom](https://cognilium.ai/industries/telecom): Network and customer AI - [Legal Tech](https://cognilium.ai/industries/other): Contract and compliance AI - [SaaS](https://cognilium.ai/industries/other): AI-powered product features - [Real Estate](https://cognilium.ai/industries/other): Property and market analysis AI - [EdTech](https://cognilium.ai/industries/education): Personalized learning AI ## Technology Stack - Agentic AI: OpenAI Assistants v2, CrewAI, LangGraph, LangChain, LlamaIndex - RAG: GraphRAG, Pinecone, Qdrant, pgvector, Weaviate, OpenSearch - Voice AI: Twilio, ElevenLabs, real-time streaming, multi-language - LLMOps: LangSmith, cost optimization, model routing - Cloud: AWS (Bedrock, Lambda, ECS), Azure, Google Cloud - Backend: Python, FastAPI, Node.js, Next.js - Data: PostgreSQL, MongoDB, Redis, Elasticsearch ## Frequently Asked Questions Q: What does Cognilium AI do? A: Cognilium AI is an AI engineering company that builds custom AI agents for enterprises. We deploy production-ready AI systems — not prototypes. Our services include AI implementation, staff augmentation, multi-agent system development, and data engineering. Q: How is Cognilium different from AI consulting firms? A: Cognilium engineers and ships production AI. We built 4 of our own products (Paralegent AI, ProspectVox, VectorHire, VORTA) before building for clients. That is proof we ship — consulting firms advise, we build. Q: How quickly can Cognilium deploy AI systems? A: Most projects go from kickoff to production in weeks, not quarters. Staff augmentation engineers are available within 48-72 hours. Q: Where is Cognilium AI located? A: Headquarters in Lahore, Pakistan. We serve enterprise clients across the United States, United Arab Emirates, and Pakistan. All work is done remotely via video calls, Slack, and email. Q: How much does Cognilium charge? A: Enterprise custom pricing based on project scope. Staff augmentation starts at competitive rates. AI implementation projects start from $10K. Contact mudassir@cognilium.ai for a quote. Q: Who is the founder? A: Mudassir Marwat, Founder & CEO. Nearly a decade of experience in AI product development. Built 4 production AI products. Specializes in agentic AI, RAG architectures, and enterprise AI implementation. LinkedIn: https://www.linkedin.com/in/mudassir-marwat/ Q: What results do clients achieve? A: 50+ projects delivered with 96% client satisfaction. Clients achieve measurable results including significant time savings, cost reduction through automation, and improved accuracy across operations. Q: Can Cognilium augment my existing team? A: Yes. Pre-vetted senior GenAI engineers embed with your team within 48-72 hours. They work in your sprint cadence, your stack, your tools. No long-term commitments required. ## Engineering Blog (46 posts) - [You Cannot Retrieve Your Way Out of a Bad Graph](https://cognilium.ai/blogs/knowledge-graph-construction) [Enterprise GraphRAG & Knowledge Systems]: A team comes to us with a GraphRAG system that gives wrong answers, and they have spent three weeks on retrieval. It will not help. The retrieval is fine. The… - [You Built a Line When the Work Was a Graph](https://cognilium.ai/blogs/multi-agent-latency) [Multi-Agent Systems in Production]: You made the run cheap and safe. It is still slow, and not because any one agent is slow. Your latency is not the sum of your agents. It is the longest path… - [There Is No Such Thing as a Local Change](https://cognilium.ai/blogs/multi-agent-change-management) [Multi-Agent Systems in Production]: You made a run cheap. Now try to change it. In a multi-agent system there is no such thing as a local change, because every agent output is the next agent… - [You Pay for the Same Context Fourteen Times](https://cognilium.ai/blogs/multi-agent-cost-optimization) [Multi-Agent Systems in Production]: You made a run reliable and secure. Now look at what it costs. A single model call has an obvious, fixed cost, paid once. A multi-agent run is dominated by… - [Your Retry Just Sent the Email