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The High-Performance Data Layer That Powers AI.

Vector search indexing, real-time embedding pipelines, semantic caching, and hybrid knowledge retrieval engineered for sub-second retrieval at enterprise scale.

Engineering Philosophy

AI Data Layer & Infrastructure

AI models are only as good as the context fed into them. Generic RAG implementations suffer from hallucination, slow retrieval latency, and outdated chunking that dilutes relevant facts.

We engineer production-grade AI data infrastructure: high-throughput vector pipelines, intelligent hybrid search algorithms, and semantic caching layers that deliver millisecond-level precision.

Capabilities & Deliverables

Data Layer Capabilities

01.

Hybrid Vector & Keyword Indexing

Combining dense vector embeddings with BM25 sparse keyword search for pinpoint accuracy across technical terms, IDs, and conceptual queries.

TERNIQS SPEC✓ DELIVERABLE
02.

Semantic Chunking & Parsing

Document hierarchy-aware chunking that preserves tables, code snippets, and section metadata rather than arbitrary token character splits.

TERNIQS SPEC✓ DELIVERABLE
03.

Semantic Response Caching

In-memory vector caching that serves repeated semantic queries in < 10ms while reducing LLM API token costs by up to 60%.

TERNIQS SPEC✓ DELIVERABLE
04.

Real-Time CDC & Vector Sync

Change Data Capture (CDC) pipelines that update vector indexes the millisecond records change in your primary PostgreSQL or MongoDB database.

TERNIQS SPEC✓ DELIVERABLE
05.

Cross-Encoder Re-Ranking

Two-stage retrieval pipelines utilizing lightweight re-ranking models to filter top-k results for maximum context density and zero token bloat.

TERNIQS SPEC✓ DELIVERABLE
06.

Row-Level Multi-Tenant Security

Strict metadata-based tenant isolation ensuring users can only retrieve and search documents they are explicitly authorized to view.

TERNIQS SPEC✓ DELIVERABLE
Execution Framework

Infrastructure Blueprint

STEP 01

Schema

Audit & Modeling

We inspect your knowledge corpus and model custom metadata indexing schemas.

PHASE_01PROD_READY
STEP 02

Embed

Pipeline Engineering

We configure chunking strategies, embedding models, and vector database clusters.

PHASE_02PROD_READY
STEP 03

Benchmark

Search Precision

We benchmark recall and MRR scores using automated synthetic question testing.

PHASE_03PROD_READY
STEP 04

Scale

High-Load Deployment

We deploy with semantic caching, query rate limits, and latency telemetry.

PHASE_04PROD_READY

Build a reliable, high-speed data foundation for your AI systems.

Talk directly with our lead architects and engineers. No sales reps, no fluff — just technical scope and clear milestones.

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