Gen AI
We connect your knowledge bases, CRM and ERP through RAG pipelines, route each request to the model that suits it, and check every response before it reaches anyone.
OpenAI · Anthropic · Azure · Bedrock · Llama · Mistral
Architecture overview · 6 layers
Generative AI architecture
Six layers sit between your data and a response someone can act on. The guardrails layer is what makes the difference between a system you can put in front of customers and one you can only show colleagues.
Enterprise data & context
CRM, ERP, knowledge bases, support tools and event streams feed the pipeline with data that is actually current.
Embedding & vector store
Documents are split at boundaries that keep meaning intact, embedded, and re-indexed whenever the source changes.
RAG & prompt orchestration
The query is matched against the index, the most relevant passages are pulled, and a prompt is built around them.
LLM models & AI services
The assembled prompt goes to whichever model suits that task, judged on the accuracy it needs and what it costs to run.
Guardrails & safety
The answer is checked before anyone sees it. Anything failing a check is held back rather than passed along.
AI-powered outcomes
The validated answer reaches the user, or triggers the next step in a workflow, and the interaction is logged.
Key capabilities
What we build and run
Use case & strategy
We find the use cases worth building, with a feasibility check and ROI mapping against your data and compliance rules.
Data source integration
Pipelines wired into databases, APIs, CRM, ERP, support tools, content platforms and event streams, so models see current data.
Embedding & vector storage
Chunking, embedding and indexing into Pinecone, Weaviate, pgvector, ChromaDB or Milvus, with a re-index strategy that keeps answers current as your content moves.
Prompt engineering & RAG
Retrieval, context assembly and prompt templates on LangChain, LlamaIndex or Semantic Kernel, tuned on your own questions rather than a benchmark.
Model integration
OpenAI, Anthropic, Azure OpenAI, Bedrock, Gemini, Llama and Mistral behind one interface, so a model change is a config edit.
Guardrails & safety
Output validation, PII detection and hallucination checks, with an audit trail that shows why any given answer was allowed through.
Technology stack
Models, stores and orchestration
Multi-model routing keeps the architecture independent of any one of these. Swapping a provider is a configuration change.
Models
OpenAI
Anthropic
Azure OpenAI
AWS Bedrock
Google Gemini
Meta Llama
MistralVector stores
Pinecone
Weaviate
pgvector
ChromaDB
MilvusOrchestration
LangChain
LlamaIndex
Semantic KernelUse case · Insurance
Claims and policy analysis, read by a machine first
An insurance provider put claims processing and policy analysis behind a RAG pipeline with fine-tuned models, so assessors review exceptions instead of reading every file.
Read the case studies →Figures from a 2026 insurance engagement; methodology available on request.
Frequently asked questions
It depends on the use case, the accuracy you need, how sensitive the data is and what you are willing to spend. We build with multi-model routing, so the choice stays open as prices and capabilities move.
Related services
Pick one use case and we will pilot it
Tell us the task you would most like to hand over and where the data for it lives. We will come back with an approach and a realistic shape for the work.