--- title: "AI Systems We Built: Real Enterprise Case Studies" canonical_url: "https://cognilium.ai/solutions" slug: "solutions" section: "solutions" page_type: "pillar" date_modified: "2026-09-29" description: "Real AI systems running in production. RAG search engines, multi-agent workflows, web scrapers, AI chatbots. See what we engineered for enterprises." publisher: "Cognilium AI" author: "Mudassir Marwat" person_same_as: - "https://www.linkedin.com/in/mudassir-marwat/" - "https://orcid.org/0009-0008-1927-2598" - "https://github.com/mudassirmarwat" - "https://medium.com/@mudassir-marwat" - "https://www.youtube.com/@mudassir-marwat" - "https://dev.to/mudassirmarwat" - "https://hashnode.com/@mudassirmarwat" - "https://huggingface.co/mudassirmarwat" - "https://bsky.app/profile/mudassir-marwat.bsky.social" - "https://substack.com/@mudassirmarwat" - "https://topmate.io/mudassirmarwat" - "https://fueler.io/mudassirmarwat" - "https://www.instagram.com/mudassirmarwat/" - "https://www.facebook.com/mudassir.marwat" - "https://www.reddit.com/user/mudassirmarwat/" - "https://www.f6s.com/member/mudassir-marwat" - "https://cognilium.ai/founder" answers: - "Custom AI development, shown by what it built" - "The foundations AI stands on" - "Figures from named systems, not blended averages" - "One workflow, one metric, then a decision" - "Six verticals, each with a system behind it" - "The stack these systems run on" entities: [] related: --- # AI Systems We Built: Real Enterprise Case Studies **Enterprise RAG Search System** RAG that actually finds answers. 10M+ documents, sub-2-second response. ## In short **Enterprise RAG Search System** RAG that actually finds answers. 10M+ documents, sub-2-second response. **Agentic Slack Chatbot with RAG** Multi-agent Slack bot that searches meeting history and creates tasks automatically. **WhatsApp eCommerce Chatbot** WhatsApp bot that knows your inventory, processes orders, and syncs with ERP. **Enterprise Agent Orchestration** Production multi-agent platform — supervisor + worker topology with state checkpointing and audit trail. **Document Intelligence Pipeline** 8-stage pipeline — parse, classify, extract, validate, score, graph, link — for unstructured PDFs at scale. **Multi-Tenant SaaS AI Platform** Per-org tool registration, zero-trust isolation, usage billing — the AI architecture for SaaS founders. **Enterprise Knowledge Graph** GraphRAG over heterogeneous corporate data — entities, relationships, multi-hop queries beyond vector-only retrieval. **Microsoft Word AI Add-In** In-document AI redlines + clause analysis with <2s round-trip. Office.js + Cloudflare Workers + LLM proxy. **AI Content Co-Pilot (LMS-Embedded)** RAG-grounded teaching assistant for Canvas / Blackboard / LearnWorlds. Cuts content prep 4hr → 12 min. **AWS Bedrock AgentCore Deployment** Productized AgentCore deployment — guardrails, memory, observability, multi-env IaC, customer-VPC topology. **24/7 Voice AI Customer Support** Text + voice multilingual support — many languages, sentiment escalation, real-time CRM integration. **Voice AI Candidate Screening** Adaptive follow-up questioning, multi-language, evidence-driven structured scoring with ATS write-back. **B2B Sales AI Outreach** LinkedIn intelligence + multi-channel outreach + CRM-integrated personalization. AI SDR at scale. **AI Recruiting with ATS Integration** Parallel-agent resume screening — skills, experience, culture, comp — with explainable scoring and ATS write-back. **AI Quoting Engine for Distributors** RFQ intake to reviewed quote for sourcing businesses — messy lists read, margins applied, uncertain lines marked. Phased, no ERP rebuild. **Legal Contract Review** 11-agent specialized contract review pipeline with Word add-in distribution and rulebook-driven analysis. **Advanced Web Scraping Solution** Custom scrapers for sites with anti-bot protection — rotation, fingerprinting and rate discipline, at scale. **Large Scale Price Aggregator** Industrial-scale pipeline reading price, stock, ratings and reviews from ~20 major retailers on a daily cycle. **E-commerce & retail** A price-aggregation product of our own, reading price, stock and reviews from ~20 major retailers on a daily cycle. **Legal** Paralegent AI, our contract-review product: 23 agents reviewing against a firm's own playbook, distributed in a Word add-in. **Distribution & ERP** A C-level decision assistant over the ERP for a parts manufacturer, in the chat tool the executives already used — and Quote Rabbit, quoting for Business Central distributors. **Family-office finance** A multi-tenant platform with a temporal knowledge graph, document intelligence across the document types in scope, and evidence-based answers. **Recruiting** VectorHire: resume, LinkedIn, GitHub and live voice-interview agents screening candidates in parallel. **Education** A K-12 teaching platform's AI layer: hybrid retrieval engineered to the client's own books, with an LLM judge scoring every lesson before it ships. **A working session** Bring the real workflow — the inbox, the spreadsheet, the document pile. We tell you what would actually move it, and just as plainly when something will not work. **A pilot: one workflow, one metric** Four to six weeks, scoped to a single workflow with one measurable metric defined up front. Small enough that you can afford to be wrong about it. **The decision, with numbers on the table** The pilot's measured result justifies the next phase, or tells us both to stop. Every later phase is a separate conversation, had against evidence. **What AI systems does Cognilium build?