Unlock your enterprise knowledge with AI search and retrieval-augmented generation. Austronix builds RAG systems that ground LLMs in your documents, policies, databases and applications — delivering accurate, cited, secure answers at scale.
AI search and retrieval-augmented generation (RAG) transform how enterprises access knowledge. Instead of relying on an LLM's training data, RAG retrieves relevant, approved information from your systems and generates answers grounded in that context — with citations.
We help enterprises build AI search and RAG systems over their documents, knowledge bases, wikis, databases, tickets, contracts, policies and internal applications. Whether you need a semantic search layer, a question-answering system, a document assistant or a RAG-powered copilot, we engineer the retrieval and generation pipeline around your data and workflows.
Our approach combines document ingestion, chunking, embeddings, vector databases, hybrid retrieval, re-ranking, prompt architecture, citation and evaluation into one coordinated delivery process. Every RAG system is grounded in your approved sources and constrained by your access policies.
We focus on RAG systems that are accurate, observable and dependable in production — with freshness controls, access control, hallucination checks, cost controls and evaluation frameworks built in from the start.
Enterprises see value in AI search and RAG but struggle with data quality, retrieval accuracy, security, freshness, cost and adoption. Our systems are engineered to solve these problems in real production environments.
Documents, wikis, tickets, databases and apps hold knowledge that teams can't easily search. We build unified AI search across approved enterprise sources.
LLMs without grounding invent facts. We build RAG pipelines that retrieve approved sources and cite them so answers are accurate and traceable.
Search and RAG must respect who can see what. We build access-aware retrieval that filters results by user role, permission and policy.
Knowledge changes constantly. We implement freshness controls, incremental indexing and version handling so answers reflect current information.
Simple vector search often misses relevant results. We combine hybrid retrieval, re-ranking and query understanding to improve accuracy.
Embedding, indexing and generation can be expensive. We implement caching, batching, model routing and monitoring to keep costs predictable.
End-to-end AI search and RAG services — from strategy and data preparation to retrieval, generation, deployment and evaluation.
Identify the highest-impact search and RAG use cases inside your organization and define a technical roadmap aligned with business goals.
Ingest documents from your repositories, file shares, wikis, CMS, email and databases — with parsing, cleaning and metadata extraction.
Split documents intelligently and generate embeddings that capture meaning for accurate semantic retrieval.
Store and query embeddings in the right vector database for your scale, latency, filtering and cost requirements.
Combine keyword, vector and hybrid search with re-ranking and query understanding for the best retrieval quality.
Build retrieval-augmented generation pipelines that retrieve context, construct prompts and generate grounded answers with citations.
Generate accurate, cited answers grounded in retrieved enterprise sources — not model memory.
Ensure search and RAG respect user roles, permissions and data policies — different users see different results.
Evaluate retrieval accuracy, answer grounding, citation quality, latency and cost across real user questions and edge cases.
Measure search usage, retrieval quality, answer accuracy, cost and business impact across teams and use cases.
Implement secure access, audit, PII handling and compliance controls for AI search and RAG systems.
Deploy AI search and RAG on secure, scalable cloud infrastructure with monitoring, cost controls and continuous delivery.
Production-grade AI search and RAG systems designed for specific roles, applications and workflows.
Unified AI search across documents, wikis, policies, tickets and databases with semantic understanding and citations.
Ask questions over contracts, policies, manuals, research and reports and get grounded answers with source references.
RAG systems that retrieve policies, help articles and past tickets to draft accurate support replies.
Search and RAG over proposals, case studies, pricing and product docs to help sales teams respond faster.
RAG over code, docs, runbooks and tickets to help engineers find answers and resolve issues faster.
RAG systems that answer HR policy questions, benefits queries and onboarding questions with citations.
RAG over contracts, regulations and compliance documents with clause extraction and citation.
RAG over reports, filings and financial documents to answer questions with grounded, cited responses.
RAG systems that retrieve across multiple enterprise sources with shared identity, permissions and knowledge.
Whether you want enterprise knowledge search, document Q&A, support RAG or a multi-source RAG platform — let's discuss your data sources, use cases, access requirements and success metrics, and identify the right models, architecture and delivery approach.
Real outcomes enterprises see when search and RAG are engineered for production.
AI search and RAG help teams find accurate answers in seconds instead of searching across multiple systems manually.
Grounding answers in approved enterprise knowledge lets teams get cited, trustworthy responses without digging through documents.
Automating retrieval, summarization and citation frees teams to focus on higher-value work.
A structured lifecycle that takes an AI search or RAG system from use-case definition to production deployment and continuous improvement.
We identify the roles, workflows and questions where AI search and RAG will deliver the highest impact, and align on outcomes.
We map the documents, knowledge bases, databases and applications that will feed the search and RAG system.
We design the ingestion, chunking, embedding, retrieval, re-ranking, prompt and citation architecture.
We ingest, parse, chunk and embed approved sources, and build the vector and keyword indexes.
We build the search experience, retrieval pipeline, generation layer, citations, access control and API integrations.
We evaluate retrieval accuracy, grounding, citations, safety, latency and cost across real questions and edge cases.
We deploy the system to production with secure infrastructure, monitoring, audit logging and cost controls.
We monitor usage, retrieval quality, answer accuracy, latency and cost, and iterate based on real feedback.
After launch, we improve ingestion, retrieval, re-ranking, prompts and coverage as enterprise needs and AI capabilities evolve.
The models, frameworks, retrieval layers, vector databases and infrastructure we use to build AI search and RAG systems.
