Knowledge²

Stop Paying forAI Indecision.

AI pipelines waste tokens searching for the right context. We make your Agents and RAG precision tools.

Accuracy
Up to
35%Lift
Latency
Up to
5xLower
Cost
Up to
90%Lower
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1. VECTORIZING QUERY...
> Input:
"Standard isolation procedure"
> Status:
⚠ Muddled / Ambiguous
Isolate HostNetwork ProtocolViral MalwareIsolate PatientClinical ProtocolViral CultureRisk IsolationPolicy ProtocolViral Exclusion"Standard isolation"↓[0.02, -0.41, 0.91...]SEARCHING
Medical
Insurance
Cyber

Trusted by forward-thinking teams

Elevata
Yael Group
Canadian Insuretech Company
Elevata
Yael Group
Canadian Insuretech Company
Elevata
Yael Group
Canadian Insuretech Company
Elevata
Yael Group
Canadian Insuretech Company

Generic Models vs.Knowledge²

The Waste

  • Cold-start Retrieval
    Every query rebuilds context from zero.
  • Context Window Bloat
    Stuffing 100k tokens just to find one answer.
  • Hallucination Risk
    Without guardrails, the model guesses.

Knowledge²

  • Aligned Precision
    Retrieval paths are tuned to your specific data topology.
  • Pattern Reuse
    Cache successful plans so agents get smarter over time.
  • Staged Context
    Pre-fetch exactly what is needed, nothing more.
  • Compounding Accuracy
    Performance improves with every interaction.

Knowledge²
Platform

Retrieval Optimization

Model AlignmentAutomated tuning pipeline using synthetic query generation.
Retrieval-Optimized ModelsCustom embeddings and ranking tuned to your corpus.
RAG BlueprintsReference architectures for low-latency, low-noise retrieval.

LLM Cost Optimization

Semantic CachingReuse answers for semantically similar queries.
Query NormalizationRewrite inputs to maximize cache hits across workloads.

Agent Optimization

AgentBoostRuntime primitives for reusing plans, tools, and guardrails.

How K² Improves Your AI Workflows

Orchestration & Agents
LangChain, AutoGPT
K² Platform
Active Optimization Layer
Foundation Models & Infrastructure
OpenAI, Pinecone, AWS

Observability

Without K²Passive Monitoring
With K²Active Optimization

Generic Embeddings

Without K²Trained for Benchmarks
With K²Trained for Your Data

Vector DBs

Without K²Passive Storage
With K²Agentic Intelligence

See Knowledge² in action.

Watch how we reduce latency and cost while improving retrieval accuracy in real-time.

Get Started

Answers, upfront

Frequently asked questions

Straightforward answers so you can evaluate Knowledge² alongside your current retrieval stack.

Do I need to move my data or change my vector store?

No. You keep your vector store and existing infrastructure. K² delivers open weights for you to host or a secure API endpoint for your model. There is no data migration required.

How much data is needed to get started?

We recommend a starter set of roughly 500-1,000 high-signal documents and 50-250 production queries. More data helps, but we can begin with what you already have and iterate quickly.

How does this impact my LLM costs?

By delivering tighter retrieval and leaner prompts, customers regularly see double-digit reductions in input tokens and a corresponding drop in generation spend.

How quickly can we see the performance improvement?

Teams typically run pilots inside two weeks. Because we evaluate against your existing stack, you get clear deltas before committing to production rollout.

What data telemetry do you collect?

We only capture the evaluation signals you explicitly opt into. When you self-host the models, no runtime telemetry leaves your environment.

Is the juice worth the squeeze? What trade-offs should we expect?

Unified training and learned fusion do ask for a short pilot, but the payoff is immediate: higher recall, calibrated reranker scores, and lower token spend. Teams typically hit positive ROI once the models are serving just a few hundred high-value queries per day.