Orchestra Pods
Validated Methodology
for product development
& ops automation.
Lean, AI-native delivery units. Senior engineers orchestrate purpose-built AI agents, each owning a role in spec, design, build, test, or review. The pod retains memory from sprint to sprint, so velocity and context compound over time.
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 human review at every step
Senior-only teams, no juniors learning on your dime
Lower cost per output than a traditional team
No need 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 used at every stage, from ideation to validation. Humans set acceptance criteria before AI touches anything, and senior engineers review every change.
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
Ticketing, QA, code review, and documentation run automatically. Time saved goes toward strategy, user experience testing, new feature development, and experimentation.
Production-Ready Starting Point
AI generates baseline code. Senior engineers refine and harden before it ships, triple checking for regulatory compliance.
Guardrails at Every Checkpoint
Our engineers use AI to work through edge cases before every release. Speed and safety are not a tradeoff.
Engineer Stories
What AI-native looks like, from our team’s perspective.
What does AI-native actually mean?
“I don’t write the code anymore. I run AI agents in parallel on architecture, implementation, and testing. My job is noticing when one drifts, and redirecting it.”
How much faster, really?
“Tickets I used to estimate at a week now take a day, reviews included. The extra time goes into testing and edge cases, not just more features.”
Doesn’t quality suffer?
“[Before AI], you had to choose between ideal and on-time. Now I can do things I never had time for: better error handling, more comfortable UX, covering edge cases.”
What about production risk?
“Before I touch anything, I have AI map the codebase, even features I built months ago and forgot. New work fits the system instead of breaking something downstream.”
Ops & Back Office Automation
Remove manual work
and free up time.
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.
Remedy builds automation systems that don't optimize workflows. They eliminate them.
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
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 in architecture, big data, and engineering. Led Trade & Treasury engineering at Citi, designed a 50 TB-a-week data system at Amazon, and holds a patent for Masterpass at Mastercard.
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.