TruthLayer

Deliverables / RoleFull-Stack AI Engineer

Platforms / CategoryAI Product

CAPABILITIES

TruthLayer is a specialized Enterprise RAG platform built to navigate conflicting documentation via an automated trust hierarchy.

8 StagesGraph Pipeline

HybridSearch Model

Timeline

2024 (3 Months)

Code

TruthLayer is an enterprise knowledge assistant designed to solve a specific problem in large organizations: conflicting documentation. Using a multi-stage LangGraph architecture, it retrieves context via a hybrid semantic/BM25 pipeline, reranks it, and evaluates conflicting information against a predefined trust hierarchy before synthesizing an answer.

DECISION // PHILOSOPHY

“Enterprise AI is not just about retrieving data—it is about verifying truth when internal sources disagree.”

Key Details

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Hybrid Retrieval Strategy

Combines dense vector search (Semantic) and sparse keyword search (BM25) to capture both contextual meaning and exact terminology, ensuring high recall.

Cross-Encoder Reranking

Applies a secondary cross-encoder model to meticulously score and rerank the retrieved chunks against the original query for maximum relevance.

Trust Hierarchy Resolution

When documents conflict, the agent uses metadata to weigh sources (e.g., official docs > slack threads) to synthesize the most reliable truth.

Confidence Guardrails

Generates confidence scores and employs an automated refusal mechanism if the retrieved context does not meet the minimum threshold for a definitive answer.

SYSTEM
ARCHITECTURE

Engineered for production durability, type safety, and scalable domain isolation. Every module operates with strict boundaries and predictable failure handling.

MODULAR DOMAIN ISOLATION — BUILT FOR ZERO-DOWNTIME DEPLOYMENTS, TYPE-SAFE CONTRACTS, AND FAIL-SAFE EXCEPTION BOUNDARIES.

Architectural Modules

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LangGraph State Machine

Predictable, deterministic graph execution tracking the state of retrieval, routing, and generation.

Hybrid Search Engine

ChromaDB vector search coupled with BM25 indexing for unparalleled retrieval accuracy.

Trust Evaluation Node

A specialized LangGraph node dedicated exclusively to metadata analysis and conflict resolution.

PROJECT
IMPACT

Production performance measured under live traffic load, latency stress profiles, and automated system profiling.

ALL OUTCOMES ARE EMPIRICALLY VERIFIED IN PRODUCTION ENVIRONMENTS — MEASURED UNDER HIGH CONCURRENCY, PEAK LATENCY PROFILES, AND AUTOMATED SYSTEM HEALTH AUDITS.

8 Stages

Graph Pipeline

Hybrid

Search Model

KEY
CAPABILITIES

Core capabilities engineered for high-scale reliability, intuitive operator workflows, and real-time production execution.

PRODUCTION-TESTED SYSTEM FEATURES — ARCHITECTED FOR LOW-LATENCY INTERACTION, DETERMINISTIC STATE HANDLING, AND COMPREHENSIVE TELEMETRY.

MODULE / 01

LangGraphRAGChromaDB

Advanced Hybrid RAG

An 8-stage pipeline leveraging both Semantic and BM25 search mechanisms for broad and precise context retrieval.

8Pipeline Stages

MODULE / 02

HeuristicsMetadata

Conflict Resolution

Automatically detects contradicting statements across documents and resolves them using a predefined A/B/C trust tier system.

3Trust Tiers

SYSTEM
MODULES

Logical grouping of platform capabilities and execution primitives by architectural domain.

ISOLATED DOMAIN SPECIFICATIONS — MODULAR ARCHITECTURAL LAYERS DELIVERING HIGH THROUGHPUT AND RESILIENT FAULT TOLERANCE.

Domain Capabilities

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AI Orchestration

01LangGraph State Machine
02Multi-stage verification pipeline
03Dynamic routing based on query complexity
04Streaming chunk iteration

Retrieval Engine

01Hybrid Retrieval (Semantic + BM25)
02Cross-Encoder Reranking
03Metadata filtering (source, date, status)
04ChromaDB vector persistence

Trust & Safety

01Source trust hierarchy (A > B > C)
02Automated conflict detection
03Confidence scoring thresholds
04Low-confidence refusal generation

DEVELOPMENT
PROCESS

A rigorous, phased engineering lifecycle designed to transform architectural requirements into scalable, production-ready systems.

DISCIPLINED SYSTEM METHODOLOGY — ARCHITECTED FOR END-TO-END OBSERVABILITY, DETERMINISTIC REVIEWS, AND ZERO-REGRESSION RELEASE CYCLES.

Connected Lifecycle Grid

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PHASE / 01Graph Design

Pipeline Architecture

Designed the LangGraph nodes to cleanly separate retrieval, reranking, trust evaluation, and generation.

LangGraphArchitecture

PHASE / 02Hybrid Engine

Retrieval Tuning

Tuned the weights between sparse and dense vectors and integrated the cross-encoder for precision reranking.

BM25Cross-Encoder

ARCHITECTURE STACK // COMPONENT MATRIX

TECHNOLOGY
STACK

An engineered architectural map outlining foundational nodes, runtime environments, and type-safe deployment frameworks.

DETERMINISTIC ARCHITECTURE STACK — PROFILED FOR PRODUCTION LATENCY, STRICT TYPE SAFETY, AND HIGH-CONCURRENCY SCALABILITY.

Total Nodes

15+

Architectural Components

Type Safety

N/A (Python)%

Neural Nets

3 Models (Embeddings, Reranker, LLM)

Layer 1

frontend

Streamlit

Layer 2

backend

FastAPIPython 3.11Pydantic

Layer 3

ai

LangGraphLangChainGroqSentenceTransformersCrossEncoder

Layer 4

infrastructure

ChromaDBSQLite