Enterprise Knowledge Bases
Connect AI applications to internal documents, policies, manuals and business knowledge.
RAG Development
Connect AI to your knowledge with reliable retrieval.
Minvalam builds Retrieval-Augmented Generation systems that connect LLMs to private documents, databases and business knowledge to produce more useful, context-aware AI experiences.
RAG
Enterprise knowledge
Vector
Semantic retrieval
Hybrid
Search architecture
Secure
Data access
AI Architecture
Production-ready intelligence
Input & Context
Users, business data and operational signals
Intelligence Layer
Models, retrieval, reasoning and orchestration
Business Actions
APIs, workflows, systems and human review
Production deploymentReady to scale
What we build
Build AI that knows your business context.
RAG gives AI applications a retrieval layer that can locate relevant information before generating an answer.
Vector Search
Use embeddings and semantic retrieval to find contextually relevant information.
Hybrid Search
Combine semantic and keyword retrieval when both meaning and exact terms matter.
Document Processing
Ingest, normalize, chunk and enrich complex business documents.
Access Control
Respect user and organizational permissions when retrieving private information.
RAG Evaluation
Evaluate retrieval quality, answer grounding and system behavior against realistic data.
Capabilities
A complete RAG architecture, not just a vector database.
Effective RAG requires thoughtful ingestion, retrieval, ranking, context construction, generation and evaluation.
Discuss your requirementsDocument ingestion
Text extraction
Chunking strategies
Embeddings
Vector databases
Hybrid search
Metadata filtering
Reranking
Context construction
Citation support
Access control
RAG evaluation
Our process
From AI strategy to production
We combine product thinking, software engineering and AI expertise to take ideas from concept to reliable production systems.
01
Data
Identify and understand the knowledge sources.
02
Ingest
Build reliable pipelines for documents and data.
03
Index
Create embeddings, metadata and searchable indexes.
04
Retrieve
Find the most relevant context for each query.
05
Generate
Provide retrieved context to the language model.
06
Evaluate
Measure retrieval and answer quality.
07
Optimize
Improve accuracy, latency and operating cost.
Enterprise-ready
AI engineered for real business outcomes.
Great AI software is not just about models. It requires secure architecture, useful workflows, measurable outcomes and dependable production engineering.
Grounded AI responses
Private knowledge integration
Permission-aware retrieval
Source-aware answers
Flexible data ingestion
Hybrid retrieval
Scalable indexing
Continuous evaluation
Technology
Built with modern AI engineering stacks
We select technology based on the product, performance, security and operational requirements rather than forcing every project into one stack.
PythonFastAPIOpenAIAnthropicLlamaLangChainLlamaIndexPostgreSQLpgvectorRedisVector DatabasesAWSDockerKubernetes
Industries
AI for the way your industry actually works.
We adapt AI architectures to the data, workflows, regulations and operational realities of each business.
Legal
Finance
Healthcare
SaaS
Education
Manufacturing
Customer Support
Enterprise IT
Professional Services
FAQ
Frequently asked questions
Have a more specific requirement? Our engineers can help you evaluate the right architecture.
Talk to an engineerWhat is RAG?
Retrieval-Augmented Generation is an architecture where relevant information is retrieved from an external knowledge source and supplied to a language model as context before generating an answer.
Why use RAG instead of only an LLM?
RAG allows an application to work with current, domain-specific or private information that may not be contained in the model's general training knowledge.
Can RAG work with PDFs and internal documents?
Yes. RAG pipelines can ingest documents such as PDFs, text files, web content and structured business information after appropriate processing.
Can RAG enforce user permissions?
Yes. Retrieval can be designed around metadata, roles, tenant boundaries and application-level authorization so users only retrieve information they are allowed to access.
Ready to build your next AI product?
Tell us what you're building, what problem you're solving and where you want AI to take your business.