The foundation layer that scales with frontier-model powered products — collapsing warehouse, lake, stream processor, feature store, and queue into one system.
Built for teams like Lovable · Cursor · HeyGen · Synthesia · Tome — and every team building with GPT-4o, Claude, Mistral, or Llama 3.
Book a Technical SessionEvery day these remain unaddressed, you’re forced to compromise between speed, cost, and user experience.
Not another point solution. A foundation layer rebuilt from first principles — where indexed storage, not compute, bears the bulk of the query workload. 100–1,000x more efficient per query on a price/performance basis.
Our rebuilt distributed search architecture traverses petabytes through optimised index structures. Predictable latency regardless of data volume.
User interactions and model outputs become queryable within ~2 seconds of ingestion, enabling real-time personalisation and safety features.
Unified keyword and structured metadata search in a single query. No performance penalty for combining boolean logic with text relevance — critical for production RAG pipelines.
You lease infrastructure, not queries. ~5M queries/mo on base capacity. Costs 80–95% lower than pay-per-query warehouses at scale. No surprise bills when your app goes viral.
From $1,575/mo base + $1,200/TB/mo
Any dataset at any scale can be rebuilt from object store in ~3 hours. Essential for disaster recovery, DevOps at scale, and data sovereignty.
Your web engineers and full-stack devs integrate directly. No specialist data engineering team required to get started.
Current architectures force you to choose between retrieval speed and hybrid filtering capabilities. MinusOneDB eliminates that trade-off.
Combine keyword matching and structured metadata filters in a single request. No separate indices, no fan-out, no result merging in application code.
New documents are searchable within ~2 seconds of ingestion. Your RAG pipeline always returns current results, not stale snapshots.
Every piece of data maintains its complete provenance. Know exactly which sources contributed to each retrieval result — essential for evaluation and debugging.
| Capability | Current State | 30 Days | 90 Days |
|---|---|---|---|
| Context retrieval latency P95 | 500–700 ms | 180–250 ms | Constant-time, sub-100 ms |
| Traffic spike handling | Pre-provisioned or fail | Dynamic, 2× overhead | Dynamic, minimal overhead |
| Privacy request fulfilment | Manual, days | Semi-automated, hours | Automated, minutes |
| User memory quality | Generic, limited scope | Personalised, session | Personalised, persistent |
| Content safety coverage | Basic, high latency | Comprehensive, medium | Comprehensive, real-time |
Designed for resource-constrained teams that need impact without overhead.
Assemble the right context for each LLM interaction in seconds, combining user history, domain knowledge, and real-time signals in a single query.
Persistent, per-user memory that scales from early adopters to millions. Session context, preferences, and interaction history — queryable instantly.
~2 seconds from event to queryable data. Adapt recommendations, content, and behaviour in real time as users interact with your product.
Token-level logging with lineage tracking. Trace which data influenced which outputs. Run evaluation queries across your entire interaction history without query-cost anxiety.
