Services
Production AI automation systems for business operations.
Frumu designs, builds, deploys, and operates governed AI workflows, internal tools, RAG systems, multimodal workflows, integrations, and runtime infrastructure for teams that need AI to do real work reliably.
Start with one high-value workflow.
Most Frumu projects begin with one repeated process that already costs time, creates bottlenecks, or depends on manual research, coordination, or content generation. We map the workflow, identify the tools and data involved, build a working prototype, and define what it would take to run it reliably in production.
AI Automation Discovery
Map one workflow, identify automation points, risks, data sources, tools, and production requirements.
Production Prototype
Build a working internal AI tool or workflow with real inputs, real outputs, and reviewable artifacts.
Managed AI Automation Runtime
Deploy and operate a governed automation system with monitoring, integrations, evals, and ongoing improvements.
Multimodal Workflow Prototype
Design and build AI-assisted image, video, or creative generation workflows with prompt systems, review loops, and reusable pipelines.
What we build
These services can be delivered as a focused implementation, a full production system, or ongoing AI automation capacity.
Governed AI Workflows
Turn repeated business processes into structured AI workflows with clear steps, tool access, validation, and handoff points.
- Long-running workflow execution
- Human review points where needed
- Structured outputs and artifacts
- Retry and repair paths
Custom AI Automation Tools
Build internal AI tools tailored to your company’s workflows, data, teams, and operational constraints.
- Research and reporting tools
- Operations automation
- Sales and marketing intelligence
- Support and knowledge workflows
AI Runtime and Infrastructure
Deploy the runtime layer needed to operate AI systems reliably, not just prompt them manually.
- Job queues and background execution
- Model routing and fallback behavior
- Cost and latency controls
- Monitoring, logs, and audit trails
RAG and Knowledge Systems
Create grounded AI systems that can search, retrieve, cite, and reason over company knowledge.
- Document ingestion
- Hybrid search and retrieval
- Citations and traceability
- Evals and regression testing
Multimodal AI Workflows
Build systems for AI-assisted image, video, and creative generation workflows with prompt orchestration, review loops, and reusable pipelines.
- Image and video generation pipelines
- Prompt generation and refinement
- Reviewable media outputs
- Reusable creative workflow systems
Tool and Data Integrations
Connect AI workflows to APIs, internal tools, Slack, Discord, Telegram, GitHub, Notion, Google Workspace, and custom systems.
- APIs and internal systems
- Slack, Discord, Telegram, GitHub, Notion, Google Workspace, and custom tools
- Secure tool access
- Permission-aware automation
Common starting points
The best projects usually begin with one repeated workflow, one knowledge bottleneck, or one operational process that already has clear value.
Operations Automation
Automate recurring internal processes while keeping visibility, review, and control.
- Daily research briefs
- Competitor monitoring
- Internal reporting
- Lead enrichment
- Document processing
AI Research and Intelligence Systems
Build workflows that gather information, verify sources, structure findings, and produce useful reports.
- Market research
- Sales intelligence
- Technical research
- Due diligence workflows
- Trend monitoring
Internal Knowledge Assistants
Create AI systems that can answer questions from company knowledge with citations and traceability.
- Team knowledge bases
- Customer support knowledge
- Onboarding assistants
- Policy and compliance search
- Engineering documentation assistants
Agentic Workflow Systems
Move beyond single chatbots into systems where AI can plan, execute, inspect, and hand off work through a governed runtime.
- Multi-step task execution
- Tool-using agents
- Approval-based workflows
- Artifact generation
- Human-in-the-loop automation
Built around operational trust
Frumu’s work is shaped around the parts that decide whether AI stays a demo or becomes dependable infrastructure.
Reliability
Retries, fallbacks, state tracking, and recoverable execution.
Observability
Logs, traces, run history, and visibility into what happened.
Security
Scoped tool access, permission boundaries, data handling, and audit trails.
Evals
Regression tests, quality checks, and known-good examples.
Cost Control
Token limits, caching, model routing, and usage visibility.
Human Control
Approval points, review flows, and clear responsibility for sensitive actions.