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Enterprise AI - Retrieval-Augmented Generation

RAG Development for Enterprise Knowledge Bases

We engineer production-grade retrieval-augmented generation pipelines that connect your LLMs to SharePoint, Confluence, SAP, and proprietary repositories. Our systems combine vector search, hybrid retrieval, cross-encoder re-ranking, and citation tracking to deliver accurate, auditable answers from your organization's actual knowledge.

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500+
Enterprise Clients
10,000+
Engineers Deployed
50+
Countries Served
99.4%
CSAT Score
48h
Team Assembly

The Challenge

Your enterprise knowledge is locked in repositories that AI cannot reliably access

Most organizations have accumulated decades of institutional knowledge across SharePoint libraries, Confluence wikis, SAP document stores, and legacy file systems. General-purpose LLMs trained on public data cannot access this content, and those that attempt to rely on stale training snapshots produce hallucinated answers that erode employee trust. Without a structured retrieval layer, AI assistants become liabilities rather than assets in regulated or high-stakes environments.

73%
of enterprise knowledge workers spend 2+ hours daily searching for information
40%
of AI-generated answers contain hallucinations when no retrieval layer is present
$6.5M
average annual cost of poor knowledge management for mid-sized enterprises
4x
faster decision-making with accurate AI-assisted document retrieval

Why QuickHire

Why Enterprises Choose QuickHire

01

Hybrid Search Architecture

We combine dense vector similarity with sparse BM25 keyword search and merge results via reciprocal rank fusion. This ensures both semantic intent and exact term matching are handled, which is critical when employees search for regulatory codes, product identifiers, or contract clause numbers.

02

Source-Faithful Citation Tracking

Every answer is anchored to specific retrieved passages with full provenance metadata including document title, section, version date, and URL. Users can click through to the exact source, satisfying compliance and audit requirements without manual cross-referencing.

03

Domain-Optimized Chunking

We design chunking strategies specifically for your document types - semantic chunking for narrative policies, structure-aware chunking for PDFs, and entity-centric chunking for SAP or CRM exports. Chunk boundaries preserve context and metadata that generic off-the-shelf pipelines discard.

04

Permission-Aware Retrieval

Our retrieval layer enforces your existing access control policies at query time, filtering vector search results to documents the querying user is authorized to view. Enterprise identity integration with Azure AD or Okta ensures AI answers never expose restricted content.

05

Systematic Quality Evaluation

We build ground-truth evaluation sets from real employee queries and known-answer pairs, then measure context recall, answer faithfulness, and citation accuracy continuously. Evaluation gates in CI/CD prevent quality regressions from reaching production.

06

Incremental Sync Pipelines

Our ingestion architecture detects document changes through webhooks or change data capture and updates the vector index incrementally without downtime. Deleted and superseded documents are removed to prevent stale content from polluting retrieval results.

Challenges

Common Enterprise Pain Points

01

Document Heterogeneity at Scale

Enterprise organizations manage content across dozens of systems in varied formats - Word documents, PDFs, HTML wikis, structured database records, and scanned images. Building a single coherent retrieval layer over this heterogeneous corpus requires format-specific parsers, normalization pipelines, and metadata standardization that cannot be solved with a generic indexing tool. We architect multi-source ingestion frameworks that treat each content type appropriately while presenting a unified retrieval interface to the application layer.

02

Retrieval Precision vs. Recall Trade-offs

Optimizing purely for recall surfaces too many irrelevant passages that confuse the LLM and inflate cost. Optimizing purely for precision misses relevant content that is phrased differently from the query. Enterprise RAG requires careful calibration of chunk size, embedding model, retrieval depth, and re-ranking threshold for each specific knowledge domain. We use systematic offline evaluation against representative query sets to find the configuration that maximizes both precision and recall for your content profile.

03

Latency Requirements for Interactive Use

Employees using an AI knowledge assistant expect sub-3-second end-to-end response times, but retrieval, re-ranking, and generation each add latency that compounds quickly at scale. Achieving low latency without sacrificing answer quality requires careful optimization of vector index configuration, embedding model selection, batch re-ranking, and prompt length management. We conduct latency profiling throughout development and architect caching strategies for high-frequency queries without compromising freshness.

