Internal Knowledge Assistant
Answer employee questions instantly.
RAG Development
Enterprise RAG systems that deliver trusted AI answers
Transform documents, policies, SOPs, contracts, knowledge bases, and operational data into intelligent AI assistants that provide accurate, cited, and secure answers.
Enterprise SearchAI Knowledge AssistantsHybrid RetrievalCitation-Based AnswersMulti-Source KnowledgePermission-Aware AccessRAG EvaluationProduction Monitoring
- Millions
- Documents indexed
- Sub-second
- Retrieval latency
- Enterprise
- Security & permissions
- Production
- Ready architecture
What it unlocks
What enterprise RAG makes possible
RAG is not just search. It becomes the knowledge layer powering intelligent business applications.
Policy & SOP Assistant
Surface procedures and compliance guidance.
Customer Support Assistant
Provide accurate, cited support responses.
Contract Intelligence
Search, compare, and analyze contracts.
Sales Enablement Assistant
Find answers from product, pricing, and sales content.
Executive Knowledge Copilot
Access organizational intelligence instantly.
Healthcare Knowledge Assistant
Clinical and operational guidance retrieval.
Education Knowledge Assistant
Curriculum and learning content retrieval.
Pet Care Knowledge Assistant
Care protocols, treatment knowledge, and operations support.
What we build
End-to-end RAG, not just a vector DB
We deliver the whole pipeline — ingestion, chunking, embedding, retrieval, re-ranking, generation, evaluation — and the evals that prove it works.
01
Enterprise Search
One search bar across every system your teams live in.
- Cross-system search
- Knowledge graph enrichment
- Permission inheritance
02
Semantic Retrieval
Hybrid lexical + vector search that beats keyword on long-tail queries.
- Hybrid retrieval
- Query rewriting
- Context expansion
03
Vector Database Setup
Pick and provision the right store for performance, cost, and operability.
- PGVector
- Pinecone
- Qdrant
- Weaviate
04
Document Processing
Reliable extraction from PDFs, scans, slides, and contracts.
- OCR
- PDF extraction
- Image understanding
- Metadata enrichment
05
Multi-Source Knowledge
One assistant answering from documents, databases, and live systems.
- CRM integration
- ERP integration
- Database retrieval
- API retrieval
06
RAG Quality Engineering
Evals and dashboards so retrieval quality only goes up.
- Groundedness scoring
- Hallucination detection
- Retrieval evaluations
- Answer quality testing
Architecture
Enterprise RAG architecture
Production-grade RAG requires much more than a vector database. Seven layers, each observable and secure.
1
Knowledge Sources
DocumentsPoliciesConfluenceSharePointCRMERPDatabases
2
Processing Layer
OCRChunkingMetadata extractionClassification
3
Embedding Layer
Embedding modelsVector generation
4
Storage Layer
Vector DBMetadata storePermission index
5
Retrieval Layer
Hybrid searchRe-rankingFiltering
6
Generation Layer
LLMsPrompt orchestrationCitation generation
7
Evaluation Layer
Quality metricsTracingMonitoringObservability
Retrieval engineering
Vector database & search expertise
No vendor bias. We benchmark against your data and volume, then recommend the lowest-friction option.
PGVector
Best for PostgreSQL-first architectures.
Qdrant
High-performance open-source vector search.
Pinecone
Managed enterprise vector database.
Weaviate
Semantic search platform with modules.
Elasticsearch
Hybrid keyword + semantic search.
OpenSearch
Enterprise-scale retrieval systems.
Selection matrix
| Database | Performance | Cost | Scale | Multi-tenancy | Ops complexity | Recommended for |
|---|---|---|---|---|---|---|
| PGVector | Good | Low | Medium | App-level | Low | Postgres shops, moderate volume |
| Qdrant | High | Low–Med | High | Native | Medium | Self-hosted, performance-sensitive |
| Pinecone | High | High | Very high | Native | Very low | Fully-managed, fast time-to-market |
| Weaviate | High | Med | High | Native | Medium | Hybrid search + built-in modules |
| Elasticsearch | High | Med–High | Very high | Index-level | High | Existing ELK, strong keyword needs |
| OpenSearch | High | Med | Very high | Index-level | High | AWS-native, open-source ELK alt |
Showcase
Knowledge assistants we build
Each one is a focused RAG application — scoped to a real problem, measured against real answers.