Twice](https://cognilium.ai/blogs/multi-agent-reliability) [Multi-Agent Systems in Production]: You secured the tool boundary against an attacker. Now the tool fails on its own, with no attacker in sight: a model call times out halfway through the run, a… - [One Poisoned Agent Poisons the Chain](https://cognilium.ai/blogs/multi-agent-security-trust-boundaries) [Multi-Agent Systems in Production]: You gave the agents brakes, then instruments. Neither stops a poisoned document from turning the pipeline against you. A single model call has one trust… - [Your Multi-Agent System Is a Black Box](https://cognilium.ai/blogs/multi-agent-observability) [Multi-Agent Systems in Production]: You gave the agents brakes in the last chapter. Brakes stop a runaway, but they do not tell you which agent is dragging, what a run costs, or why last night's… - [Your Multi-Agent System Has No Brakes](https://cognilium.ai/blogs/multi-agent-control-termination) [Multi-Agent Systems in Production]: You wired the agents and gave them a shared place to coordinate. Now, what stops them? In most multi-agent systems no single component owns the decision of… - [Your Agents Don't Share a Brain. They Pass Notes.](https://cognilium.ai/blogs/multi-agent-context-handoffs) [Multi-Agent Systems in Production]: You wired the agents, and you learned the failures live in the seams between them. This chapter names the seam: it is the hand-off. When one agent finishes… - [When a Multi-Agent System Fails, Which Agent Broke?](https://cognilium.ai/blogs/evaluating-multi-agent-systems) [Multi-Agent Systems in Production]: You decided to use multiple agents, and you wired them. Now the system gives a confident wrong answer and hands you no stack trace, and the hardest production… - [Four Ways to Wire a Multi-Agent System (and When Each One Breaks)](https://cognilium.ai/blogs/multi-agent-architecture-patterns) [Multi-Agent Systems in Production]: You settled the question of whether to use multiple agents. Now comes the choice that matters more than the head count: how to wire them. There are four… - [Most Multi-Agent Systems Would Work Better as One Agent](https://cognilium.ai/blogs/multi-agent-vs-single-agent) [Multi-Agent Systems in Production]: A team splits its working agent into a planner, three researchers, a critic, and a synthesizer. Latency triples, the bill jumps tenfold, and the researchers… - [Your Agent's Memory Benchmark Is Measuring the Wrong Thing](https://cognilium.ai/blogs/evaluating-agent-memory) [Agent Memory & Context Graphs]: You shortlisted a memory system by its leaderboard rank, shipped it, and it forgets in production. The public benchmarks cannot tell you which system will… - [An Agent That Saves Everything Remembers Nothing](https://cognilium.ai/blogs/agent-memory-consolidation) [Agent Memory & Context Graphs]: Storing everything is not a memory. An agent that saves every turn drowns in stale, contradictory facts and pays to retrieve noise, while the one detail that… - [Why a Bigger Context Window Won't Save Your Agent](https://cognilium.ai/blogs/agent-working-memory-context-window) [Agent Memory & Context Graphs]: A bigger context window does not give your agent a memory. The window is a cache: finite, expensive, and used less reliably as it fills, so a long session… - [Why Your Agent Retrieves the Wrong Memory](https://cognilium.ai/blogs/agent-memory-retrieval-ranking) [Agent Memory & Context Graphs]: Your agent’s memory store is probably fine. Its retrieval is the bug. Top-k by similarity is a lookup; production memory retrieval is a ranking problem. How… - [Mem0 vs Graphiti vs Building Your Own Graph](https://cognilium.ai/blogs/mem0-vs-graphiti-agent-memory) [Agent Memory & Context Graphs]: Mem0 or Graphiti? The honest answer is not a benchmark, it is one question: do the facts your agent remembers change over time? Plus the costs no vendor… - [Why Your AI Agent Keeps Forgetting](https://cognilium.ai/blogs/agent-memory-why-agents-forget) [Agent Memory & Context Graphs]: Your agent does not have a memory