Orchestra Pods
The Validated Methodology
behind product development
and ops automation.
2-3 senior builders. The output of a full engineering team.
An Orchestra Pod is a lean, AI-native delivery unit. Two to three senior product engineers orchestrate a fleet of purpose-built AI agents, each owning a defined role across spec, design, build, test, and review.
The pod ships continuously, without the overhead of recruiting, ramping, or managing a large team. This is the delivery model powering both capabilities: accelerated product builds and ops automation.
AI models are selected per workload based on performance, and cost. The stack evolves as models improve. You are not locked into yesterday's tools.
What you get:
Faster delivery with compliance embedded from the start
Lower cost per output than a traditional engineering team
No bloated engineering staff to recruit, ramp, or manage
What Orchestra Pods build:
AI infrastructure and deployment
Data harmonization across fragmented systems
RAG systems and vector database development
Custom AI agents for your workflows
Computer vision applications
ML pipelines, model training, and inference
Big data processing and data lake architecture
AI-Accelerated Delivery
Faster delivery. No compliance shortcuts.
AI is embedded at every stage of how we build: ideation, requirements, prototyping, development, and validation. A structured review process runs at each checkpoint, validating outputs against your quality and compliance standards before work moves forward.
The result: timelines that compress from months to weeks, with HIPAA, PCI, and SOC 2 compliance built in, not bolted on.
Rapid
Prototyping
AI-assisted feature definition, mockup design, and code generation compress discovery and early build timelines from months to weeks.
Automated
Overhead
Story definitions, test data generation, code review, and documentation are handled automatically. Engineers and PMs spend time on strategic decisions, not admin.
Production-Ready Starting Point
AI generates baseline code. Senior engineers refine it to production-grade quality and regulatory compliance before it ships.
Guardrails at Every Checkpoint
A structured review framework validates AI outputs against your compliance requirements at each stage. Speed and safety are not a tradeoff.
Ops & Back Office Automation
The manual work
constraining your business,
retired.
Remedy builds automation systems that don't optimize workflows. They eliminate them.
One example: we replaced a client's 14-person offshore review team with a 5-stage AI-powered pipeline that was faster and more accurate than the human process it replaced.
Review cycle: from 45 minutes to real-time
Headcount: 14 FTEs replaced by 2 orchestrating engineers
Delivery model: 2 humans managing 10 specialized AI agents
Most back-office operations still run on manual work. Someone reviews reports. Someone triages alerts. Someone formats data and passes it along. It works until volume grows, the team turns over, or error rates start to compound.
The question we ask every client: “What's the one operation that, if automated tomorrow, would free up the most time?”
Built for Regulated Industries
Enterprise-grade.
Secure by design.
Capability
What it means
Enterprise Security
Zero data retention, encrypted environments, auditable workflows that meet banking and government standards
Guardrails
A structured framework that channels AI toward compliant, high-quality outputs ensuring consistency, accuracy, and control
AI Across the Stack
Embedded in ideation, requirements, prototyping, development, and validation
Human Refinement
Every AI output reviewed and refined by senior PMs, architects, and engineers
Compliance Coverage
SOC 2, HIPAA, PCI, GDPR, and SOX-aligned practices for regulated environments
Deployment Options
VPC, private cloud, and air-gapped environments for full data sovereignty
Leadership
A leadership team that has invested, built, and scaled before.
Igor Faybyshev
Big Data & AI R&D
23 years across architecture, big data, and engineering. Former Director of Engineering at Citi (Trade & Treasury), Senior Software Engineering Manager at Amazon, and Senior Director of Engineering at Mastercard. Patent holder for Masterpass.
Oleg Krook
Architecture & Engineering
25 years across software architecture, product development, and AI engineering. Former Director of Engineering at ClassPass, CTO of Block Six Analytics, and Engineering Manager at Amplify Education. Certified Scrum Master.
Ivan Yakovenko
AI/ML
23 years in engineering and AI. MS in Computer Science specializing in Artificial Neural Networks. Former Lead Engineer at Target (ML pipelines, anomaly detection, big data). Founder of Webcyte. Builder of LLM-based generative applications.