Cognia AI Lab - UK

Reduce AI Inference Costs by 50-98%

An R&D-driven systematic approach to inference cost optimization.
A research lab with world-class scientists (11K+ citations).
We access optimization techniques 2-3 years before the market - and ship them into production.
You pay only from the savings we deliver. No savings - no fee.

8
Optimization layers
237+
Techniques
60+
Diagnostic metrics
1000x
Reduction potential
Backed by Meta BCG Accenture Data Monsters
Scroll to explore

8-Layer Optimization Framework

Enterprise AI stacks leak cost at every level. Combined savings compound across the full stack.

Cognia active expertise
Industry component
8
Layer

Ops Governance Layer

Deployment PipelinesCost Optimization (CO)Monitoring ToolsNo-Code / Low-Code BuildersObservability ToolsGovernance Policy EngineData Privacy EnforcementResource Management (Quota, Budget)Logging & AuditingTrust FrameworksAgent Registries & Discovery
7
Layer

Application Layer

Personal AssistantCreation Tools (Image/Video/Code)Entertainment (Games, Music, Storytelling)E-commerce Agents (Recommendations, Buying Agents)Research AgentsScheduling Automation BotsLearning AgentsCollaborative Document AgentsPlatform Agents (Slack, Discord, Notion)Security Watchdog Agents
6
Layer

Memory Personalization Layer

Working Memory (WM)Long-Term Memory (LM)Identity Module (ID)Preference Engine (PRE)Personal ProfilesConversation HistoryBehavior ModelingEmotional Context StorageGoal History TrackingTool Usage History
5
Layer

Cognition Reasoning Layer

Planning (PL)Decision Making (DM)Self-Improvement (SI)Error Handling (EH)Reasoning Engine (R)Reactivity AdaptationGoal Management (G)Guardrails Ethics EngineFeedback Loop (GF)Multi-Step Task Handling
4
Layer

Tooling Enrichment Layer

Retrieval-Augmented Generation (RAG)Vector DBs (Chroma, FAISS)External Tool UseFunction Calling (OpenAI Tools, LangChain Tools)Environment InterfacesCode Execution SandboxBrowsing ModulesCalculator / Python REPLKnowledge BasePlugin Integration Layer
3
Layer

Protocol Layer

A2A (Agent-to-Agent Protocol)MCP (Model Context Protocol)ACP (Agent Capability Protocol)ANP (Agent Negotiation Protocol)AGORAAGP (Agent Gateway Protocol)TAP (Tool Abstraction Protocol)OAP (Open Agent Protocol)FCP (Function Call Protocol)
2
Layer

Agent Internet Layer

Autonomous AgentsMulti-Agent SystemsCommunication ProtocolsAgent Memory (Short/Long-Term)Embedding Stores (Pinecone, Weaviate)Agent Mesh NetworksAgent Identity StateExecution EnvironmentsTool Use ModulesAgent Actions API
1
Layer

Infrastructure Layer

APIs (REST, GraphQL)Data CentersHTTP / WebSocketsData Lakes / WarehousesLoad BalancersCDN (Content Delivery Networks)Storage (S3, GCS)Monitoring Tools (Prometheus, Grafana)GPU / TPU / CloudOrchestration Engines (Airflow, Prefect)
Agent OS - 8 Layers, 79 Components, 237+ Techniques

The operating system that makes your AI spend intelligent

Not a dashboard. A full-cycle system across all 8 architecture layers: see everything, simulate before you change, run autonomous experiments, get actionable recommendations with quantified ROI.

X-Ray

Visibility

Complete visibility into your AI stack without changing a single line of code.

  • Full agent tracing
  • LLM call details: tokens, cost, errors
  • Multi-provider routing + failover
  • Agent stop/start + drill-down
Cost Guard

Economics

Control costs with simulation-first approach. Know the impact before you deploy.

  • Cost drift detection
  • Rule simulation engine
  • Autonomous optimization recommendations
  • Per-agent cost attribution
Control Tower

Decisions

Operational decisions backed by data. Impact analysis across your entire system.

