QueryForge – Enterprise Private Knowledge Agent
Secure, Multi-Agent RAG Ecosystem for Sovereign Knowledge Management

QueryForge is an open-source, local-first optimization engine for enterprise Retrieval-Augmented Generation (RAG) systems. It automatically generates multiple query variants—utilizing decomposition, HyDE, step-back prompting, and rewriting—to run parallel retrieval across vector, BM25, and metadata strategies. By fusing and reranking results, it evaluates end-to-end performance to recommend the optimal configuration for any specific dataset. Designed to solve the primary production failure mode of poor retrieval quality, QueryForge transforms manual tuning into an automated, reproducible science while running entirely offline to ensure absolute data privacy.

Open-Source Integration Highlights

QueryForge is an open-source, local-first optimization engine for enterprise Retrieval-Augmented Generation (RAG) systems. It automatically generates multiple query variants—utilizing decomposition, HyDE, step-back prompting, and rewriting—to run parallel retrieval across vector, BM25, and metadata strategies. By fusing and reranking results, it evaluates end-to-end performance to recommend the optimal configuration for any specific dataset. Designed to solve the primary production failure mode of poor retrieval quality, QueryForge transforms manual tuning into an automated, reproducible science while running entirely offline to ensure absolute data privacy.

Open-Source Integration Highlights

Executive Summary

Executive Summary: QueryForge Optimization Engine

Vision: Transforming RAG from a "black box" of inconsistent retrieval into a deterministic, high-performance science by automating multi-strategy discovery on local, sovereign infrastructure.

1. The Strategic Imperative

Production RAG failure is often rooted in single-query ambiguity. Manual tuning of chunking and retrieval parameters is time-consuming and rarely yields the global optimum. QueryForge bridges this gap by automating the discovery of the best multi-query and retrieval mix.

2. The Solution: Systematic Optimization

An automated MLOps framework that delivers measurable gains in recall and answer accuracy. By leveraging an entirely local, open-source stack, enterprises can optimize performance on proprietary data without compromising data sovereignty or cloud dependency.

Quantifiable RAG Impact

  • 🎯 Recall Optimization: Automated discovery of top-k and hybrid weighting.
  • 🛡️ Sovereign Security: 100% offline execution for sensitive datasets.
  • ⚡ Operational Efficiency: Eliminates weeks of manual hyperparameter tuning.
  • 📊 Validated Accuracy: Benchmarked via Ragas faithfulness and relevancy.

Strategic Imperative: Mastering Retrieval Science

QueryForge exists to eliminate Retrieval Fragility in enterprise RAG systems. While many GenAI initiatives stall due to hallucinations, root cause analysis reveals that the dominant failure mode is actually poor or incomplete retrieval. QueryForge reframes retrieval optimization as a first-class, measurable capability.

1. Multi-Dimensional Strategic Intent

Architecture Lens Strategic Value
Enterprise Architecture (TOGAF) A horizontal intelligence capability reusable across Legal, Finance, and Engineering domains.
Agile Delivery (SAFe) Acts as a shared platform ART (Agile Release Train) enabling multiple downstream value streams.
ML Engineering (MLE) Operationalizes retrieval experimentation with reproducibility, metrics, and governance guardrails.

Business Capability Map: Knowledge Optimization

[Diagram: Knowledge Access → Retrieval Optimization → Answer QA]

Highlighting QueryForge as the Shared Platform Capability

2. Strategic Differentiators

Retrieval as Science

Treats query formulation and retrieval configuration as an experiment space with objective, reproducible metrics.

Local-First by Design

Enables full optimization on proprietary datasets without cloud exposure, ensuring absolute data sovereignty.

Model-Agnostic

Works with any embedding model or LLM (Ollama, local PyTorch, etc.), avoiding restrictive vendor lock-in.

Enterprise-Ready

Built with auditability and transparency baked into the core, ready for highly regulated corporate environments.

Target User Personas: Optimizing Knowledge Retrieval

These personas represent the primary stakeholders in enterprises deploying Sovereign RAG systems, focusing on those who require high-precision retrieval without manual tuning or data leakage.

EA

Enterprise Architect

CTO Office

Goals: Standardize RAG quality across the enterprise; minimize technical debt from siloed AI "black boxes".