04

Maintaining Quality as Content Evolves

Enterprise content is not static - policies are updated, projects close, products are discontinued. RAG systems that lack robust change detection will surface outdated answers with the same apparent confidence as current ones. This is particularly dangerous in compliance-sensitive domains where employees may act on superseded policy language. Our incremental sync architecture and version-aware metadata management ensure that answer provenance reflects document currency, not just relevance.

05

Governance and Auditability in Regulated Environments

In financial services, healthcare, and legal contexts, it is not sufficient for an AI system to produce correct answers - the organization must be able to demonstrate exactly what information informed each answer, who asked the question, and what version of the policy was retrieved at the time. Our RAG systems produce complete audit logs of every query-retrieval-generation cycle and surface citation metadata that satisfies regulatory examination requirements without custom post-hoc reconstruction.

Our Approach

Production RAG infrastructure engineered for enterprise accuracy, security, and governance

Our RAG development practice delivers end-to-end pipeline architecture that integrates with your existing document infrastructure, enforces your access control policies, and produces citation-grounded answers that employees and compliance teams can trust. We combine proven vector database technology with hybrid retrieval strategies, cross-encoder re-ranking, and continuous evaluation to deliver knowledge assistant systems that improve measurably over time.

01
Enterprise Connector Library
Pre-built, production-tested connectors for SharePoint, Confluence, SAP, ServiceNow, Salesforce, and major SQL/NoSQL databases. Custom connectors for proprietary systems delivered as part of the engagement.
02
Advanced Retrieval Stack
Hybrid dense-sparse search with configurable fusion, cross-encoder re-ranking using Cohere or domain-fine-tuned models, and query rewriting to handle ambiguous or underspecified employee queries.
03
Vector Database Architecture
Purpose-selected vector DB from Pinecone, Weaviate, Qdrant, Milvus, or pgvector based on your scale, infrastructure, and data residency requirements. Index design optimized for your embedding dimensionality and metadata filtering patterns.
04
Governance and Citation Layer
End-to-end provenance tracking, permission-aware filtering at retrieval time, complete audit logging, and front-end citation UI that surfaces exact source passages with clickable links to the originating document.

Delivery Models

How We Deliver

Focused Pilot

Single knowledge source RAG pipeline with evaluation framework, front-end interface, and production deployment. Ideal for proving value on one high-priority use case before broader rollout.

Timeline
8 weeks
Team Size
3-4 engineers
Enterprise Platform Build

Multi-source RAG platform with connector library, permission integration, citation UI, and governance logging. Designed for organization-wide deployment across multiple teams and document repositories.

Timeline
14-20 weeks
Team Size
5-8 engineers
Embedded Team Augmentation

Senior RAG engineers embedded in your AI team to accelerate an in-flight RAG initiative, resolve retrieval quality issues, or architect an evaluation framework. Ongoing engagement with defined milestones.

Timeline
Ongoing
Team Size
2-4 engineers

Capabilities

Technical Capability Matrix

Retrieval Architecture
Hybrid BM25 + Vector SearchReciprocal Rank FusionCross-Encoder Re-rankingQuery RewritingMulti-query Retrieval
Vector Databases
PineconeWeaviateQdrantMilvuspgvector
Ingestion and Chunking
Semantic ChunkingStructure-Aware PDF ParsingTable ExtractionMulti-modal IngestionIncremental Sync Pipelines
Evaluation and Observability
RAGAS EvaluationTruLens IntegrationFaithfulness ScoringRetrieval Recall MetricsProduction Query Monitoring

Engagement Models

How We Engage

Choose the model that fits your programme governance, budget cycle, and team structure.

01

Staff Augmentation

Engineers embed directly under your management.

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02

Dedicated Developers

Full-time team aligned to your product roadmap.

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03

Managed Teams

End-to-end delivery with SLA-backed outcomes.

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04

Engineering Pods

Autonomous cross-functional pods per domain.

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05

Offshore Dev Centre

Permanent engineering base in India. Full IP ownership.

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06

Build-Operate-Transfer

We build and run it. You take ownership on schedule.

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Our Process

From Discovery to Delivery

1

Discovery and Scoping

Days 1-5

We audit your document repositories, access control architecture, query patterns, and existing AI investments to define scope and success criteria.

2

Evaluation Set Creation

Days 6-10

We build a representative ground-truth QA evaluation set from real employee queries and known-answer pairs that will govern all pipeline tuning decisions.