Employee Knowledge Assistant
- Problem
- Staff waste hours hunting across wikis and drives.
- Solution
- Unified RAG over all internal docs.
- Benefit
- Faster answers, less interruption.
Customer Support Assistant
- Problem
- Agents give inconsistent, slow answers.
- Solution
- Cited RAG over help center + policies.
- Benefit
- Faster, consistent resolutions.
Compliance Assistant
- Problem
- Policies are scattered and hard to apply.
- Solution
- RAG over policy + regulatory corpus.
- Benefit
- Confident, traceable guidance.
Legal Assistant
- Problem
- Contract review is manual and slow.
- Solution
- Contract intelligence with clause search.
- Benefit
- Faster review, fewer misses.
Healthcare Assistant
- Problem
- Clinical and ops knowledge is siloed.
- Solution
- Permission-aware clinical RAG.
- Benefit
- Quicker, safer guidance retrieval.
Therapy Assistant
- Problem
- Goals, assessments, and protocols are fragmented.
- Solution
- RAG over care plans + intervention library.
- Benefit
- Better-informed care decisions.
Education Assistant
- Problem
- Learners can't find the right content.
- Solution
- Curriculum-grounded Q&A.
- Benefit
- Higher engagement and outcomes.
Pet Care Assistant
- Problem
- Treatment knowledge lives in many heads.
- Solution
- RAG over protocols + clinic operations.
- Benefit
- Consistent, fast care support.
Operations Assistant
- Problem
- SOPs and runbooks go unread.
- Solution
- RAG over operational documentation.
- Benefit
- Fewer errors, faster execution.
Marketplace Knowledge Assistant
- Problem
- Buyers and sellers can't self-serve.
- Solution
- RAG over catalog + policy + support.
- Benefit
- Higher conversion, lower support load.
Quality engineering
How we measure RAG quality
If you cannot measure retrieval quality, you cannot trust your AI system. We ship the evals with the system.
Context Recall
Context Precision
Answer Accuracy
Groundedness
Citation Accuracy
Latency
User Satisfaction
Hallucination Rate
Evaluation workflow
Test Dataset
Retrieval Evaluation
Answer Evaluation
Human Review
Production Monitoring
Continuous Optimization
Security & governance
Enterprise security & governance
Built for procurement and InfoSec from day one — not bolted on after the pilot.
Permission-Aware Retrieval
Results filtered per user at query time.
Role-Based Access
Scoped to roles, teams, and tenants.
Source-Level Security
Access inherited from the source system.
Audit Logging
Every query and answer is traceable.
Data Encryption
TLS in transit, KMS-backed at rest.
Private Deployments
VPC, dedicated tenancy, or on-prem.
Compliance Support
GDPR-aware; HIPAA-aware for healthcare.
Security Reviews
Threat modeling and red-team testing.
Users only see information they already have access to.
Vertical depth
Industry-specific knowledge systems
The retrieval patterns transfer; the domain tuning is where we earn our keep.
Healthcare
- Clinical documentation
- Medical protocols
- Knowledge assistants
Therapy
- Goals
- Assessments
- Intervention knowledge
Education
- Learning content
- Question answering
- Curriculum assistants
Pet Care
- Treatment protocols
- Clinic operations
- Knowledge retrieval
Retail
- Product knowledge
- Operations knowledge
- Vendor knowledge
Manufacturing
- Maintenance knowledge
- Operational procedures
- Compliance documentation
Proof of execution
Built on real platform experience
We don't just build RAG systems for clients. We build and operate AI-powered platforms ourselves.
