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AI Search & RAG Solutions in Kenya

Let AI Answer From Your Own Knowledge. Build enterprise Retrieval-Augmented Generation (RAG) systems that ground language models on your private documents with verifiable citations and zero hallucinations.

ACCURACY THROUGH GROUNDING

Eliminate Hallucinations by Anchoring AI on Your Verified Data

Standard AI models only know what they were trained on during their initial training cutoff. If you ask them about your company's proprietary pricing tiers, internal HR policies, or technical service manuals, they either guess or hallucinate plausible-sounding falsehoods.

Beatsy Solutions engineers enterprise Retrieval-Augmented Generation (RAG) architectures. When a user asks a question, our system first retrieves the most relevant semantic passages from your private vector database, feeds those exact excerpts into the model prompt, and forces the model to synthesize an answer exclusively from that evidence.

Verifiable Page & Paragraph Citations

Every response generated by our RAG pipelines includes clickable reference citations. Users can click any citation card to view the exact source PDF, page number, and paragraph, eliminating guesswork and establishing audit trust.

RAG Engineering Pillars

Semantic Chunking & Metadata

We parse complex tables, headings, and lists into coherent semantic chunks tagged with author, date, and security permissions.

Hybrid Vector & BM25 Retrieval

Combines dense vector similarity with sparse BM25 keyword matching so acronyms, product SKUs, and concepts are all found accurately.

Cross-Encoder Re-Ranking

Passes candidate chunks through a cross-encoder re-ranker to ensure only the top 3-5 most pertinent context passages reach the LLM.

Security & Tenant Partitioning

Vector embeddings are partitioned by tenant and user role, guaranteeing employees never retrieve confidential executive records.

TECHNICAL ARCHITECTURE

How Retrieval-Augmented Generation Works

The end-to-end journey from raw documents to verified answer generation.

1

Ingestion & Vectorize

PDFs, spreadsheets, and databases are parsed and embedded into high-dimensional vectors stored in pgvector or Qdrant.

2

Query Embedding

When a user asks a question, it is converted into a vector vector in real-time to search semantic similarity.

3

Context Injection

The top matching document passages are injected directly into the LLM system prompt as the sole reference material.

4

Cited Generation

The model generates a factual answer referencing the exact source documents. If no evidence exists, it states so clearly.

PRACTICAL APPLICATIONS

Enterprise RAG Scenarios in Kenya

Grounding AI across knowledge-heavy commercial workflows.

Policy & HR Manuals

Enable 500+ employees to search company leave policies, medical schemes, and grievance procedures with instant paragraph citations.

Legal Precedent Search

Search thousands of Kenyan case law judgments, statutes, and legal gazettes for relevant precedents in seconds.

Engineering & Telecom

Help field engineers query equipment installation manuals and technical schematics directly on their phones on site.

Medical Guidelines

Assist healthcare practitioners in retrieving verified clinical protocols and drug dosage guidelines without speculative output.

RAG Tiers

AI Search & RAG Packages

Includes document parsing pipeline, vector database setup, hybrid search tuning, citation interface, and initial maintenance.

Standard Price

Starting From Only
Ksh 50,000 One-Time Payment
Internal company AI assistant to search documents and generate reports.
  • Internal business AI assistant
  • Search company documents
  • Answer staff queries
  • Generate reports & summaries
  • Knowledge-base integration
  • Role-based access
RAG Add-Ons

Search & RAG Add-Ons

Add automated cloud storage connectors, cross-encoder re-ranking, or private dedicated on-premise vector hosting.

Integration
WhatsApp Business Cloud AI Integration
Ksh 20,000

Direct connection with official Meta WhatsApp Cloud API with webhook routing and conversational state management.

Knowledge
Custom PDF & Company Docs RAG Ingestion
Ksh 25,000

Parsing, semantic chunking, and vector indexing for up to 500 pages of proprietary PDF manuals and company documents.

Support
Live Agent Human Handover Module
Ksh 15,000

Seamless escalation protocol that packages conversation summaries and transfers live sessions to WhatsApp or email reps.

Sales
Automated Lead Qualification & CRM Push
Ksh 18,000

Automatic capture of contact details and instant push to CRM, Google Sheets, or email/SMS alerts.

Workflow
Custom AI Workflow Pipeline Connector
Ksh 22,000

Cross-system automated trigger connecting email, Google Drive, or ERP webhooks to AI processing tasks.

Security
Role-Based Knowledge Access Control
Ksh 15,000

Departmental metadata filtering ensuring staff only retrieve information corresponding to their authorized role.

Maintenance
Post-Launch AI Fine-Tuning & SLA Monitoring
Ksh 25,000

3 months of prompt optimization, vector index re-indexing, hallucination auditing, and model version maintenance.

RAG FAQs

Frequently Asked Questions: AI Search & RAG Solutions

Add automated cloud storage connectors, cross-encoder re-ranking, or private dedicated on-premise vector hosting.

RAG is an architecture that connects an AI model directly to your approved company documents. When a user asks a question, the system first retrieves the most relevant paragraphs from your indexed documents and provides them as factual context to the AI, ensuring responses are grounded in your actual business records.

No technology eliminates hallucinations 100%, but RAG dramatically reduces them by restricting the AI to answer only from verified retrieved excerpts. If the information is not in your documents, the system is instructed to state that the answer is not available.

Yes. Documents are pre-processed, chunked into logical semantic segments, converted into mathematical vector embeddings, and indexed into a vector database that performs millisecond similarity searches across thousands of pages.

Yes. Every generated answer includes clickable footnotes or source badges showing the exact document name, page number, and paragraph excerpt from which the answer was synthesized.

When you upload an amended document or policy, the outdated vector chunks are purged and the new document is embedded and indexed, making updated information immediately available across all queries.

Yes. We apply metadata filtering to vector search queries, ensuring that an employee querying the system only retrieves context from documents authorized for their security role or department.

We deploy PostgreSQL with the pgvector extension for robust relational and vector storage, as well as dedicated vector engines such as Qdrant, ChromaDB, or Pinecone depending on your data volume and infrastructure.

Ground Your AI on Real Company Facts

Let us build a private, verifiable search and RAG engine that turns your organization's document repositories into an intelligent, cited knowledge assistant.

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