AI Transformation
AI assistants and search built on your own documents, tickets and tools
Most companies have tried an AI demo. Few have one their people use every day, because the demo could not see the company's own documents, could not act in its tools, and nobody could say what it cost or who saw what.
We pick one workflow with you, connect the AI to the data and tools that workflow needs, add access control, logging and cost limits, and deploy it with monitoring. You get the running system, its code, and the runbooks to operate it.
What We Don't Do
- ✗Training large language models from scratch
- ✗Computer vision or image processing labs
- ✗Research-heavy ML experiments
- ✗Generic chatbot implementations
- ✗Proof-of-concept demos without production path
What We Build
- AI systems that integrate with your existing tools
- Production deployments with monitoring and fallbacks
- Cost-aware, provider-agnostic architectures
- Systems that your team will actually use daily
- Logs and an audit trail for every AI call
What We Deliver
What we build, with examples
Internal Copilots
Engineering, operations, support, and leadership assistants built around your data, tools, and workflows. Each one is connected only to the sources and tools it needs.
Examples
- •Engineering copilot with access to codebase, docs, and tickets
- •Operations assistant for infrastructure queries and runbooks
- •Support agent with product knowledge and customer history
- •Leadership dashboard assistant for metrics and insights
RAG-Based Knowledge Systems
Search and reasoning over your organizational knowledge. Documents, tickets, code, emails, and databases become queryable with natural language.
Examples
- •Semantic search across all company documentation
- •Code search with context understanding
- •Historical decision lookup from emails and tickets
- •Database schema and query assistance
Tool-Enabled AI Systems
AI that can act, not just answer. Trigger workflows, call services, query systems. MCP-style server and tool integrations for real automation.
Examples
- •Deploy and rollback commands through chat
- •Create tickets and update status
- •Query production metrics and logs
- •Manage cloud resources via natural language
Platform Integration
OpenAI, Claude, Gemini, Mistral, or open models. Provider-agnostic architecture means you are never locked in. Cost-aware routing and fallback strategies.
Examples
- •Multi-model architecture with automatic fallback
- •Cheaper models for simple requests, larger ones only where needed
- •A/B testing different models for quality
- •Self-hosted options for sensitive data
Security & Compliance
Role-based access control, data isolation, prompt and response traceability. Every interaction logged and auditable.
Examples
- •User and team-based permissions
- •Data access policies per department
- •Full audit trail of all AI interactions
- •Log retention set to match your policy
Production Deployment
Cloud or VPS deployment with observability, rate limits, fallbacks, and real-world reliability. Built to keep running when a model call fails or a provider is slow.
Examples
- •Kubernetes or container deployment
- •OpenTelemetry integration for observability
- •Rate limiting and cost controls
- •Disaster recovery and backup strategies
How We Work
Typical 1-3 month engagement timeline
1. Discovery & Architecture
- Map organizational knowledge sources
- Identify use cases and user workflows
- Select AI platform and tooling
- Design system architecture and data flow
2. RAG Pipeline & Integration
- Build document ingestion and chunking pipeline
- Set up vector database and embeddings
- Implement semantic search and retrieval
- Connect to internal tools and APIs
3. Tool Calling & MCP Integration
- Design and implement MCP servers
- Create tool schemas and handlers
- Build workflow automation logic
- Test tool reliability and error handling
4. Security & Access Control
- Implement authentication and authorization
- Set up data isolation and permissions
- Add audit logging and traceability
- Checks that each user sees only the data they are allowed to
5. Deployment & Handover
- Production deployment with observability
- Load testing and optimization
- Documentation and runbooks
- Team training and knowledge transfer
Engagement Structure
AI transformation engagements are scoped based on system complexity, data sources, integration requirements, and deployment targets.
What's Included
- Architecture design and RAG pipeline setup
- Tool integrations and MCP server configuration
- Production deployment with observability
- Complete documentation and handover
Larger initiatives are delivered through multiple phased engagements, each scoped independently. Tell us your scope: book a 20-minute call.
Ongoing support is handled separately under a production support agreement
Best Suited For
Teams that want one AI system in daily use, not another demo. To start, email io@deemwar.com with the workflow you want to change and where its data lives.