You vibe-coded it. We make it production-ready.
You built a prototype. It works on your laptop. Then 10 users hit it and the database falls over. The agent hallucinates. The pipeline backs up. The CEO asks when it'll be ready and you realize your "MVP" needs to be rebuilt from scratch. That's where we come in. We take scrappy AI prototypes and engineer them into production systems that handle real traffic — ClickHouse backends doing 200K events/sec at 12ms P99, RAG pipelines with 99.9% retrieval accuracy, Kubernetes infrastructure that auto-scales without paging anyone at 3 AM. Not by rewriting everything. By fixing the architecture: sharding keys, compression codecs, query patterns, deployment pipelines, monitoring, and runbooks. We're AI product engineers, not just consultants. We ship working infrastructure, hand over the runbook, and your team takes it from there. Every engagement starts with a free technical audit: we baseline your architecture, map your infrastructure spend to actual workloads, and give you a roadmap with specific, measurable targets — not generic recommendations.
12ms
P99 Latency
200K req/s
Throughput
Real-world metrics from USA enterprise infrastructure.
Powering Next-Gen Infrastructure
We don't ship features. We ship measurable results.
How do you measure infrastructure consulting ROI before you write a check? You can't. That's exactly why every engagement starts with a baseline audit. We measure P50, P95, and P99 query latency. We map your infrastructure spend to actual workloads and query patterns. We profile the top 10 most expensive queries running in production right now. Only then do we set targets: cut latency 10x, reduce cloud waste 35%, compress a migration from quarters to weeks, or improve deployment frequency 5x. Our track record includes migrating a $47K/month Snowflake pipeline to ClickHouse at $8.2K — 82% reduction, verified. Building a 200K events/sec real-time analytics platform with 12ms P99 latency. Deploying enterprise RAG systems that lifted support resolution rates 45% in the first quarter. Rewriting a Node.js gateway in Go that eliminated 800ms GC pause spikes and now handles 18K RPS on a single instance. Every project ends with a documented before-and-after comparison — specific numbers, not anecdotes.
Before: Real-time dashboards were unusable. Queries timed out.
After: Sub-second analytics on 200M events/day. Teams ship dashboards, not workarounds.
Before: Node.js gateway caused intermittent timeouts under load.
After: Rewrote in Go. One instance handles 18K RPS. Zero GC pauses.
Before: Over-provisioned infrastructure with no observability.
After: Right-sized clusters with auto-scaling. Saved $240K/yr on compute.
Before: Customer support couldn't find answers. Escalation rates climbed.
After: Production RAG pipeline with multi-stage verification. 45% support lift.
Core Disciplines
What sets infrastructure that survives production apart from infrastructure that burns budget? Real deployment experience. Not certification courses, not blog posts — experience debugging ClickHouse merge storms at 2 AM, tuning Kafka consumer lag under 200K events/sec sustained load, and migrating petabyte-scale data warehouses without a minute of downtime. We've optimized RAG pipelines for sub-100ms response times at 99.9% retrieval accuracy across millions of documents. Every engagement draws from patterns forged in real incidents across fintech, analytics, and AI workloads — adapted to your specific data shapes, query patterns, and scale requirements.
AI Product Engineering
You used Cursor, Bolt, or Replit to build a prototype. The demo was great. Now it needs to handle real users, real data, and real scale. That's the hard part. We take vibe-coded products and engineer them into production systems — proper auth, rate limiting, caching, database schema design, deployment pipelines, monitoring, and cost controls. React frontends that render sub-second dashboards. Go APIs handling 18K RPS with zero GC pauses. ClickHouse backends at 12ms P99. Kubernetes that auto-scales without paging anyone. We own the full stack from system design through runbook handoff. Your startup gets infrastructure depth without the hiring process. Your enterprise gets a modern platform your team can actually operate.
Data Infrastructure
What happens when PostgreSQL queries hit 47 seconds and dashboards keep timing out during customer demos? You need infrastructure designed for analytics at scale, not bolted on. We design and operate ClickHouse clusters and Kafka streaming pipelines that handle millions of events per second. Our expertise covers MergeTree schema design with column-specific codecs that cut storage 40-60%, sharding strategies that balance write throughput with query performance, and data retention policies with automated TTL tiering from NVMe to HDD to object storage. We've migrated from Redshift, Snowflake, and PostgreSQL at petabyte scale — each with a migration playbook that minimizes downtime and validates performance before cutover.
Production RAG Systems
Why do most RAG systems fail within a month of deployment? Because vector similarity search alone is not a retrieval strategy — it's just the starting point. We build RAG pipelines that combine ClickHouse as a vector store with multi-stage retrieval, cross-encoder re-ranking, query rewriting, and content safety guardrails. Our systems maintain sub-100ms query latency at 99.9% retrieval accuracy across millions of documents under production load. We handle chunking strategies that preserve semantic boundaries, embedding pipeline monitoring with drift detection, hybrid search blending vector and keyword retrieval, and feedback loops that continuously improve result quality.