Knowledge AssistantsAI SearchRecommendation SystemsOperational IntelligenceMulti-Tenant Architectures
BloomCommerce
The complete stack for the e-commerce ecosystem
Connects sellers, buyers, warehouses, logistics partners, and affiliates — AI-driven discovery, seamless checkout, and end-to-end order fulfilment on one platform.
Explore BloomCommerce →BloomLearnThe complete platform for the education industry
Connects academies, tutors, teachers, students, and parents — structured learning, AI adaptive review, and full academy operations from one spine. Powers Eductate, our flagship NEET / JEE / UPSC / TNPSC / TET prep surface.
Explore BloomLearn →BloomSenz PlatformAI-native therapy operations for providers and families
Mission-critical workflows for centres, therapists, audiologists, doctors, and the families who depend on them — AI-assisted documentation, apps, and unified care journeys.
Explore BloomSenz Platform →BloomPetosMarketplace platform for the pet care ecosystem
Connects pet parents, clinics, groomers, walkers, sitters, trainers, and specialty retailers — discovery, booking, health timelines, and commerce on one marketplace spine.
Explore BloomPetos →BloomCRMThe customer relationship spine for the BloomSenz ecosystem
Modular monolith CRM serving every Splendens vertical — leads, marketing campaigns, service-team operations, and revenue analytics from a single Spring Boot backend.
Explore BloomCRM →Outcomes
Business outcomes our RAG systems target
Reduce employee search time
Reduce support workload
Improve knowledge accessibility
Accelerate onboarding
Improve response consistency
Reduce repetitive questions
Increase productivity
Improve decision-making
Actual results depend on implementation scope and knowledge quality.
Methodology
How we ship a RAG system
Nine disciplined steps from knowledge discovery to a monitored production system.
Knowledge Discovery
Data & Access Analysis
Content Processing Strategy
Architecture Design
Retrieval Engineering
Evaluation Framework
Pilot Deployment
Production Rollout
Monitoring & Optimization
For decision-makers
Why enterprise RAG is different from ChatGPT
A public chatbot can't see your data, respect your permissions, or cite its sources. Enterprise RAG does all three.
| Capability | Public ChatGPT | Enterprise RAG |
|---|---|---|
| Enterprise permissions | ||
| Uses your internal data | ||
| Provides citations | ||
| Governance & audit | ||
| Knowledge remains yours |
FAQ
Common RAG questions
- Which vector database should we choose?
- It depends on volume, ops maturity, and whether you're already on Postgres. We benchmark against your data and recommend the lowest-friction option — often PGVector for Postgres shops, Qdrant for self-hosted performance, or Pinecone for fully-managed speed.
- Can RAG work with SharePoint?
- Yes. We connect to SharePoint, sync incrementally, and inherit its permissions so users only retrieve what they're allowed to see.
- Can RAG work with Confluence?
- Yes — Confluence is one of the most common sources. We index spaces and pages with their access controls intact.
- Can RAG access databases?
- Yes. Beyond documents, we retrieve from relational databases, APIs, and runbooks, fusing structured and unstructured sources into one answer.
- Can RAG respect user permissions?
- Yes. We index permissions alongside content and filter retrieval per user at query time. Most enterprise rollouts require this from day one.
- How do you prevent hallucinations?
- Grounded generation with citations, groundedness scoring, retrieval evals on golden datasets, and human review gates where stakes are high — measured continuously, not assumed.
- Can RAG work on-premise?
- Yes. The same containers run in your VPC, on dedicated tenancy, or fully on-prem with self-hosted models when zero data egress is required.
- How long does implementation take?
- A vertical-slice MVP typically ships within a few weeks; production hardening and rollout follow. The knowledge audit in week one sets the precise timeline.
- How much data can be indexed?
- Millions of documents. We size the vector store and pipeline to your corpus and growth, with incremental indexing so freshness keeps up.
- Do you support multilingual knowledge?
- Yes. Multilingual embeddings and retrieval let users query in their language against content in another.
Get started
Turn organizational knowledge into an AI advantage
Build secure, citation-based AI assistants powered by your own documents, systems, and knowledge assets.
Knowledge audit · Architecture review · Retrieval strategy · MVP roadmap