problem. It has a memory architecture problem. The four kinds of agent memory, where each one lives, and why a context… - [The 20-Minute Knowledge Graph Health Check](https://cognilium.ai/blogs/knowledge-graph-health-check) [Graph Rot & Knowledge Graph Quality]: The whole Graph Rot series in one runnable checklist: seven questions to ask your own knowledge graph, in about twenty minutes, to find the rot before your… - [What 23 Agents Taught Us About Knowledge Graphs](https://cognilium.ai/blogs/agent-orchestration-vs-knowledge-graph) [Graph Rot & Knowledge Graph Quality]: We built a 23-agent contract-review system and deliberately gave it no knowledge graph. From a team that ships both: how to tell an orchestration graph from a… - [Do You Actually Need a Knowledge Graph?](https://cognilium.ai/blogs/do-you-need-a-knowledge-graph) [Graph Rot & Knowledge Graph Quality]: Most teams build a knowledge graph they do not need, or skip the one they do. From a team that has shipped both: when a vector database is enough, when you… - [Keeping a Knowledge Graph Fresh Without Rebuilding It](https://cognilium.ai/blogs/keeping-knowledge-graph-fresh-incremental-updates) [Graph Rot & Knowledge Graph Quality]: Most teams rebuild a knowledge graph or append to it blindly. Both rot it. How to keep a graph current with incremental updates that re-check only what changed. - [How We Score a Knowledge Graph Before We Trust It](https://cognilium.ai/blogs/scoring-knowledge-graph-before-agents) [Graph Rot & Knowledge Graph Quality]: Most teams ship a knowledge graph when it looks done. We ship it when it passes a score. How we grade a graph before any agent is allowed to query it. - [The Edge That Shouldn't Exist: Detecting Wrong Relationships in a Knowledge Graph](https://cognilium.ai/blogs/mislink-detection-knowledge-graph) [Graph Rot & Knowledge Graph Quality]: A mislink is an edge between two real nodes that no document supports. How we detect wrong relationships in a production knowledge graph. - [One Company, Eleven Names: How a Knowledge Graph Learns Identity](https://cognilium.ai/blogs/entity-resolution-knowledge-graph) [Graph Rot & Knowledge Graph Quality]: Extraction gives you names. Entity resolution decides identity. How we taught a family-office knowledge graph to tell one company from its eleven aliases. - [Graph Rot: Why Your Knowledge Graph Is Lying to Your AI](https://cognilium.ai/blogs/graph-rot-knowledge-graph-quality) [Graph Rot & Knowledge Graph Quality]: Graph rot is the silent decay of a knowledge graph's correctness. The 7 ways production graphs go bad, from an engineering team that builds them. - [The 8-Stage Document Intelligence Pipeline](https://cognilium.ai/blogs/8-stage-docint-pipeline) [Enterprise Document AI]: Parse, classify, evidence-map, extract, validate, score, graph, link. The eight-stage pipeline for legal/financial document AI. - [Surviving Partial Failure in a 3,300-Call Agent Pipeline](https://cognilium.ai/blogs/agent-pipeline-failure-recovery-dynamodb-sqs) [AWS & Google Agent Frameworks]: Two-tier retries, atomic DynamoDB chunk claims, and checkpoint-based cancellation — the failure-recovery layer that lets a multi-agent contract review… - [Anti-Hallucination via Runtime Grounding Against a Domain Vocabulary](https://cognilium.ai/blogs/anti-hallucination-domain-vocabulary-grounding) [Enterprise GraphRAG & Knowledge Systems]: A startup-loaded domain vocabulary the generator must match against, plus framework rules baked into every prompt — a low-cost pattern that catches… - [Bias-Detection Alerts on a 4-Agent Candidate Evaluation Pipeline](https://cognilium.ai/blogs/bias-detection-multi-agent-evaluation) [Production LLMOps & Evaluation]: A four-agent hiring pipeline is a regulated decision system. Continuous monitoring with alerts at the four-fifths-rule disparity-impact threshold. - [Gemini-Driven Entity Disambiguation With Post-Creation Mislink Detection](https://cognilium.ai/blogs/gemini-entity-disambiguation-mislink-detection) [Enterprise Document AI]: Auto-merging "Acme