Engineering solutions to high-impact challenges.
We focus on outcome-driven software engineering. Here is how we helped startups and growing companies optimize infrastructure, implement robust AI systems, and scale their applications.
Scaling Conversational AI
Helping a leading conversational customer support startup handle massive chat throughput while reducing operating expenses.
The Challenge
The client's platform was handling 50k daily dialogues. However, direct API calls to OpenAI created sluggish user response times and high billing overhead.
The Constraints
The system had to maintain live streaming formats (Server-Sent Events) and could not drop any messages during high-traffic client spikes.
Our Approach & Architecture
We decoupled the heavy LLM logic from the main application thread using an asynchronous event pipeline:
- Implemented semantic caching via Redis to serve recurring queries instantly without calling the LLM.
- Introduced Sidekiq-backed job queues to process analytics and transcripts asynchronously.
- Optimized stream payload sizes, dropping connection setup times by 200ms.
Predictive Autoscaling
Designing a custom queue-based autoscaler to handle unpredictable task workloads and minimize cloud server idle time.
The Challenge
A transaction alert client had erratic notification bursts. Cloud instances were either sitting idle (wasting money) or scaling up too slowly (causing notification delays).
The Constraints
Standard CPU/Memory metrics-based scaling on AWS was too slow, taking 5+ minutes to spin up instances when sudden load arrived.
Our Approach & Architecture
We engineered a deterministic queue-depth monitoring script that bypasses standard hypervisor metrics:
- Implemented a custom daemon in Ruby that polls Redis/Sidekiq queue depth and growth velocity.
- Configured ECS capacity providers to scale containers based on *predicted arrival rates* rather than raw resource saturation.
- Optimized Docker container startup times from 90 seconds to under 15 seconds by slimming down base images.
Monolith Revitalization
Auditing and refactoring a legacy Rails application to unlock engineering velocity and cut database load.
The Challenge
A SaaS provider's 7-year-old Rails monolith was bogged down. Transactions locked up during peak hours, and slow local test suites delayed feature releases.
The Constraints
The system could not be taken offline, and rewriting it in a new language was ruled out due to cost.
Our Approach & Architecture
We audited the system and applied small, targeted engineering changes:
- Pruned hundreds of unused indices and set up logical PostgreSQL database partitioning on chronological tables.
- Eliminated major N+1 queries in the core APIs using strict preload policies.
- Refactored slow RSpec tests by mocking external HTTP endpoints and database calls.
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