Custom LLM Integration Agency for Enterprise Products
RoboSoft Works builds production LLM integrations — RAG pipelines grounded in your own data, custom agents, and provider-agnostic architecture for GPT, Claude, and Gemini — engineered in 14-day sprints with full code ownership.
- Problem: Generic chatbot wrappers hallucinate, lack grounding in real data, and lock you into a single model provider.
- Solution: 14-day sprint delivering a grounded RAG pipeline, evaluation harness, and provider-abstraction layer across GPT, Claude, and Gemini.
- Security: Private/VPC model deployment, PII redaction, zero-data-retention API tiers, and full audit trails for regulated industries.
- Technology: LangChain, LangGraph, LlamaIndex, vector databases (Pinecone, Weaviate, pgvector), custom evaluation harnesses.
- Model Coverage: OpenAI GPT, Anthropic Claude, Google Gemini, and open-weight models (Llama, Mistral) through one abstraction layer.
Frequently Asked Questions
Which LLM should we use — GPT, Claude, or Gemini?
It depends on the task, not brand loyalty. We benchmark your actual use case against the candidate models using your own evaluation set and recommend (or route between) the one(s) that perform best on accuracy, latency, and cost — then build a provider-abstraction layer so switching later is a config change, not a rewrite.
How do you prevent hallucinations and keep answers grounded?
We build a retrieval layer (RAG) that grounds every answer in your actual documents or data, add citation/source tracking so answers are verifiable, and run continuous evaluation against a labeled test set so regressions are caught before they reach users.
Can our customer data stay private during LLM integration?
Yes. We deploy via private/VPC model endpoints or zero-data-retention API tiers where available, redact PII before it reaches the model, and keep full audit trails on every prompt and response for compliance review.