Pain Points: Inconsistent RAG performance across different departments (Legal vs. Engineering).

How QueryForge Helps: Provides a horizontal intelligence capability for cross-domain knowledge access.

ME

ML Engineer

Applied AI / MLOps

Goals: Improve recall and accuracy (Recall@k, MRR) without manual hyperparameter tuning.

Pain Points: Endless trial-and-error cycles adjusting chunk sizes and top-k parameters.

How QueryForge Helps: Automates multi-strategy discovery and provides local Ragas evaluation.

PE

Platform Engineer

Internal AI Platforms

Goals: Provide reusable, scalable RAG primitives to internal product teams.

Pain Points: Managing dozens of custom retrieval implementations with zero standardization.

How QueryForge Helps: Offers a shared platform capability that integrates into existing SAFe value streams.

GR

Compliance Officer

GRC / Security

Goals: Ensure 100% data sovereignty; zero exfiltration of proprietary knowledge.

Pain Points: Cloud AI providers failing strict "no-cloud" data residency requirements.

How QueryForge Helps: Executes entirely offline via local Ollama and FAISS.

01b. Lightweight Requirements & User Stories (MoSCoW Prioritization) Click to Expand

These user stories align SAFe Lean Portfolio Management with RAG Engineering Excellence, focusing on the Epic: Optimize Retrieval Quality for Enterprise RAG.

ID User Story Priority Linked Component Acceptance Criteria
US-01 As an ML Engineer, I want to generate query variants so that ambiguous questions retrieve complete context. Must Multi-Query Strategist Automated decomposition & HyDE generation.
US-02 As a Platform Owner, I want retrieval metrics (recall@k) so that improvements are measurable. Must Eval Engine (Ragas) Report generation with grounded metrics.
US-03 As a Security Officer, I want local evaluation so that proprietary data never leaves the environment. Must Ollama / Local Stack Zero cloud API calls during optimization.
US-04 As an MLE, I want versioned configs to ensure deterministic optimization runs. Should Registry / Config Reproducible results across different runs.
01c. User Journey Map: The Optimization Lifecycle Click to Expand

This journey tracks a ML Engineer from dataset ingestion to exporting a production-ready RAG configuration.

Stage Actions / Touchpoints Legacy Pains Autonomous Resolution Metrics Impact
1. Setup Upload documents + ground truth gold questions. Manual data prep; no clear baseline. Unified ingestion with Local Embeddings. Ready in <5m
2. Generation Auto-generate Decomposition/HyDE variants. Poor recall from ambiguous phrasing. Multi-Query Engine covers broader intent. +40% Intent Recall
3. Retrieval Parallel search across Vector/BM25/Metadata. Keyword misses vs. Vector halluncinations. Reciprocal Rank Fusion finds common relevance. High Precision
4. Recommendation Select optimal config based on Ragas evaluation. Manual "vibe-check" based tuning. Automated leaderboard of RAG strategies. Optimized in Hours

01d. Technical Rollout Roadmap (SAFe + EA)

This implementation roadmap sequences prioritized user stories into SAFe Program Increments (PIs), prioritizing retrieval stability and offline evaluation in the Foundation phase. The strategy establishes a "Retrieval-as-Science" baseline before scaling into agentic orchestration and automated CI/CD quality gates.

Implementation Phases & PI Mapping Expanded
Phase Focus Deliverables Strategic Value Dependencies
1: Foundation MVP Retrieval Query variant generators (HyDE, Decomp); Vector + BM25 retrieval; Offline eval harness. Baseline recall established. Local Ollama & FAISS setup.
2: Optimization Governance Strategy fusion & reranking; Metrics dashboards; Configuration versioning. Repeatable, auditable tuning. Phase 1 Evaluation stability.
3: Platform Scale & CI/CD Agent-based orchestration; Plug-in retriever arch; CI RAG regression testing. Enterprise-wide RAG primitives. Docker/Kubernetes local cluster.

Program Increment Planning Board

Visualizing features mapped to Program Increments and architectural enablers.

Multi-Agent Reasoning Chain: The Retrieval "Logic Swarm"

The QueryForge engine orchestrates a swarm of specialized agents that function as a high-performance RAG optimization department. This architecture bridges the gap between raw data and high-precision answers through sequential, auditable reasoning stages.