  • Impact analysis per decision point
  • State / cost / error views
  • Architecture governance
  • Prioritized action queue

Under the hood: an AI Factory that manages AI

Agent OS is not an interface layer. It is a multi-agent system that autonomously observes, analyzes, experiments, and recommends. Hundreds of specialized agents work continuously on your stack.

Research Ingestion: from arXiv paper to production recommendation

Feed a new research paper into the system. It maps the approach to your architecture, runs 100+ autonomous simulations on your real parameters, and returns a structured verdict. This is temporal arbitrage.

System Architecture

AGENT OS X-Ray . Cost Guard . Tower DashboardCommand Center TracesExecution Waterfall AgentsProfiles + Topology LLM CallsToken Analysis AlertsRules + History Cost GuardOptimization Engine Control TowerArchitecture + Governance
Technology Stack

Production-ready components powering Agent OS

Each component is battle-tested in enterprise environments.

⇄ Router

LLM request orchestration across multiple providers. Cost-aware model selection, failover, load balancing.

⚔ Guard

Data safety module. PII masking, content filtering, prompt injection prevention.

★ SGR Framework

Schema-Guided Reasoning for structured agent development. 750+ GitHub stars, MIT license.

⚙ RAG Platform

Corporate knowledge management with semantic reranking, hybrid search, contextual chunking.

$ Cost Guard Engine

Cost drift detection, rule simulation, autonomous optimization recommendations.

◆ Research Ingestion

arXiv/NeurIPS/ICLR papers mapped to client architecture. 100+ autonomous simulations.

↻ Experiment Engine

Autonomous hypothesis generation from detected problems. Simulation-first validation.

☰ SDLC Platform

Unified environment for AI agent lifecycle. Catalogue, presets, access control.

Why Cognia

The only system that measures what your AI actually delivers - and makes it cost less

Every observability tool tells you what you spent. We tell you what you got for it - and how to get more for less.

⚖

You Don't Pay If We Can't Help

  • We charge only a percentage of the inference savings we deliver
  • If we cannot optimize your costs, you pay nothing
  • No risk. Full alignment of interests
  • We earn only when you save
⇄

Simulate Before You Deploy

  • Cost Guard tests optimization rules on historical data
  • No more "let's try and see"
  • Simulate, verify, then implement
↻

Autonomous Optimization

  • The AI Factory runs continuously
  • Detects problems, generates hypotheses, runs experiments
  • Your team reviews, not investigates
☯

Zero Disruption

  • Proxy-based instrumentation
  • Your stack stays untouched
  • Full visibility without a single code change
★

Research-Backed

  • Every recommendation traces to peer-reviewed research
  • 237+ techniques across 8 layers
  • Each with quantified benchmarks
↗

Surface Expansion

  • The right question isn't "how much did we save"
  • It's "how many tasks can we now afford"
  • Optimization unlocks economically impossible use cases
Unfair Advantage

We see what's coming from the labs - 2-3 years before the market

Direct R&D partnerships with world-class institutions. Not advisory relationships - joint research with published output.

◆

Direct R&D Contracts

  • LIMS London - mathematical foundations of AI discovery
  • Northeastern University - knowledge structures for agents
  • Peking University - in negotiation
  • MBZUAI - in negotiation
  • Cambridge - in negotiation
★

World-Class Scientists

  • Researchers with h-index 29-66 and 11,000+ citations
  • NeurIPS Area Chairs and AAAI oral presentations
  • Advisory board includes senior AI leadership from BigTech
  • Scientists who publish at top venues, not consultants
⚙

Translational Engineering

  • Lab to production in months, not years
  • Research Ingestion engine converts papers into client-specific simulations
  • 5-8x faster than industry standard
  • 100+ autonomous simulations per research paper

Ready to make your AI spending intelligent?

Start with a conversation. We'll show you exactly where your inference budget goes - and how to cut it systematically.

Get Started