Solutions
The memory layer for the enterprise. Designed for scale, privacy, and precision.
Unified Memory
Most memory tools require MCP servers, multiple API connections, and ongoing integration maintenance. We attach at the most basic layer—your LLM API call. Just swap your provider key for an ISO key and we inject relevant context from Zendesk, Slack, Salesforce, Teams, Jira, and 40+ tools into every request. Same memory, zero overhead.
Automatic Information Collection
We connect to Zendesk, Slack, Salesforce, Teams, Jira, and 40+ other tools you already use. Every conversation, ticket, and document is automatically captured and organized. Want to build a dashboard for your data? Use Cursor—it has access to the same information. Want to write a customer response? The same data is available in Slack via Gemini. Same memory, different tools, different capabilities.
Zero Overhead Setup
Most tools force you to set up MCP servers, wire multiple APIs, and maintain complex integrations. We attach at the most basic layer: your LLM API call. Just swap your provider key for an ISO key. We call your LLM for you while injecting relevant memory into every request. No new infrastructure, no configuration drifts.
Instant Provider Switching
Claude costs spiking? Switch to Gemini with one click in the console. Your memory and context stay completely intact—same conversation history, same knowledge base, different provider. No migration scripts, no reconfiguration, no loss of context. The ISO key abstracts the provider so you can optimize for cost, speed, or capability on demand.
Token & Context Optimization
Traditional AI applications send full conversation history with every request, wasting tokens on irrelevant information. Our system uses semantic retrieval to send only the most relevant memory nodes instead of entire histories, achieving 40-60% token savings while improving accuracy.
Custom Dataset Curation
Every LLM query is automatically saved with full metrics and relevance scores. Use this data to build custom RAG systems, train specialized bots, or generate business insights. All prompts, responses, context nodes, and scores are exportable in JSONL or CSV.
Semantic Retrieval Instead of Full History
Instead of sending 10,000 tokens of conversation history, our BM25 + rerank algorithm analyzes your query and retrieves only the most relevant memory nodes (typically 300 tokens). Content-addressable storage eliminates duplicate information across requests.
Selective Memory Injection
Our system detects when context is actually needed. Simple queries like 'What's 2+2?' bypass memory injection entirely, while complex queries automatically retrieve relevant facts. Token budget management respects context window limits while maximizing information density.
End-to-End Observability
Multi-tab observability suite with real-time insights into every prompt, response, and quality metric. Track model performance, conversation quality, guardrail compliance, and full pipeline traces in one unified console.
Auto-Extracted Guardrail Monitoring
Guardrails are automatically inferred from your system instructions and user prompts — no manual configuration required. Tell your bot "You are a marketing assistant" and a TONE-Marketing guardrail is established instantly. The system then evaluates every message across all models and API keys, surfacing exactly where guardrails break. Nothing is blocked; violations are reported for review.
Automatic Dataset Extraction
Every interaction is automatically decomposed into a structured record with 20+ fields — prompt text, retrieved nodes, relevance scores, model choice, latency, token counts, and quality ratings. Export in JSONL for fine-tuning pipelines or CSV for business intelligence. No manual cleanup required.
Full Pipeline Telemetry
No blackbox. Every millisecond from request reception to token delivery is traced across eight pipeline stages — routing, BM25 retrieval, semantic reranking, context injection, provider API, response parsing, guardrail validation, and telemetry commit. See exactly where time is spent and why decisions were made.
Cross-Model Benchmarking
Compare any model head-to-head on cost per 1K tokens, latency p99, and six quality dimensions — groundedness, correctness, drift resistance, hallucination rate, safety compliance, and relevance. No more guessing which model to deploy. The data decides.
Memory Gap
Surface missing information from across your organization. See which business functions your AI handles well — and where institutional knowledge is missing.
Intelligence Coverage Map
Business functions most referenced by your workforce. Every conversation, ticket, and document is analyzed for coverage health. High-traffic areas like Enterprise Access and Revenue Operations show strong retrieval rates, while niche domains like Vendor Risk expose critical knowledge deficits.
Unanswered Query Detection
Live stream of questions with no supporting knowledge. When employees ask about data retention policies, board reports, or vendor certifications and get zero hits, those gaps are logged in real time with missing domain classification.
Coverage Trend Analysis
Track answered vs unanswered questions over time. When employee inquiries outpace your institutional knowledge, coverage gaps widen — surfacing exactly where to invest in documentation and policy.
Start building your memory layer
Connect your tools in minutes. No infrastructure, no maintenance, just memory that works everywhere.