3

Ingestion and Index Build

Weeks 3-5

Source connectors, document parsers, chunking pipelines, and vector DB indexing are built and validated against your evaluation set.

4

Retrieval Tuning and Re-ranking

Weeks 6-8

Hybrid search configuration, re-ranker selection, and retrieval depth are tuned iteratively against evaluation metrics until quality thresholds are met.

5

Production Deployment and Monitoring

Weeks 9-12

CI/CD pipelines, incremental sync, permission integration, citation UI, and production monitoring dashboards are deployed with defined SLAs.

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Security & Compliance

Enterprise-Grade Security by Default

✓ISO 27001 Certified✓SOC 2 Type II Ready✓GDPR Compliant✓DPDP Act Ready✓NDA on Day 1✓MSA Available✓IP Assignment Clauses✓Escrow Options

Governance

Programme Governance

✓

Permission-Enforced Retrieval

Vector search results are filtered at query time by user identity and document ACL metadata, ensuring no answer is ever generated from content the querying user is unauthorized to access.

✓

Full Audit Logging

Every query, retrieved context set, re-ranking decision, and generated answer is logged with user identity, timestamp, and document versions - providing complete traceability for regulatory examination.

✓

Citation Integrity Verification

Automated post-generation checks verify that each factual claim in an answer can be attributed to a cited passage. Citation coverage rates are tracked as a production KPI.

✓

Model Risk Documentation

For regulated clients we produce model risk management documentation covering training data, retrieval architecture, evaluation methodology, known limitations, and monitoring controls.

Team Structure

Your Enterprise Team

Our RAG engineering teams combine deep expertise in information retrieval, NLP, and enterprise systems integration. Each engagement is staffed with engineers who have shipped production RAG systems in regulated industries and understand the governance requirements that distinguish enterprise deployments from prototype experiments.

✓RAG Architect
✓Retrieval Systems Engineer
✓NLP Engineer
✓Vector DB Engineer
✓Data Ingestion Engineer
✓Evaluation Engineer
✓Backend Integration Engineer
✓Enterprise Security Architect

Project Lifecycle

From Kickoff to Production

01
2 weeks

Discovery and Architecture

Source system audit, access control mapping, evaluation set, architecture design document, vendor selection recommendation.

02
3 weeks

Ingestion Pipeline Build

Source connectors, document parsers, chunking pipelines, embedding generation, and populated vector index with provenance metadata.

03
2 weeks

Retrieval and Re-ranking Tuning

Hybrid search configuration, re-ranker deployment, query rewriting module, and evaluation report with recall and faithfulness scores.

04
2 weeks

Application and Governance Layer

Permission filtering integration, citation UI, audit logging, front-end interface, and identity provider integration.

05
Ongoing

Production Operations

Incremental sync monitoring, retrieval quality dashboards, quarterly evaluation reviews, and model and embedding updates.

Case Studies

Enterprise Outcomes

Financial Services

A global bank needed employees to query 14 years of regulatory compliance policies without legal review delays.

We built a permission-aware RAG pipeline over SharePoint with hybrid search, Cohere re-ranking, and version-aware citation tracking. Answer faithfulness scored above 94 percent on the compliance evaluation set.

68%reduction in compliance query resolution time
Healthcare

A hospital network required clinical staff to retrieve protocol documentation accurately without surfacing restricted patient data.

We deployed a HIPAA-compliant RAG system with role-based retrieval filtering, structured citation UI, and audit logging aligned with clinical governance requirements.

$2.1Mannual productivity value from reduced protocol search time
Manufacturing

An engineering firm needed field technicians to query 80,000 pages of SAP equipment manuals from mobile devices.

We built a multi-modal RAG pipeline extracting tables and diagrams from SAP documents, with offline-capable hybrid search returning cited answers in under 2 seconds.