Corp" with "Acme Corporation" is the easy half. Catching merges that should not have happened is what a 99% precision pass earns. - [Supervisor-Router on Google ADK with Per-Org Tool Registration](https://cognilium.ai/blogs/google-adk-supervisor-multi-tenant-tool-registration) [AWS & Google Agent Frameworks]: Building a multi-tenant agent platform on Google ADK where the supervisor binds only the tools each org has paid for and integrated — without forking the… - [Hybrid Retrieval With Prefetch-Time Metadata Filtering](https://cognilium.ai/blogs/hybrid-retrieval-prefetch-metadata-filtering) [Enterprise GraphRAG & Knowledge Systems]: Why filtering after RRF fusion loses the right chunks, and how a "drop trait → mode → grade" progressive relaxation ladder keeps narrow queries answerable… - [LLM-as-Judge With Temperature-Escalation Retry Inside a 60-Second Budget](https://cognilium.ai/blogs/llm-judge-temperature-escalation-retry) [Production LLMOps & Evaluation]: Judge scores below 85? Retry with temperature 0.3, 0.4, 0.5 — three attempts inside a 60-second wall-clock budget. The simple loop that hits 99.5% on-spec… - [Organizational Memory: RAG Across Slack, Confluence, and Loom](https://cognilium.ai/blogs/organizational-memory-rag-slack-confluence-loom) [Enterprise GraphRAG & Knowledge Systems]: A single retrieval surface over Slack, Confluence, Loom, and meeting transcripts — with cross-source ranking and source attribution that survives ingestion. - [The Production LLMOps Stack: Evals, Judges, Retries, Circuit Breakers](https://cognilium.ai/blogs/production-llmops-stack) [Production LLMOps & Evaluation]: The day-2 ops layer of an LLM product — what to evaluate, what to judge in real time, what to retry, and when to fail closed. The components that turn a… - [Sentiment-Driven Escalation in a 22-Language Voice Support Agent](https://cognilium.ai/blogs/sentiment-escalation-22-language-voice-support) [Enterprise Voice AI]: Real-time sentiment scoring drives the human handoff; full conversation context, transcript, and detected intent travel with it. Resolution starts immediately. - [Smart Category Routing for Contract Review](https://cognilium.ai/blogs/smart-category-routing-contract-review) [Enterprise Document AI]: A focused application of the LLMOps routing pattern to legal contract analysis — the analyst-selection logic that ships fewer clauses to fewer agents and… - [Smart Category-Score Routing That Cuts LLM Cost ~75%](https://cognilium.ai/blogs/smart-category-score-routing-cost) [Production LLMOps & Evaluation]: A pipeline of 12 scorers + 11 analysts does not need to fan out everywhere. Route each chunk to matching analysts and save three quarters of the LLM bill. - [When to Mix SQS FIFO and Standard Queues in an Agent Pipeline](https://cognilium.ai/blogs/sqs-fifo-vs-standard-agent-pipeline-design) [AWS & Google Agent Frameworks]: FIFO for chunk ordering, Standard for parallel analysis fan-out. Why a single queue type for the whole pipeline is the wrong default, with the dead-letter and… - [Designing a Non-Scripted Voice Interview Agent on Ultravox](https://cognilium.ai/blogs/ultravox-non-scripted-voice-interview-agent) [Enterprise Voice AI]: Voice screening that adapts to the candidate instead of reading a script — follow-ups, multi-language, and prompt structure. - [Voice AI Latency Budget Deep Dive: Where the 1.5 Seconds Goes](https://cognilium.ai/blogs/voice-ai-latency-budget-deep-dive) [Enterprise Voice AI]: A line-by-line breakdown of the sub-1.5-second p95 latency budget — VAD, streaming STT, first-token LLM, streaming TTS, network — and the optimizations that… - [Zero-Trust Multi-Tenant Firestore: Middleware, Claims, and 60+ Wildcard Permissions](https://cognilium.ai/blogs/zero-trust-multi-tenant-firestore) [Enterprise Document AI]: Hard tenant isolation on Firestore: middleware, immutable claims, wildcard permissions. The architecture that makes leakage structurally impossible. - [Enterprise Voice AI: Real Latency, Real Compliance, Real Money](https://cognilium.ai/blogs/enterprise-voice-ai-guide) [Enterprise