Reliability Protocol v2.6 // Operational focus

WE BUILD RELIABLE AI SYSTEMS
FOR REAL BUSINESS
WORKFLOWS.

We transform stochastic LLM outputs into hardened operational systems that perform consistently in production.

01 // Problem Resolution

Operational Clarity

The Friction (Cost)

  • Manual data checking across dashboards leading to high labor overhead.
  • Documentation search silos causing delays in decision making.
  • Inconsistent LLM answers creating operational risk and liability.

The Engineering

  • Retrieval pipelines (RAG) with precise source citation.
  • Custom decision automation agents with tool-integration.
  • Monitoring, schema validation, and fallback systems.

The Outcome

  • Reduced operational costs via automated document parsing.
  • Faster internal ops through instant knowledge access.
  • Predictable outputs verified against business logic.
System Design

Technical Workflow

User API
Embedding Service
Retriever & Re-ranker
LLM Engine
Schema Validator
Observability

A realistic look at our production retrieval and validation stacks.

Prototype System Simulation

Internal Operations Knowledge Agent

Context: A logistics framework prototype designed to parse 400+ compliance documents manually searched by employees.

The Build: Retrieval + Citation + Fallback validation. If a document cannot be verified with 100% citation grounding, the system triggers a manual human-in-the-loop fallback.

Verified Output Consistency Test Metric
Zero-Hallucination Guardrail System Priority
# PROTOTYPE LOG VIEW
[0.12s] Embedding generated for query
[0.34s] Top 5 document chunks retrieved
[0.45s] Re-ranking complete (Score: 0.94)
[1.20s] LLM Synthesis complete
[1.25s] SCHEMA VALIDATION: SUCCESS
[1.30s] CITATION VERIFIED: (doc_id: 421)
The Tech

Engineering Stack

Inference
Open-weight LLMs (Local)
API-hosted LLMs (High-Scale)
Quantized local inference
Data
Postgres + pgvector
Hybrid Vector Search
Embedding pipelines
Orchestration
Python Services
Async Job Workers
Retry Logic / Exponential Backoff
Safety
Structured Output Parser
Deterministic Fallback Rules
Observability (Logging)
How We Work

Engagement Model

Identity

Applied AI Group

Singularity Union is a small engineering team focused exclusively on applied LLM systems. We prioritize verifiable system performance over marketing hype.

HQ LocationIndia // Remote-Global
Response SLA24 Hours
Operational Focus

"We avoid the 'marketing chatbot' trap. We build systems that perform business logic with verifiable precision."

Initialize Connection

Start Review

Submit your technical friction points for an architecture review. Response time: Within 24 hours.