AI copilots that don't just chat. They do.
Dual-LLM orchestration. RAG with knowledge graphs. MCP action layers. Enterprise-grade copilots that execute real business operations — securely, accurately, and with a full audit trail.
Most enterprise "AI" is a chatbot with a logo.
A generic LLM behind a corporate skin. No system access. No actions. No audit trail. Stops being useful the moment the conversation needs to do something.
- Generic chatbots that can't access your systems
- Hallucinations with no source attribution
- Chat-only — no real actions executed
- No audit trail or compliance logging
- Full context from your docs, DBs, and APIs
- Cited answers with confidence scoring
- Real actions: refunds, tickets, CRM updates
- Complete audit logging and role-based access
Four pillars under the hood.
- 01
Dual-LLM orchestration
A conversational model handles dialogue while a separate action-command model executes operations. The chat layer can never directly invoke destructive actions — safety by architecture.
GPT-4o / Claude · Action router · Safety layer - 02
RAG + knowledge graph
Vector search (Pinecone / Weaviate) retrieves semantic chunks while a graph database (Neo4j) maps relationships between documents, people, and entities — full relational context, not just nearest neighbors.
Pinecone · Neo4j · Hybrid retrieval - 03
MCP / action layer
The copilot doesn't just answer — it acts. Process refunds, create tickets, update CRM records, provision environments. Every action requires auth confirmation and produces an audit trail.
MCP servers · OAuth / SSO · Audit logging - 04
Front-end wrapper
Enterprise SSO, role-based routing, session management, and a polished chat UI. Python orchestration ties everything together with streaming responses and conversation memory.
SSO / SAML · Python · Streaming UI
What a copilot looks like in production.
- 01Finance> Refund Sam Smith $10080% reduction in L1 support tickets
Advisors query portfolios, generate compliance reports, and execute operations through natural language.
- 02HR> What's our parental leave policy in Sweden?Instant answers with full legal context
Employees get policy answers grounded in the latest handbook revisions, with jurisdiction-specific context.
- 03IT Ops> Provision a staging environment for Project AtlasEnvironment ready in minutes, not days
Engineers provision infrastructure, check deployment status, and roll back releases through conversation.
- 04Legal> Find precedents for breach of fiduciary duty in Delaware89% faster legal research
Lawyers search across 10K+ documents with full citation chains and relationship context from the knowledge graph.
- 05Sales> Draft a proposal for Acme based on their last 3 interactions4x faster proposal drafting
Sales teams pull CRM context, generate proposals, and update deal stages without leaving the copilot.
How a copilot build actually runs.
- 012–4 weeks
Discovery & architecture
- Stakeholder interviews & workflow analysis
- Data source audit and integration mapping
- Architecture design document
- Security & compliance review
- Proof-of-concept demo
- 026–10 weeks
MVP build
- Dual-LLM pipeline implementation
- RAG + knowledge graph setup
- 3–5 core MCP skills
- SSO integration & role-based access
- Chat UI with streaming responses
- 034–8 weeks
Production hardening
- Load testing & performance optimization
- Hallucination guardrails & safety testing
- Full audit logging & compliance
- Monitoring & alerting dashboards
- User training & documentation
- 04Monthly
Ongoing retainer
- New MCP skill development
- Knowledge base expansion
- Model fine-tuning & prompt optimization
- Usage analytics & ROI reporting
- Priority support & SLA
What separates this from the field.
- 01
Graph + vector = full context.
Most RAG systems use vector search alone. We combine it with a knowledge graph so the copilot understands not just what documents say, but how they relate to each other.
- 02
Action, not just answers.
Our MCP action layer lets the copilot execute real operations — refunds, ticket creation, CRM updates — with proper auth and confirmation flows.
- 03
Dual-LLM safety.
Separating the conversational model from the command model means the chat layer can never directly invoke destructive actions. Safety by architecture.
- 04
No vendor lock-in.
Architecture is LLM-agnostic. Swap OpenAI for Anthropic, or Pinecone for Weaviate, without rewriting the copilot.
Ready to build a copilot that does the work?
Tell us about the workflows you'd want a copilot to actually run. We'll come back with whether it makes sense, what an MVP build looks like, and what the three-phase engagement would cost.