LLM & RAG Application Development
Build LLM-powered apps — chat, search, and assistants grounded in your own data.
Pricing
Contact for price
Scoped per data sources and features.
3–10 weeks depending on scope
Overview
We build LLM applications — chatbots, semantic search, copilots, and RAG systems grounded in your documents and data — with guardrails, evaluation, and optional self-hosting, so your AI features are accurate, safe, and on-brand.
Problems we solve
- Generic AI tools that don't know your business
- Information buried in documents and systems
- Concerns about AI accuracy, privacy, and cost
Key features & capabilities
- Retrieval-augmented generation (RAG)
- Custom chatbots & copilots
- Semantic search over your data
- Guardrails & evaluation
- Optional self-hosted models
How you benefit
- AI that actually knows your business
- Instant answers from your own knowledge
- Accurate, grounded responses with guardrails
- Privacy via optional self-hosting
Relevant use cases
- Knowledge-base assistant
- Customer-support copilot
- Internal document Q&A
Implementation process
- Use-case & data discovery
- RAG/app build & guardrails
- Evaluation & tuning
- Deploy & monitor
What you receive
- LLM application
- Knowledge ingestion pipeline
- Guardrails & evaluation
- Monitoring
Integration options
- Docs/knowledge bases
- Apps & websites
- Internal APIs
Deployment models
- Cloud (managed)
- Self-hosted/private
- Hybrid
Security & compliance
- Data scoping & access control
- Guardrails & audit logging
Support & maintenance
- Prompt/RAG tuning
- Knowledge & model updates
Service packages
Choose a starting point — every engagement is tailored, and final scope and pricing are confirmed in your quote.
Why choose AXZRO?
We build and run our own self-hosted LLM assistant
Grounded, guardrailed AI — not a generic wrapper
Privacy-first with self-hosting options
Frequently asked
Can it run on our own servers?
Yes — we self-host models (we run AXZRO's own assistant this way) for full data privacy.
How do you keep answers accurate?
RAG grounds answers in your data, plus guardrails and evaluation to reduce hallucination.
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