1. The Autonomous Workforce (Agent Personas)

Agent Role Reasoning Engine / Responsibility Strategic View (EA/MLE/SAFe)
Query Generator Produces diverse query variants (HyDE, Decomposition). Logical Application Component.
Retrieval Agent Executes retrieval strategies in parallel (Vector, BM25). Decoupled Evaluation Stage.
Fusion & Reranker Merges results and scores relevance via Cross-Encoders. Platform ART Backlog Item.
Evaluation & Recommender Computes metrics (Recall, Precision) and selects optimal config. Quality Governance Guardrail.

Agent Interaction & Artifact Flow

[Diagram: Inputs → Query Variants → Document IDs → Recommender]

2. The "Reasoning Trace" (Transparent Auditing)

To satisfy enterprise GRC requirements, QueryForge generates a White-Box Audit Trail for every optimization run:

[Query_Gen]: Generated 3 variants for 'Q: Q4 revenue trends'. Strategies: [Decomposition, HyDE].

[Retrieval_Agent]: Executed parallel search. Vector Recall: 0.72 | BM25 Recall: 0.65.

[Reranker_Agent]: Applied Cross-Encoder. Final Relevancy Score: 0.89. Top-K recommended: 5.

View Decision Matrix & Conflict Resolution Strategy

QueryForge implements enterprise-grade decision governance when strategies diverge:

Scenario Resolution Logic
Conflicting Strategy Scores Prioritize configurations maximizing Recall under strict latency constraints.
Metric Ambiguity Apply weighted scoring based on business priority (e.g., Faithfulness over Latency).

This mirrors standard enterprise governance, replacing opaque AI behavior with deterministic logic and optional Human-in-the-Loop escalation.

The QueryForge Intelligence Platform: RAG Optimization Fabric

QueryForge is architected not as a standalone application, but as a Sovereign Intelligence Platform component. It is designed to plug directly into existing RAG stacks, unifying fragmented retrieval experiments into a single, queryable "Retrieval Truth" layer.

1. Unified Intelligence Stack Architecture

Platform Characteristic Technical Implementation Strategic Function
API-Driven & Modular RESTful service hooks Enables seamless integration into existing application layers and retrieval infra.
Config-First Design YAML-based experiment definitions Ensures reproducible experiments across different datasets and environments.
Agnostic Interoperability Model-agnostic middleware Operates across any LLM or retriever (FAISS, Chroma, BM25) without vendor lock-in.

Platform Positioning: Application vs. Infrastructure

[Diagram: Application Layer ← QueryForge Intelligence ← Retrieval Infrastructure]

Visualizing QueryForge as the connective tissue for RAG performance

Intelligence Dividend

By centralizing the optimization fabric, enterprises achieve Total Population Certainty in their knowledge retrieval, reducing "hallucination-by-omission" and ensuring near-zero latency for validated query paths.

Model Lifecycle (MLE): The Sovereign Retrieval Predictor

From an ML Engineering perspective, QueryForge operationalizes a Retrieval Lifecycle analogous to traditional model management. We apply a tiered experimental strategy to ensure that RAG configurations are not just "tuned" but scientifically validated.

1. The Systematic Lifecycle Stages

Lifecycle Stage Engineering Action Strategic Use Case
Design & Experiment Define query strategies and parallel retrieval options. Establishing the hypothesis space for multi-query variants.
Evaluate & Select Measure recall, accuracy, and latency via Ragas. Choosing the optimal configuration based on objective metrics.
Deploy & Monitor Export config and re-evaluate as data shifts. Ensuring production RAG quality survives data drift.

The Continuous Retrieval Improvement Loop

[Diagram: Design → Experiment → Evaluate → Select → Deploy → Monitor]

Operationalizing retrieval optimization as a first-class MLOps pipeline

Business Impact: Champion/Challenger Retrieval

This framework complements traditional MLOps by treating the retrieval stage as a dynamic asset. Our Champion/Challenger logic ensures that only the most precise retrieval paths are promoted to the enterprise General Ledger of knowledge.

