I help B2B SaaS companies, startup founders, and engineering teams architect resilient backend systems, integrate AI workflows, eliminate database bottlenecks, and modernize critical infrastructure — built to scale without expensive rewrites.
Multi-Tenant SaaS Architecture
Fail-closed data isolation
High-Scale Search & DSLs
<150ms p95 across 1M+ docs
Zero-Data-Loss Eventing
Kafka + Transactional Outbox
Database Performance Tuning
Up to ~60% latency reduction
Pragmatic AI & LLM Workflows
Tenant-safe RAG & agents
<150ms
p95 search latency over 1M+ multi-tenant records
~60%
Latency reduction on high-traffic production endpoints
99.9%
Production uptime across multi-tenant enterprise microservices
0
Cross-tenant data leaks and lost events by construction
Direct, client-focused engineering solutions for common architectural bottlenecks and scale challenges.
I analyze EXPLAIN ANALYZE query plans, design composite indexing strategies, tune connection pooling, and refactor heavy transactional bottlenecks — often cutting p95 response times by 50–60% without adding expensive infrastructure.
I integrate tenant-aware RAG pipelines, structured agent workflows, and vector search on top of your existing database so your users get contextual AI insights without compromising security or data boundaries.
I implement event-driven architectures with the Transactional Outbox pattern, idempotent consumers, and Dead Letter Queues (DLQ) to ensure every transaction and external webhook is processed exactly once with zero data loss.
I progressively decouple monolithic services, wrap critical business logic in automated integration tests using Testcontainers, and refactor risky endpoints without requiring a risky, expensive full-rewrite.
Specialized technical offerings tailored for B2B SaaS companies, startup founders, and engineering teams.
High-concurrency APIs, multi-tenant data isolation, and robust backend systems built to scale without rewrites.
Key Deliverables:
Embed LLMs, AI agents, RAG pipelines, and intelligent workflows directly into your existing SaaS products.
Key Deliverables:
Reliable event-driven pipelines, third-party platform integrations, and asynchronous messaging that never drop data.
Key Deliverables:
Diagnose slow queries, eliminate database bottlenecks, untangle legacy codebases, and stabilize fragile production systems.
Key Deliverables:
Real production systems, concrete trade-offs, and verified business outcomes — not just code snippets.
Enterprise SaaSProduction
2023 — PresentMulti-tenant equipment lifecycle & field operations SaaS platform
High-scale multi-tenant enterprise platform powering equipment tracking, automated warranty claims, inventory, and dynamic service lifecycles with sub-150ms search over 1M+ records.
Developer ToolsMaintained
2024Safe-by-construction enterprise query parser and search compiler
High-performance open-source query compiler that parses user search syntax into ASTs and generates parameter-safe, tenant-isolated queries across SQL and Elasticsearch backends.
BackendMaintained
2023Distributed data consistency & zero-data-loss event messaging architecture
Production reference implementation for guaranteed at-least-once message delivery between microservices — transactional outbox, polling relay, idempotent consumer deduplication, and dead-letter handling.
Developer ToolsMaintained
2024Production-ready schema-per-tenant Spring Boot architecture starter
Drop-in multi-tenant architecture starter providing automatic request-scoped tenant resolution, dynamic datasource routing, fail-closed security, and thread-safe async context propagation.
A structured, de-risked approach from initial problem discovery to production deployment and team handover.
Deep dive into your business goals, user pain points, system bottlenecks, and technical constraints. I audit your existing codebase, database query plans, and architecture to identify high-leverage opportunities.
Output: System Audit Report, Architecture Strategy, and Scope of Work with clear deliverables.
Define clean domain boundaries, data models, API contracts, and isolation guarantees before writing application code. Every key architectural trade-off is documented upfront.
Output: API Specifications, Schema Designs, and Migration / Implementation Plan.
Execute the backend implementation, AI integrations, or performance refactoring with rapid, iterative milestones. Clean, typed, maintainable code backed by continuous communication.
Output: Working, production-ready modules, PRs, and weekly staging deployments.
Verify system invariants under real-world conditions. Comprehensive integration testing against real containerized databases, failure-injection tests, and query latency validation.
Output: Automated test suites (JUnit, Testcontainers), benchmark reports, and security verification.
Seamless production deployment, CI/CD pipeline automation, observability setup, and detailed documentation so your in-house team can comfortably maintain and extend the system.
Output: Production deployment, Architecture Decision Records (ADRs), and team walkthrough session.
Technologies are supporting evidence for building reliable, performant, and maintainable systems.
Battle-tested, typed backend frameworks designed for high concurrency and maintainability.
Designing for resilience, domain-driven boundaries, and zero data loss under network partitions.
High-performance data storage, complex indexing, and tactical caching layers.
Asynchronous messaging pipelines built to survive broker failures and duplicate deliveries.
Embedding practical, tenant-safe intelligence and automation into existing platforms.
Modern containerized infrastructure with repeatable testing and deployment pipelines.
Modern, type-safe client dashboards and administrative interfaces that connect seamlessly with backends.
In-depth essays on distributed reliability, PostgreSQL optimization, and AI engineering discipline.
AI Engineering3 min read
A year of using AI agents on production backend code: where they genuinely compress work, where they quietly create debt, and the review discipline that makes the difference.
Architecture3 min read
How we replaced unindexed SQL filtering with a custom query DSL compiled to tenant-scoped Elasticsearch queries — the grammar, the AST, the trade-offs, and what I'd do differently.
Distributed Systems3 min read
Dual-writes between your database and Kafka will lose events — quietly, rarely, and at the worst time. Here's the outbox pattern as actually implemented, including the parts the diagrams skip.
Tell me about your product, your current bottlenecks, and the technical milestones you need to achieve. Let's build a reliable solution together.