Governed AI Execution
Transform human intent into reliable, long-running AI workflows.
Frumu builds governed AI execution systems that turn project tasks into validated work, equipped with observability, cost controls, and secure access patterns.
What We Build
A portfolio-focused snapshot of capabilities—framed around production readiness and integration realities.
Governed AI Execution Engines
We turn human intent and project tasks into reliable, long-running AI workflows.
- Bounded execution instead of brittle open-ended agents
- Orchestration of multi-model and multi-tool workflows
- Integration into your existing product and data flows
- Approvals and human checkpoints where needed
Issue-Driven Workflow Generation
Coding is a major surface, but not the only one. We build systems that parse tasks and validate work.
- Parse tickets, issues, and human intent automatically
- Generate targeted workflow bundles
- Repair-aware execution when steps fail
- Handoff validated artifacts, not just plausible chat output
Reusable Workflow Bundles
Stop babysitting chat wrappers. Workflows should be repeatable, inspectable, and trustworthy.
- Clear execution visibility and governed run state
- Reusable packs and presets for common operations
- Less human babysitting, more reliable execution
- Maintainable infrastructure for long-running automation
Validated RAG & Retrieval Systems
Retrieval-Augmented Generation orchestrated within a governed runtime for accuracy and trust.
- Document ingestion pipelines (chunking, metadata, versioning)
- Hybrid retrieval and reranking strategies
- Answer grounding with citations and traceability
- Evals and regression testing for output quality
Multimodal Media & Data Pipelines
Production pipelines for image, video, and text generation with queues, safety, and cost controls.
- Orchestrated generation with safety checks
- Job queues for throughput and reliability
- Cost controls, rate limits, and safe fallbacks
- Deployment automation and scaling backends
AI Backends & LLMOps
Secure backends to run AI features at scale: multi-tenant patterns, observability, and maintainable ops.
- Vector DB, cache layers, and background processing
- Admin panels, auth, and secure access patterns
- Monitoring, tracing, and failure analysis
- Tenant isolation and PII-safe data handling
Featured Work
Portfolio examples (experiments/prototypes) that demonstrate system design, integrations, and production thinking.
AImajin Mobile App
Problem: Ship AI image/video creation to mobile with real-time UX.
Solution: 6-month build: React Native + Django + WebSockets; Docker Swarm scaling; RevenueCat + Supabase; shipped on Google Play.
Tandem: Governed AI Execution
Problem: Teams need structured execution and artifact validation, not just open-ended chat assistants.
Solution: A governed workflow runtime that turns human intent into validated work, equipped with validations, repairs, and reusable workflow bundles.
Silicon Dreams
Problem: Slow external AI calls need a responsive UX and cost controls.
Solution: Async generation via Celery + Redis, durable state in Postgres, OAuth login, and token metering.
Pulse (Micro-Drama Platform)
Problem: Short-form vertical content needs smooth UX and structured series/episode organization.
Solution: Expo + Django + Celery + Channels with an AI Story Lab pipeline (bible → beats → scripts → exports).
Destination Research Engine
Problem: Research is slow and inconsistent; outputs must be structured.
Solution: Automated discovery + extraction into structured fields.
Editorial Pipeline
Problem: Consistent long-form output requires structure and review.
Solution: Multi-agent writing personas + iterative review loop + images.
Telegram RAG Assistant
Problem: Knowledge-heavy chat needs grounded answers + memory.
Solution: RAG + vector DB memory + multimodal features in Telegram.
How Delivery Works
A senior-engineer delivery loop: define acceptance criteria, integrate safely, test regressions, deploy, and handoff.
Discovery
Clarify users, data, constraints, and success metrics.
Integration plan
Architecture + security approach + acceptance criteria.
Build
Implement with clean interfaces and documented decisions.
Test / eval
Evals + regression tests for quality and safety.
Deploy
CI/CD, monitoring, runbooks, and rollback paths.
Handoff
Docs, runbooks, and knowledge transfer to your team.
Production Readiness
The parts most “AI demos” skip: reliability, monitoring, privacy/security, and cost controls.
Evals & regression tests
- Eval harness for quality
- Regression suite for changes
- Golden sets + failure analysis
Monitoring & tracing
- Request tracing across tools
- Error tracking and alerting
- Model/output drift signals
Cost & latency controls
- Token/rate limits
- Caching strategies
- Routing + fallback behavior
Security
- Tenant isolation patterns
- Audit logs
- PII-safe options and access controls
Want to run governed AI workflows for your team?
Book a free 30-minute call or email. We will reply with a scoped integration plan for your use case.
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