Cloud Infrastructure & Local-First SRE

The infrastructure is architected as a Sovereign Knowledge Landing Zone. Unlike cloud-dependent RAG stacks, QueryForge utilizes a local-first reference architecture to isolate sensitive intellectual property while providing enterprise-grade scalability.

1. Local-First Reference Architecture

To ensure absolute data privacy and zero external API dependency, we deploy an immutable, offline-capable stack:

Layer Component Infrastructure Strategy
Compute Ollama / Local LLM Runs on developer laptops or on-prem servers; no external cloud calls.
Vector Store FAISS / Open-source Persistent local vector indexing with support for deterministic replays.
Parallelization Multiprocessing Parallel execution of query variants to maximize local CPU/GPU utilization.

Scalability Path: From Laptop to Cluster

[Image of a multi-stage deployment architecture showing local developer environment, CI pipeline containerization, and on-premises cluster deployment]

Visualizing the progression from Local → CI Pipelines → On-prem Cluster topologies.

2. SRE & Reliability for RAG Close

Applying the No-Cloud model to ensure knowledge availability during critical optimization windows:

  • 🛡️ Zero-Trust: Authentication via local identity providers for on-prem dashboards.
  • 🚧 Containerization: Dockerized environments to ensure identical behavior in local vs. CI pipelines.
  • 🔑 Deterministic Replays: Benchmarking against versioned datasets to guarantee 99.99% reproducibility.

Why This Infrastructure Works

This stack is CTO Ready (guarantees sovereign control), CISO Ready (zero external API leakage), and Platform Ready (scales via standard containerization). It transforms the SRE function into a Digital Controller for RAG performance.

AI Governance & Regulatory Compliance

To satisfy enterprise audit standards, QueryForge implements a "White-Box" Governance Framework. This removes the "Black Box" risk of opaque retrieval by ensuring every optimization is backed by Traceability of Truth and strict local controls.

1. The "Traceability of Truth" Framework

Governance Pillar Implementation Regulatory Outcome
Data Sovereignty Air-gapped local execution Zero data egress; full alignment with GDPR and data residency requirements.
Auditability Full JSON Audit Logs Captures every query variant and document ID for SOC2/ISO-aligned reporting.
Reproducibility Versioned Configs & Datasets Ensures deterministic runs suitable for Internal Model Risk Management (MRM).

Governance Overlay: Pipeline-Wide Controls

[Diagram: Security & Compliance Controls Layered Across the RAG Pipeline]

Visualizing integrated guardrails for regulated environments

The Compliance Dividend

QueryForge isn't just a performance tool—it's a Regulatory Powerhouse. By providing a pre-validated, "link-to-source" audit trail, it allows enterprises to scale RAG initiatives while maintaining Total Population Certainty and absolute data privacy.

Impact & Outcomes: The Knowledge Transformation

QueryForge moves the enterprise from "Vibe-Based" RAG development to Total Population Certainty. By treating retrieval as a measurable science, the platform delivers impact across three core areas: Operational Efficiency, Data Sovereignty, and Financial Optimization.

1. Hard-Dollar Impact: The Sovereign RAG Advantage

Value Driver Legacy Manual Baseline QueryForge Outcome Business Impact
Information Discovery Hours spent per query 70–90% Time Reduction Immediate labor cost mitigation.
Retrieval Quality Inconsistent/Hallucinatory Measurable Recall Gains High-fidelity decision support.
Data Security Cloud Exfiltration Risk Zero Leakage (Local-First) Absolute IP Protection.

2. Strategic Insight: Scalable RAG Excellence

Center of Excellence

Establishes a centralized retrieval capability that enables repeatable RAG quality improvements across all corporate departments.

Efficiency Dividend

Significantly reduces long-term LLM tokens costs by providing higher-relevance context, minimizing the "noise" sent to the model.

Value Realization Map: Technical to Business ROI

Strategic View: Linking Retrieval Science to Profitability

[Diagram: Technical Improvements → Performance Gains → Business Outcomes]

Mapping how automated retrieval optimization translates directly into enterprise-wide operational throughput.

The Privacy-First Standard

QueryForge demonstrates that Enterprise-Grade AI Engineering does not require cloud dependency. By achieving a center of excellence in retrieval, QueryForge enables a future where knowledge access is both instantaneous and entirely sovereign.