Portfolio: production AI integration
Production-grade AI systems for startups and SMBs—integrated into your product and workflows.
Built with measurable acceptance criteria: evaluation, observability, cost/latency controls, and secure access patterns.
What We Build
A portfolio-focused snapshot of capabilities—framed around production readiness and integration realities.
RAG & Retrieval Systems
Retrieval-Augmented Generation that stays grounded: ingestion, hybrid search, reranking, and citations.
- Milvus / Qdrant / Chroma setup and operations
- Document ingestion pipelines (chunking, metadata, versioning)
- Hybrid retrieval + reranking strategies
- Answer grounding with citations and traceability
Conversational AI Assistants
Chat experiences that integrate into your product and tools (not a demo chatbot that breaks in production).
- Custom knowledge integration + conversation memory
- Tool calling with allowlists and safe fallbacks
- Tone/persona configuration for your brand
- JSON/REST APIs + Telegram/Discord interfaces
Multimodal Media Pipelines
Production pipelines for image/video generation and editing with queues, safety, and cost controls.
- Flux / WAN / Kling style pipelines with safety checks
- ComfyUI / ControlNet workflows and orchestration
- Upscalers + post-processing
- Job queues for throughput and reliability
AI Backends & LLMOps
Secure backends to run AI features at scale: multi-tenant patterns, observability, and maintainable ops.
- Django / FastAPI backends with Celery task queues
- Vector DB + cache layers + background processing
- Admin panels, auth, and secure access patterns
- Monitoring/tracing and failure analysis
Workflow Automation (n8n)
Automations that turn messy inputs into structured outputs and keep systems in sync (CRM, Sheets, email).
- Playwright scraping and data capture
- LLM enrichment (OpenAI/Gemini) with validation
- Email + Sheets + CRM synchronization
- Human-in-the-loop review steps where needed
Infrastructure & DevOps
Cost-aware deployment for real services: Dockerized systems, reproducible infra, and scaling backends.
- Ansible playbooks for deployment and server provisioning
- Hetzner deployments (cost/perf tradeoffs)
- Docker Swarm for scaling backend services
- GPU routing (RunPod / Replicate) + monitoring
Mobile + Product Delivery
We ship full products—not just models—across mobile, backend, and integrations.
- React Native mobile apps (real-time UX via WebSockets)
- Django backend with queues and async job processing
- RevenueCat subscriptions + Supabase integration
- Store deployment workflows (Google Play)
AI Proof-of-Concepts
Rapid prototyping with technical validation so you can make decisions with evidence, not vibes.
- Feasibility tests and MVP builds
- Clear technical validation notes (limitations + risks)
- Demo-ready prototypes for internal buy-in
- Path to production: backlog + acceptance criteria
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 Desktop AI Workspace
Problem: Non-developers need AI help on local files without SaaS exposure.
Solution: Local-first desktop AI with plan mode, autonomous loops, and zero-trust permissions. BYOK providers or local models.
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 ship an AI feature that holds up in production?
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