Blinkt AI®

One connection. Every NLP operation.

One connection. Every NLP operation.

Full context across every call.

The infrastructure agents need doesn't exist yet.

LLM-based agents run in loops — reason, call a tool, observe, repeat — chaining 7–12 NLP operations per reasoning cycle. But every commercial NLP API forces each call through a separate REST connection: new handshake, no memory of the last call, no streaming, no flow control. The result is thousands of milliseconds of dead time, lost context, and brittle polling — the three taxes that kill agent pilots in production.

We commissioned a research paper on why this mismatch exists and what the optimal architecture looks like. Read the paper →

Introducing Blinkt AI

The real-time cognitive pipe for AI agents. Stop stitching together stateless REST APIs. Blinkt is a unified, WebSocket-native NLP engine that streams twelve cognitive operations—from entity extraction to causal reasoning—over a single, persistent connection. State is preserved. Context accumulates. Retrieval learns from your data. It’s the missing infrastructure layer for high-frequency intelligence.

Introducing Blinkt AI

The real-time cognitive pipe for AI agents. Stop stitching together stateless REST APIs. Blinkt is a unified, WebSocket-native NLP engine that streams twelve cognitive operations—from entity extraction to causal reasoning—over a single, persistent connection. State is preserved. Context accumulates. Retrieval learns from your data. It’s the missing infrastructure layer for high-frequency intelligence.

The Problem: The "Three Taxes"

One connection. The complete cognitive stack. Zero repeated context. Today, your agent pays three hidden taxes on every NLP pipeline.

The Latency Tax

REST was designed for websites, not agents. Every operation opens a new TCP+TLS handshake. Ten NLP operations across five reasoning cycles means 50 separate handshakes—5 to 15 seconds of pure overhead before any intelligence begins. With Blinkt AI, it's one handshake. The connection stays hot. You pay the setup cost once, then stream data at wire speed.

The Context Tax

REST is stateless. The entity graph from step 1 gets re-sent in full for step 3—or more likely, dropped to save tokens. Coreference clusters never carry forward. Causal analysis runs without the context that makes it accurate. Stateless APIs force your agent to think with amnesia. Blinkt AI preserves context in memory, so your agent gets smarter the longer the session runs.

The Polling Tax

Your agent triggers a long-running analysis, then burns reasoning cycles polling for completion. 95% of those API calls return "still processing." It is inefficient and brittle. With Blinkt AI, there’s zero polling. It pushes results the microsecond they are ready.

Blinkt eliminates all three. One WebSocket connection. Persistent state. Server-push for everything async.

How agents use Blinkt AI

How agents use Blinkt AI

Your agent connects once
    ↓
wss://api.blinkt.ai/ws/nlp
    ↓
Chain any operation on the open connection:
  process_text          → sentence segmentation
  extract_entities      → named entity recognition
  resolve_coreferences  → cross-document pronoun resolution
  extract_keywords      → statistical keyword extraction
  extract_topics        → neural topic modeling via BERTopic
  vectorize_text        → vector embeddings
  rerank_documents      → cross-encoder scoring (self-improving on your domain)
  chunk_and_enrich      → semantic chunking + metadata
  determine_experts     → persona generation
  analyze_for_insights  → multi-agent analysis
  extract_all_knowledge → causal + temporal + entity graph (Pearl's Ladder)
    ↓
Context accumulates across every call
    ↓
Results stream back as each stage completes
    ↓
Your agent reasons on partial results immediately

Why one WebSocket changes everything.

Engineered for the "Real-Time” Frontier. We didn't just wrap a REST API in a WebSocket. We rebuilt the transport layer for high-frequency intelligence.

50–80% smaller payloads

Blinkt speaks MessagePack natively. While we support JSON for easy debugging, our binary mode slashes payload sizes by 50-80% for vector embeddings and knowledge graphs. This means faster parsing, lower bandwidth costs, and zero CPU burn on massive data transfers.

Zero memory bloat

Our architecture respects TCP backpressure. If your agent is processing a 1GB document on a slow mobile connection, Blinkt automatically throttles the stream. Eliminate Out-of-Memory (OOM) crashes and dropped frames—just smooth, reliable delivery.

Non-blocking parallelism

Don't block. Request entity extraction, sentiment analysis, and topic modeling simultaneously over a single wire. Results interleave as they complete, maximizing throughput without managing multiple connections.

Self-improving retrieval

Stop renting generic models. Blinkt uses your usage patterns (clicks, dwells, queries) to fine-tune a custom cross-encoder for your domain automatically. We push versioned models to your Hugging Face repo. Your retrieval gets sharper with every API call.

100% context retention

The server maintains entity maps, coreference clusters, and expert personas in active connection memory. When your agent calls causal extraction in step 7, it implicitly accesses the resolved coreference chains from step 4. The analysis is more accurate because it sees the whole picture. No re-processing. No context loss.

Usage-based. No seat licenses. No minimums.

Foundational

Sentence segmentation, entity extraction, keyword extraction, topic extraction, vectorization.

Advanced

All Foundational features + coreference resolution, semantic chunking, document reranking, expert persona generation, causal relationship extraction, temporal relationship extraction.

Orchestration

All Advanced features + full pipeline intelligence: causal reasoning, temporal analysis, multi-agent evaluation in a single call + model fine-tuning add-on.

Advanced

All Foundational features + coreference resolution, semantic chunking, document reranking, expert persona generation, causal relationship extraction, temporal relationship extraction.

Foundational

Sentence segmentation, entity extraction, keyword extraction, topic extraction, vectorization.

Orchestration

All Advanced features + full pipeline intelligence: causal reasoning, temporal analysis, multi-agent evaluation in a single call + model fine-tuning add-on.

Give your agents real NLP infrastructure.

Give your agents real NLP infrastructure.

Give your agents real NLP infrastructure.

One connection. Full context. Every operation.

One connection. Full context. Every operation.

Join the waitlist.

Join the waitlist.