From focused document Q&A systems to enterprise-wide search and RAG platforms.
The architectural patterns and engineering capabilities behind accurate, secure, production-grade AI search and RAG systems.
AI search and RAG systems touch valuable enterprise knowledge, so security and compliance must be designed into the architecture from day one. We apply practical controls aligned with your policies and risk profile.
Search and RAG quality means retrieval accuracy, grounding, citation quality, tone, safety, latency and cost — validated continuously across the lifecycle.
We maintain clear communication so stakeholders understand the use cases, data sources, model choices, retrieval strategy, evaluation results, costs and roadmap.
Deep enterprise experience, secure engineering and a focus on accuracy — that's how we build AI search and RAG systems that actually get used.
We design search and RAG systems for reliability, security, auditability and scale — not just demos.
We connect search and RAG to your approved documents, knowledge and systems so answers are accurate and traceable.
Search and RAG respect your identity, roles and policies. Different users see different results.
We combine hybrid retrieval, re-ranking and query understanding to surface the most relevant results.
We design search and answer UX that fits into workflows instead of interrupting them.
Caching, batching, routing, prompt optimization and monitoring keep search and RAG costs predictable as usage scales.
We implement tracing, analytics and evaluation so you can see exactly how the system is performing.
We continue supporting your search and RAG system with tuning, new sources, model updates and maintenance.
Flexible delivery models for everything from a single search PoC to a long-term enterprise RAG program.
Validate the value of search or RAG for a specific role or dataset in a controlled pilot with clear success metrics.
Ideal For
End-to-end delivery of a clearly defined search or RAG system with ingestion, retrieval, generation and deployment.
Ideal For
A dedicated team continuously builds, improves and scales your search and RAG platform across sources and use cases.
Ideal For
Flexible engineering for adding search or RAG features to existing products or evolving systems over time.
Ideal For
Every engagement produces clear, reviewable artifacts across strategy, architecture, implementation, safety and operations.
Faster knowledge access, better customer experiences, deeper insight and smarter products — the outcomes AI search and RAG deliver.
Help teams find accurate answers in seconds instead of searching across multiple systems manually.
Empower support and sales teams with instant, grounded, cited answers inside their tools.
Make documents, policies, tickets and databases instantly usable with citations and traceability.
Automate research, triage, summarization and question-answering across every function.
Add AI search and RAG capabilities to your SaaS and internal tools to differentiate your product experience.
Build search and RAG architecture that evolves as data, models, users and business needs grow.
You don't always need to build a new product. Existing applications, portals and internal tools can often be enhanced with AI search and RAG features.
Launching an AI search or RAG system is the beginning of its operational lifecycle. These systems require monitoring, evaluation, prompt tuning, model updates, index refreshes, safety updates and continuous improvement.
AI search uses semantic understanding, vector embeddings and hybrid retrieval to find relevant information across documents, databases and applications — going beyond keyword matching to understand meaning and intent.
RAG is a technique that combines search with large language models. It retrieves relevant, approved information from your data sources and uses it as context for the LLM to generate grounded, cited answers instead of relying on model memory.
Fine-tuning changes the model's weights using your data, while RAG retrieves relevant context at query time and passes it to the model. RAG is often faster to implement, easier to update and provides citations and access control.
We connect to document repositories, file shares, wikis, CMS, email, databases, ticketing systems, CRM, ERP, code repositories, intranets and APIs — with appropriate parsing and metadata extraction.
We integrate SSO and enterprise identity, enforce role-based and document-level permissions, filter retrieval results by user access and log all activity for audit.
We ground generation in retrieved approved sources, require citations, validate outputs, implement refusal handling when no source is found and evaluate grounding continuously.
We work with commercial and open-source models including GPT-4o, Claude 3.5, Gemini, Azure OpenAI, AWS Bedrock and open-source LLMs — selected based on quality, cost, latency and privacy.
We use embedding batching, caching, model routing, prompt optimization, streaming and usage monitoring to keep costs predictable as usage grows.
We define task-based success metrics, track retrieval relevance, answer grounding, citation quality, latency, cost per query and user feedback — and iterate based on real usage.
A focused PoC for one dataset can typically be delivered in 2–4 weeks. A production system with multi-source ingestion, access control, citations and analytics usually takes 6–14 weeks depending on scope.
Yes. We design search and RAG systems that support multiple sources, teams, workspaces and tenants with strict isolation, access differences and audit trails.
Yes. We provide ongoing monitoring, retrieval tuning, model updates, index refreshes, guardrail updates, cost optimization, new source integration and analytics after launch.
We empower organizations across diverse sectors with custom technology architectures designed to solve unique operational challenges and accelerate market growth.
We create secure and scalable digital solutions for healthcare providers, pharmaceutical organizations, biotechnology companies, and medical technology businesses.
Healthcare platforms, patient portals, hospital systems, and digital health applications.
Research platforms, pharmaceutical operations, data management, and biotechnology solutions.
Connected medical technology, device platforms, monitoring systems, and digital medical solutions.
As enterprises embrace AI at scale, we’re helping them transform data into intelligence, insights into action, and potential into growth. Tell us a bit more about yourself, so we can explore what’s possible together.
We'll carefully review your requirements and assess the best path forward.
One of our dedicated experts will reach out to you within 24 hours.
We'll schedule a comprehensive discovery call to deeply understand your project goals.
We'll deliver a tailored AI strategy, technical proposal, and project roadmap.
