AI systems businesses run every day
Cognilium AI is an AI company. 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.
17
systems on this page, each with its own case study
37
AI agents in production across four systems
3
clouds in production, each with infrastructure as code
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.
Enterprise RAG Search System
RAG that actually finds answers. 10M+ documents, sub-2-second response.
- 10M+ documents indexed
- <2 sec response
Agentic Slack Chatbot with RAG
Multi-agent Slack bot that searches meeting history and creates tasks automatically.
- Grounded, citation-traceable answers
- Runs continuously
- Hours returned to the team
WhatsApp eCommerce Chatbot
WhatsApp bot that knows your inventory, processes orders, and syncs with ERP.
- 24/7 availability
- Many languages
- Escalates on sentiment or complexity
Enterprise Agent Orchestration
Production multi-agent platform — supervisor + worker topology with state checkpointing and audit trail.
- LangGraph + Bedrock
- Sub-2.5s p99
- Full observability
Document Intelligence Pipeline
8-stage pipeline — parse, classify, extract, validate, score, graph, link — for unstructured PDFs at scale.
- <2s per 10-page doc
- EHR + Guidewire ready
Multi-Tenant SaaS AI Platform
Per-org tool registration, zero-trust isolation, usage billing — the AI architecture for SaaS founders.
- Per-tenant routing
- Isolation enforced at the data layer
- Tenant provisioning in minutes
Enterprise Knowledge Graph
GraphRAG over heterogeneous corporate data — entities, relationships, multi-hop queries beyond vector-only retrieval.
- Entity resolution + hybrid retrieval
- Multi-hop graph queries
- Answers grounded in your corpus
Microsoft Word AI Add-In
In-document AI redlines + clause analysis with <2s round-trip. Office.js + Cloudflare Workers + LLM proxy.
- In-document round-trip
- Precision validated per deployment
- MS AppSource ready
AI Content Co-Pilot (LMS-Embedded)
RAG-grounded teaching assistant for Canvas / Blackboard / LearnWorlds. Cuts content prep 4hr → 12 min.
- Follows the methodology you supply
- Hours returned to teaching
- LTI 1.3 Advantage
AWS Bedrock AgentCore Deployment
Productized AgentCore deployment — guardrails, memory, observability, multi-env IaC, customer-VPC topology.
- 5-day MVP deployment
- <200ms p95 latency
- Multi-region failover
24/7 Voice AI Customer Support
Text + voice multilingual support — many languages, sentiment escalation, real-time CRM integration.
- <600ms first-token latency
- Escalation deflection
Voice AI Candidate Screening
Adaptive follow-up questioning, multi-language, evidence-driven structured scoring with ATS write-back.
- Parallel screening at scale
- Shorter time-to-shortlist
- Multilingual support
B2B Sales AI Outreach
LinkedIn intelligence + multi-channel outreach + CRM-integrated personalization. AI SDR at scale.
- LinkedIn enrichment
- Intent-timed calling
- Multi-channel follow-up
AI Recruiting with ATS Integration
Parallel-agent resume screening — skills, experience, culture, comp — with explainable scoring and ATS write-back.
- Parallel resume screening
- Ranked against your own criteria
- Explainable + audit-ready
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.
- Quotes in minutes, not hours
- A person always presses send
- Keeps QuickBooks
Legal Contract Review
11-agent specialized contract review pipeline with Word add-in distribution and rulebook-driven analysis.
- 2-8 min reviews
- Cited findings
- 11 AI specialists
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.
Advanced Web Scraping Solution
Custom scrapers for sites with anti-bot protection — rotation, fingerprinting and rate discipline, at scale.
- 10M+ products weekly at peak
- 6-tier anti-bot escalation
- Self-healing parsers
Large Scale Price Aggregator
Industrial-scale pipeline reading price, stock, ratings and reviews from ~20 major retailers on a daily cycle.
- Millions of product URLs daily
- ~20 major retailers
- Six-tier anti-bot escalation
Figures from named systems, not blended averages
75%
fewer LLM calls from smart routing, in Paralegent, our own contract-review product
97%
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.
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.
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.
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 25 document types, 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.
The stack these systems run on
Chosen per system, proven in production — and every delivery includes the source code and the documentation.
Common questions
Asked before every engagement, answered honestly
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.
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.
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.
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.
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.
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.
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.
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.
Bring the workflow that hurts
This is our AI engineering practice
It is real work and it is where our four products came from. But what Cognilium leads with is narrower: optimization apps that run in tandem with Microsoft Dynamics 365, computing the decisions the ERP records but does not derive — the optimal price, the optimal pick path, the optimal stock level. See the optimization apps · How we build inside the ERP.