** Five kinds, and every one exists on this page as a built system rather than a service category: agents that carry out real work inside a company's tools, answers drawn from your own documents and data (RAG, knowledge graphs, NL2SQL), industrial-scale web scraping and data pipelines, documents read and understood with auditable confidence, and conversations held by voice. **How quickly can you implement an AI solution?** We start small on purpose: a working session first, then a pilot scoped to one workflow with one measurable metric, typically four to six weeks. The pilot's measured result is what justifies going further, or tells us both to stop. We would rather size the timeline to your workflow on a call than promise a number a page cannot know. **What ROI can we expect from your AI systems?** We do not publish ROI promises. Outcomes are measured against your own baseline: the pilot defines one metric up front, and the system's result on that metric is the evidence. Where we do publish figures they come from named systems — like the 75% cut in LLM calls that smart routing produced in Paralegent, our own contract-review product. **What scale can your solutions handle?** We design each system for the scale its workload actually has: document-scale RAG over full enterprise corpora, scrapers that re-collect entire catalogs on a daily cycle, multi-agent systems that run workflows concurrently rather than in queues, chatbots and voice agents that hold many sessions at once, and data pipelines with failover designed in. Before cutover we load-test against your expected volume, and capacity scales with the data instead of being provisioned as a fixed ceiling. **Which industries do your systems run in?** The record is concrete: e-commerce and retail (our own price-aggregation product), legal (Paralegent, our contract-review product), distribution and ERP (a delivered decision assistant for a parts manufacturer, plus Quote Rabbit for Business Central), family-office finance, recruiting (VectorHire), and education (a K-12 teaching platform's AI layer). If your industry is not on that list, the mechanisms still transfer — and we will say plainly whether they fit. **Why work with Cognilium instead of building in-house?** Not a promise — a head start. The patterns our engineers deploy are the ones already running in our own production systems: guardrails written in code rather than prompts, retrieval architectures where every answer carries its citation, infrastructure as code on all three major clouds, observability from day one. And you keep the source code and the documentation either way, so nothing depends on us staying in the room. **What technology stack powers your solutions?** LLMs (GPT-4, Claude, Llama, Mistral), vector databases (Pinecone, Weaviate, Qdrant), orchestration (LangGraph, CrewAI, Google ADK, AWS Bedrock AgentCore), web scraping with browser grids, proxy rotation and fingerprint discipline, infrastructure as code on AWS, GCP and Azure, and monitoring with OpenTelemetry, CloudWatch and LangSmith. All solutions include source code, documentation, and deployment support. **What support and maintenance do you provide?** Every system ships with monitoring and defined escalation paths, and we stay available for optimization and fixes after deployment. What we do not sell is a boilerplate SLA: support is scoped to the system we built for you, and knowledge-transfer sessions are part of delivery — your team should never be dependent on us to understand what it runs. It builds agents that carry out real work, answers drawn from a company's own data, documents read and understood, and conversations held by voice — and it proves its engineering on its own products. Every card on this page is a system that exists, with its own page and its own story. systems on this page, each with its own case study AI agents in production across four systems clouds in production, each with infrastructure as code Colourful network cables in a patch panel ## Custom AI development, shown by what it built Multi-agent systems, RAG implementations, and conversational AI that understand context and deliver results. Not categories — fifteen systems, each with its own page. ## The foundations AI stands on Industrial-scale pipelines and web scraping built to survive real load: recovery, validation and monitoring designed in, down to a 250TB Spark pipeline. This is where Cognilium started in 2019, and every AI system we build still stands on this discipline. ## Figures from named systems, not blended averages fewer LLM calls from smart routing, in Paralegent, our own contract-review product database-load reduction from Redis caching, in a marketplace monitor we run Everything else is measured against your own baseline: the pilot defines one metric up front, and the system's result on that metric is the evidence. Security practice is aligned with SOC 2 and GDPR practices — stated as practice, not waved as a certificate. ## One workflow, one metric, then a decision We do not propose platforms. We propose a first build small enough to be provably right — and every later phase is a separate conversation, had with the previous phase's numbers on the table. ## Six verticals, each with a system behind it No logo wall and no invented industries — one real build per vertical, described the way the record allows. ## The stack these systems run on Chosen per system, proven in production — and every delivery includes the source code and the documentation. Asked before every engagement, answered honestly A working session on your own data beats a slide deck: bring the inbox, the spreadsheet or the document pile, and we will tell you what would actually move it — and just as plainly when something will not work. --- Canonical HTML: https://cognilium.ai/solutions