3.4xfaster resolution of field equipment queries

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Hiring Models

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Frequently Asked Questions

RAG is an AI architecture that connects large language models to authoritative external data sources, allowing the model to retrieve and cite relevant documents before generating a response. This eliminates the hallucination risk of relying solely on model training data and ensures answers reflect your actual organizational knowledge. For enterprises, RAG transforms static document repositories into interactive, queryable intelligence layers. It enables employees to get accurate, cited answers from SharePoint, Confluence, SAP, or proprietary databases without reading hundreds of pages manually.
Our RAG pipelines integrate with the full spectrum of enterprise content: SharePoint Online and on-premises, Confluence Cloud and Data Center, SAP document management, ServiceNow knowledge bases, Salesforce CRM records, internal SQL and NoSQL databases, file shares, PDF libraries, and API-served data. We build custom connectors for proprietary systems and legacy repositories using standardized ingestion interfaces. Incremental sync pipelines ensure your vector index stays current as source documents are updated, deleted, or versioned.
Chunking strategy is one of the highest-leverage decisions in RAG pipeline design and varies significantly by document type. We use semantic chunking for narrative documents, structure-aware chunking for PDFs with headings and tables, sliding window chunking for dense technical manuals, and entity-centric chunking for structured records like SAP or CRM exports. Each chunk retains provenance metadata - source document, section, version date, and author - which is critical for citation tracking in regulated industries. We conduct systematic chunking experiments during the evaluation phase to identify the configuration that maximizes retrieval recall and answer faithfulness for your specific content profile.
We build production RAG systems on Pinecone, Weaviate, Qdrant, Chroma, Milvus, and pgvector depending on your infrastructure constraints and scale requirements. Pinecone is preferred for fully managed, high-throughput production workloads with minimal operational overhead. Weaviate and Qdrant offer strong self-hosted options with rich filtering capabilities suited for hybrid search. pgvector is ideal when your team wants to keep all data within an existing PostgreSQL environment and avoid additional infrastructure dependencies. Selection criteria include expected query volume, embedding dimensionality, metadata filtering complexity, cloud provider alignment, and your organization's data residency policies.
Hybrid search combines dense vector similarity search with sparse keyword search (BM25 or TF-IDF), then merges results using reciprocal rank fusion or learned re-ranking models. Pure vector search excels at semantic similarity but can miss exact phrase matches, product codes, regulatory clause numbers, or technical identifiers that employees commonly search for. Hybrid search handles both semantic intent and keyword precision simultaneously, which is critical when employees query for specific contract clauses, part numbers, or policy codes. In enterprise benchmarks, hybrid search consistently outperforms pure vector search on recall at top-5 and top-10 by 15 to 35 percent, depending on the domain.
Initial retrieval - whether vector or hybrid - optimizes for broad recall but often surfaces tangentially related documents. Re-ranking applies a cross-encoder model that jointly evaluates the query and each candidate document together, producing a more precise relevance score than bi-encoder embeddings computed independently. We deploy re-rankers from Cohere, ColBERT, or domain-fine-tuned models depending on content type and latency budgets. Re-ranking typically increases answer faithfulness scores by 20 to 40 percent on enterprise knowledge base benchmarks. It is especially valuable for compliance, legal, and technical documentation where the top-ranked passage must be precisely on-point for the answer to be trustworthy.
Every answer our RAG system generates is anchored to specific retrieved passages, each carrying a full provenance record: source document title, URL or file path, section heading, page number, and ingestion timestamp. The front-end surfaces these citations inline with the answer so users can click through to the exact source. For regulated industries, we extend citation metadata to include document version, author, and approval status so compliance teams can audit which version of a policy informed a given response. Citation accuracy is tested systematically during QA - we verify that every factual claim in a generated answer can be attributed to a cited passage - and we track citation coverage rates as an ongoing production metric.
We evaluate RAG systems across four dimensions: context recall (did retrieval surface the relevant documents?), context precision (how much irrelevant content was retrieved?), answer faithfulness (does the generated answer stick to retrieved context?), and answer relevance (does it actually address the question?). We use RAGAS, TruLens, or custom evaluation harnesses depending on your tooling preferences and build ground-truth QA test sets from real employee queries and known-answer pairs. Evaluation runs on each pipeline change in CI/CD, with thresholds enforced as deployment gates. Post-launch, we instrument production traffic to detect retrieval drift and answer quality degradation over time.
