// precoglabs — system_status: active

Sovereign
Intelligence.
Deployed.

Powering elite private and public institutions with high-efficiency vector compression and AI memory management. The infrastructure layer the next era of AI demands.

Explore Memory Matrix Request Access
01 — Compression
02 — Memory Layer
03 — Vector DB
04 — In-Memory Cache
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~10×
Compression Ratio
98.3%
Cosine Fidelity
11.4×
Higher Throughput
<1ms
Cache Latency
// architectural_directive

The memory wall is real.
Transformer size grew 240× in a decade.
Memory bandwidth grew 1.6×.

The Hidden Bottleneck

AI Has a
Memory Problem.

The greatest limitation in many AI systems today is no longer intelligence alone. It is memory. Every AI agent that remembers context, every copilot that retrieves from a knowledge base, and every RAG pipeline serving real-time results is placing direct pressure on the memory layer.

That layer, in most production systems today, was never designed for this workload. This creates the hidden tax on AI: duplicated data, delayed responses, inflated infrastructure spend, and degraded user experience.

10–50×
Storage Overhead
Uncompressed vector embeddings vs. compressed alternatives in production systems.
80–120 GB
RAM Required
For HNSW index on 10M 1536-dim vectors. A single enterprise deployment.
30–60%
Performance Drop
Long-context LLMs vs. purpose-built memory architectures (LongMemEval, ICLR 2025).
$700B
Infrastructure Spend
Projected Big Tech AI spend in 2026 — disproportionately consumed by inefficiency.
Core Innovation

Three-Layer Architecture.

A purpose-built performance framework where each layer amplifies the others — unified by TurboQuant compression.

IN-MEMORY CACHE VECTOR DATABASE AI MEMORY LAYER TURBOQUANT
01 AI Memory Layer
Continuity & intelligence — structured memory, contextual persistence, intelligent recall across sessions, agents, and workflows. User-scoped and agent-scoped isolation at the architecture level.
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02 Vector Database
Relevance & retrieval — semantic access via pgvector with 471 QPS at 99% recall on 50M vectors. 11.4× higher throughput than Qdrant. Extends from 50K to 500K users before migration.
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03 In-Memory Cache
Speed & acceleration — first-of-its-kind acceleration layer for hot-path memory retrieval, active session state, and compressed embedding representations at sub-millisecond latency.
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// unified_engine
TurboQuant™ Compression
Google Research validated. ICLR 2026 accepted. Near-Shannon-optimal distortion.
4-bit — Default
~8×
99.5% cosine fidelity
3-bit — Balanced
~10×
98.3% cosine fidelity
2-bit — Maximum
~16×
94.0% cosine fidelity
"Google's TurboQuant proves compression is a foundational requirement. MemTurbo makes it a production capability."
MemTurbo Whitepaper — PrecogLabs, April 2026
Key Features

Built for Production AI.

Every system that depends on memory, retrieval, or real-time context benefits from MemTurbo's architecture.

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Sovereign Intelligence.

Deploying advanced AI systems with persistent memory across sessions. Multi-agent orchestration with user-scoped and agent-scoped isolation at the architecture level.

Enterprise AI
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Uncompromised Security.

Complete data isolation and control. Multi-tenant architecture with cryptographic separation between environments.

Security
// vector_acceleration

Vector Acceleration

471 QPS at 99% recall. 11.4× faster than leading alternatives.

Performance
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Cross-Platform API

Integrate across network APIs and short-token commitments to enterprise-ready endpoints.

Integration
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∞

Institutional Precision.

Scalable global deployment. From 50K to 500K users on the same infrastructure.

Scale
Engineering the Future
of AI Memory.
Hallowed be the engineers. Est. 2024.
How It Works

From Ingest to Retrieval.

STEP 01

Agent Ingest

Data enters the memory layer with intelligent chunking and structuring.

STEP 02

Vector Encoding

TurboQuant compresses embeddings at configurable fidelity levels.

STEP 03

Neural Search

Semantic retrieval at 471 QPS with 99% recall across 50M vectors.

STEP 04

Retrieval

Sub-millisecond cache delivery to the application layer.

Market Validation

The Industry Converges
on Memory.

The world's most sophisticated technology organisations are moving toward the same thesis MemTurbo was built to address. Memory is no longer a convenience — it is critical infrastructure.

Amazon Web Services
Bedrock AgentCore Memory
Selected Mem0 as exclusive memory provider for Strands Agent SDK — validating agent memory as first-class infrastructure.
Microsoft
Foundry Agent Service Memory
Entered availability early 2026. Both major cloud platforms now treat memory as critical AI infrastructure.
Google Research
TurboQuant — ICLR 2026
Vector compression achieves near-optimal distortion rates. Mathematical validation that compression-native architectures are the future.
Industry Consensus
49% CAGR Through 2030
Global RAG market projected to $28.5B by 2030. Memory-intensive systems are the fastest-growing category of enterprise AI.
Early Access Pricing

Scale Without Limits.

Production-grade memory infrastructure priced for growth. No feature gating. No hidden taxes.

Starter
Free
Up to 10K memories / month
  • Full three-layer architecture
  • TurboQuant 4-bit compression
  • Single agent / project
  • Community support
  • pgvector backend included
Start Free
Pro
$79/mo
Up to 1M memories / month
  • Everything in Starter
  • All compression modes (2/3/4-bit)
  • Multi-agent, multi-tenant
  • In-memory cache layer
  • Priority support + SLA
  • API access + webhooks
Get Early Access
Enterprise
Custom
Unlimited memories
  • Everything in Pro
  • Dedicated infrastructure
  • Self-hosted deployment option
  • Custom compression configs
  • 24/7 engineering support
  • SOC 2 compliance
Contact Sales
Join the Waitlist

Memory Architecture is the
Next Infrastructure Layer.

The window for establishing a position in this layer is measured in quarters, not years. Be among the first to deploy production-grade AI memory.

Early access opens Q3 2026. No credit card required.