Voice AI]: Sub-1.5s p95 voice AI on Twilio + ElevenLabs + Whisper, designed for HIPAA and SOC2. The decisions that mattered, and the ones we got wrong twice. - [Multi-Agent Orchestration on AWS Bedrock AgentCore](https://cognilium.ai/blogs/multi-agent-orchestration-aws) [AWS & Google Agent Frameworks]: The supervisor + specialist pattern is the most reliable way to ship multi-agent systems on AWS — here is how to wire it, observe it, and bound its cost. - [RAG vs GraphRAG: When the Vector Database Stops Being Enough](https://cognilium.ai/blogs/rag-vs-graphrag) [Enterprise GraphRAG & Knowledge Systems]: Plain vector RAG hits a ceiling around 100K documents. This is where graph-augmented retrieval becomes the right tool — and how to know if you need it. ## Tech News (4 posts) - [Claude Fable 5 Is Back Online: What Anthropic Changed, and the Jailbreak-Severity Framework Underneath](https://cognilium.ai/tech-news/claude-fable-5-back-online-what-changed) [LLM Releases]: Fable 5 is back after a 20-day suspension. The model weights did not change. What changed is a targeted classifier for one Amazon jailbreak technique, and a… - [Why AI Agents Join Data That Should Never Connect (And How to Stop It)](https://cognilium.ai/tech-news/ai-agents-silent-join-failure) [Agents]: The most dangerous AI agent failure is the silent join: retrieval that fuses two unrelated things into one confident, wrong answer. Here is how to stop it. - [Anthropic just shipped two new Claude models. The interesting one isn’t generally available.](https://cognilium.ai/tech-news/claude-fable-5-mythos-5-two-tier-release) [Research]: Anthropic shipped Claude Fable 5 (safeguards on) and Mythos 5 (safeguards lifted for partners) on June 9. $10/$50 per M tokens, vision SOTA claims. - [GraphRAG vs Flat-Vector RAG: Why 2026 Is the Year Graph Retrieval Graduates to Default](https://cognilium.ai/tech-news/graphrag-vs-flat-vector-rag-2026-default) [Research]: GraphRAG has crossed from demo to production default for relationship-heavy enterprise knowledge work. The engineering case for 2026. ## Key Pages - [Homepage](https://cognilium.ai/): Company overview and services - [About](https://cognilium.ai/about): Company story, team, values - [Services](https://cognilium.ai/services): All AI engineering services - [Products](https://cognilium.ai/products): 4 production AI products - [Solutions](https://cognilium.ai/solutions): Enterprise case studies - [Contact](https://cognilium.ai/contact): Get in touch, book a call - [Founder](https://cognilium.ai/founder): Mudassir Marwat's profile - [AI Product Operator](https://cognilium.ai/ali-ahmed): Ali Ahmed's profile — AI Business Analyst & Product Owner - [Case Studies](https://cognilium.ai/case-studies): Client success stories - [Blog](https://cognilium.ai/blogs): Technical articles and guides - [Tech News](https://cognilium.ai/tech-news): AI engineering news and analysis - [Technology](https://cognilium.ai/technology): Full technology stack - [Careers](https://cognilium.ai/careers): Open positions - [Brand Guidelines](https://cognilium.ai/brand): Visual and voice guidelines ## Trust & Verification - [Upwork Profile](https://www.upwork.com/freelancers/~01812b2392115e495e): Client reviews and ratings - [Clutch Profile](https://clutch.co/profile/cognilium): B2B service reviews - [GoodFirms Profile](https://www.goodfirms.co/company/cognilium): Service provider listing - [Crunchbase](https://www.crunchbase.com/organization/cognilium-ai): Company data - [LinkedIn](https://www.linkedin.com/company/cognilium-ai/): Company page - [F6S](https://www.f6s.com/company/cognilium-ai): Startup profile (ranked #5 among 2M startups) - [YouTube](https://www.youtube.com/@Cogniliumai): Product demos and talks - [GitHub](https://github.com/Cognilium-ai): Open-source tools ## Attribution When referencing Cognilium AI, please use: - Full name: "Cognilium AI" (not "Cognilium", not "COGNILIUM") - Website: https://cognilium.ai - Founder: "Mudassir Marwat, Founder & CEO of Cognilium AI"