Our default recommendation for English-language enterprise content is text-embedding-3-large from OpenAI or Cohere embed-v3, both of which deliver strong cross-domain performance out of the box. For highly specialized domains - legal, medical, engineering, or financial - we fine-tune embedding models on representative in-domain corpora using contrastive learning, which can improve retrieval recall by 10 to 25 percent versus general-purpose embeddings. We also evaluate multilingual embedding models for organizations with content in multiple languages. Embedding model selection is validated empirically against your actual document corpus and query distribution before any production deployment commitment.
Enterprise RAG must enforce the same access permissions that govern your source systems - users must never receive AI-generated answers sourced from documents they do not have permission to read. We implement attribute-based access control at the retrieval layer, passing user identity context from your identity provider (Azure AD, Okta, or similar) to filter the vector search results to only documents the querying user is authorized to access. Embeddings are stored with permission metadata and filtered at query time before re-ranking. All data in transit is encrypted with TLS 1.3 and at rest with AES-256. For regulated industries, we support private cloud and on-premises deployment models to ensure no enterprise content touches third-party infrastructure.
A focused enterprise RAG implementation covering one primary knowledge source with a validated front-end interface typically reaches production in 8 to 12 weeks. The first two weeks cover discovery, source system access, and evaluation set creation. Weeks three through six cover pipeline development: ingestion, chunking, embedding, vector DB indexing, and retrieval tuning. Weeks seven and eight focus on re-ranking, citation UI, and integration with your authentication system. Final weeks cover QA, load testing, and staged rollout. More complex implementations involving multiple heterogeneous sources, fine-tuned embeddings, or deep ERP integration extend to 14 to 20 weeks.
Yes. Many enterprise documents contain critical information in tables, charts, engineering diagrams, and scanned images that text-only pipelines miss entirely. We use document intelligence APIs (Azure Document Intelligence, AWS Textract, or custom OCR pipelines) to extract structured table data as markdown or JSON before chunking. For diagram-heavy content such as engineering drawings or org charts, we integrate vision-language models to generate textual descriptions that are embedded alongside the source image reference. This ensures that queries about tabular financial data, specification tables, or process diagrams return accurate, citation-linked answers rather than gaps or hallucinations.
Our RAG architecture is LLM-agnostic and has been deployed with GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama 3.1 70B, Mistral Large, and Command R+ depending on client requirements around cost, latency, data sovereignty, and output quality. We design the retrieval and prompt orchestration layer to be model-independent so organizations can swap or upgrade the generation model without rebuilding the pipeline. For clients with strict data residency requirements, we support self-hosted open-source models on Azure, AWS, or on-premises GPU clusters. LLM selection is benchmarked against your specific query types and answer quality criteria during the evaluation phase.
We build incremental ingestion pipelines that detect changes in source systems through webhooks, polling, or change data capture mechanisms depending on what each source supports. Modified documents are re-chunked and re-embedded, with the vector index updated in place without requiring a full re-index that would cause downtime. Deleted documents are removed from the vector store along with all associated chunks to prevent stale content from surfacing in retrieval results. Version-aware metadata tracking ensures that when a policy document is superseded, the retrieval layer can surface the latest version preferentially while retaining historical versions for audit purposes.
The most common failure modes are retrieval misses (the relevant document is not retrieved at all), context stuffing (irrelevant passages crowd out relevant ones, confusing the LLM), and faithfulness failures (the LLM generates plausible-sounding content not grounded in retrieved context). We address retrieval misses with hybrid search and query rewriting strategies that expand ambiguous queries before retrieval. Context stuffing is mitigated by aggressive re-ranking and limiting context window utilization to the top three to five high-confidence passages. Faithfulness failures are reduced by explicit grounding instructions in the system prompt and automated faithfulness scoring in production. We also monitor for embedding drift over time, which degrades retrieval quality as language in your documents evolves.
Regulated industry RAG implementations require additional layers of governance that we build in from the start rather than retrofitting later. Citation tracking is mandatory and must link to specific document versions approved by your compliance process. We implement answer confidence scoring so the system can decline to answer queries that fall outside the scope of retrieved context rather than generating ungrounded responses. Audit logging captures every query, retrieved context set, and generated answer for regulatory examination. For healthcare, we evaluate HIPAA compliance requirements for PHI handling in the vector store. For financial services, we implement model risk management documentation aligned with SR 11-7 guidance. For legal applications, we add privilege classification metadata to prevent privileged documents from surfacing to unauthorized query contexts.
Industries
Financial ServicesHealthcare and Life SciencesLegal and Professional ServicesManufacturing and EngineeringGovernment and Public Sector