Generative AI & LLM Applications
- RAG assistants with cited, grounded answers
- Model-agnostic: GPT, Claude, Gemini, Llama
- Agents that take action, with humans in the loop
- Evaluation, guardrails and cost controls built in
Why Generative AI with VN Softverg
Generative AI can draft, summarize, search, answer and act, but a demo is not a product. Production applications need accurate answers grounded in your data, guardrails against misuse, cost control, and integration with the systems your people already use.
We build LLM applications using models such as OpenAI GPT, Anthropic Claude, Google Gemini and open-weight models like Llama and Mistral, served through Azure OpenAI, Amazon Bedrock, Vertex AI or self-hosted infrastructure. Retrieval-augmented generation (RAG) with vector databases like pgvector, Pinecone or Azure AI Search keeps answers grounded and cited.
For workflows that need more than answers, we build AI agents that call tools and APIs, with human approval steps where it matters. Every solution includes evaluation, observability and security controls covering prompt injection, data leakage and access permissions, so you can launch with confidence.
Everything you need, under one roof
GenAI Strategy & Use-Case Design
Identify high-value use cases, assess data and risk, choose models and hosting, and define how success and quality will be measured.
- Prioritized use-case roadmap
- Model and hosting recommendation
- Risk and governance assessment
RAG Knowledge Assistants
Assistants that answer questions from your documents, wikis, tickets and databases, with chunking, hybrid search, re-ranking and source citations.
- Ingestion and indexing pipeline
- Retrieval and prompt design
- Chat interface with citations
Customer & Employee Chatbots
Conversational assistants for websites, support, HR and IT helpdesks, integrated with channels like web, WhatsApp, Microsoft Teams and Slack.
- Conversation design
- Channel integrations
- Human handoff workflow
AI Agents & Workflow Automation
Agents that use tools and APIs to look up records, draft responses, update systems and complete multi-step tasks, using frameworks such as LangGraph or the Model Context Protocol.
- Agent architecture and tool definitions
- Approval and audit controls
- Integrated agent workflows
Copilots & Document Intelligence
In-app copilots that summarize, draft and extract, plus document processing for invoices, contracts, claims and forms.
- Copilot feature integration
- Extraction schemas and pipelines
- Accuracy evaluation report
LLMOps, Evaluation & Guardrails
Automated evaluation datasets, tracing with tools like Langfuse or LangSmith, prompt versioning, guardrails against prompt injection and PII leakage, and cost monitoring.
- Evaluation suite
- Observability dashboards
- Guardrail and security configuration
Measurable from day one
Typical timeline for a RAG or chatbot prototype on your own data.
Goal for RAG assistants to reference the source behind every answer.
Quality is measured against a test set before every prompt or model change.
Typical goal for time-to-first-token in streaming chat interfaces.
A clear path from kickoff to results
Identify
Select a high-value use case, define success criteria and review data, security and compliance requirements.
Prototype
Build a working prototype on real data and test it with actual users.
Evaluate
Measure accuracy, safety, latency and cost against an evaluation set and refine.
Productionize
Harden security, add guardrails, integrate with systems and deploy with monitoring.
Improve
Monitor usage and quality, gather feedback and expand to adjacent use cases.
The right stack for the job
- Instant, accurate answers from company knowledge
- Significant time saved on drafting, summarizing and searching
- Round-the-clock customer and employee support
- Data stays within your chosen cloud and security boundaries
- Freedom to switch models as the market evolves
- Measurable quality through continuous evaluation
Built for teams like yours
Internal Knowledge Assistant
Employees searching policies, SOPs, product documentation and past tickets in natural language.
Customer Support Chatbot
Answering common questions, tracking orders and escalating complex issues to human agents.
Document Processing
Extracting data from invoices, contracts, medical records and claims for downstream systems.
Sales & Marketing Copilot
Drafting proposals, emails and content grounded in product information and brand guidelines.
Developer Productivity
Internal tools for code explanation, test generation and documentation over private repositories.
Operations Agents
Agents that triage tickets, update CRM or ERP records and prepare reports with human review.
Yes, when designed properly. We use enterprise endpoints such as Azure OpenAI, Amazon Bedrock or Vertex AI, or self-hosted models, where your data is not used for model training, and apply access controls, encryption and PII redaction.
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Learn moreCustomer Support & BPO
Voice, chat, email and technical helpdesk support with SLA-driven workflows, trained agents and transparent reporting, available 24/7.
Learn moreReady to start with Generative AI?
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