MLOps & AI Infra
How do you deploy LLMs in production without breaking your budget or your on-call rotation? We build Kubernetes-native infrastructure for AI workloads from training through inference serving. Our MLOps pipelines handle model versioning, A/B testing with traffic splitting, automated rollbacks on performance degradation, and GPU autoscaling that matches allocation to actual request load. We manage model serving with vLLM and TensorRT for throughput, and implement monitoring that catches data drift, embedding degradation, and cost anomalies before they affect users. For teams deploying RAG or agentic systems, we provide the infrastructure layer that makes AI reliable: end-to-end observability, semantic caching, rate limiting, and per-query cost tracking.
We don't just consult. We accelerate with production-grade AI.
What does production-grade AI infrastructure look like under real traffic, not in a slide deck? A ClickHouse cluster returning 12ms P99 queries on 200 million daily events with 99.999% uptime. A RAG pipeline serving millions of documents with 99.9% retrieval accuracy at sub-100ms response times under concurrent load. A Kafka streaming platform ingesting 200K events per second without a single dropped message, even during 10x traffic spikes. Our team has designed, built, and operated these exact systems for USA startups and enterprises across fintech, real-time analytics, customer support AI, and data platform modernization. We combine deep engineering with patterns forged through real production incidents — not vendor documentation. Every deployment ships with monitoring dashboards, operational runbooks, and granular cost tracking by query. We deliver enterprise reliability at startup velocity because we've already made the expensive mistakes that would slow your team down.
Faster Migrations
What if your database migration from Snowflake, Redshift, or PostgreSQL to ClickHouse took weeks instead of quarters, with zero downtime and verified cost savings? That's what our automated migration pipeline delivers. We built internal tooling that handles schema conversion with data type mapping, partition strategy recommendations based on your actual query patterns, and data validation that compares row counts and checksums between source and target. Our benchmarking framework runs your production queries against both systems before and after migration, producing a documented comparison showing exactly which queries improved and by how much. We've used this pipeline to migrate petabyte-scale datasets with zero downtime and documented cost reductions of 50-80%. Repetitive work is automated; human expertise is reserved for the edge cases.
Smarter Optimization
How do you optimize a ClickHouse cluster without guessing which knob to turn? You profile it first. Our methodology combines query profiling with ClickHouse's system tables and flame graphs, architecture analysis across ingestion, storage, and query layers, and cost modeling that maps infrastructure spend to workloads. This pinpoints exactly where performance and budget are leaking. Then we apply targeted fixes: materialized views for expensive aggregations, column-specific codecs (DoubleDelta, T64, ZSTD) that reduce storage 40-60% without impacting query speed, partitioning and TTL strategies that tier cold data to cheaper storage automatically, and ordering key adjustments aligned with your most frequent query patterns. Every optimization is benchmarked with your actual queries before and after — a documented comparison of latency, throughput, and cost per query.
Consistent Quality
What does repeatable infrastructure look like on Monday morning when a new engineer needs to understand the system? Every deployment artifact is defined as code, reviewed through pull requests with automated checks, and tested in staging against production-like data before touching live environments. We enforce consistent patterns across Kubernetes manifests with Helm, Terraform configurations with modular components, ClickHouse schemas with version-controlled migrations, and RAG pipeline logic with tested retrieval configurations. Every environment from dev through staging to production is reproducible, and every change has a clear audit trail. Canary deployments catch regressions before full rollout. Post-deployment validation confirms system health. No tribal knowledge required — the code tells you how it works.
$47K → $8.2K
Monthly Infrastructure Cost
Snowflake to ClickHouse migration. 82% reduction, verified.
12ms P99
Query Latency
Real-time analytics pipeline handling 200K events/second.
99.9%
RAG Retrieval Accuracy
Production RAG system at 1M+ documents, sub-100ms responses.
We build products that scale—from the first line of code to the last query.
Full-Stack Product Engineering
What separates a data-intensive product users actually love from one that generates constant support tickets? It's rarely about individual features. It's about how well frontend, backend, and infrastructure integrate under real load. Users notice when a dashboard takes 3 seconds to load a chart. They notice when search returns stale results. They notice when the app goes down during peak hours. We build AI-native products where every layer is optimized for its role: React frontends that render complex dashboards under 200ms with optimistic updates, Go APIs handling 18K RPS on a single instance with zero GC pauses, ClickHouse backends returning 12ms P99 queries at billions of rows, and Kubernetes infrastructure that auto-scales on request load rather than crude CPU thresholds. Your product's success depends on UX and scalability working together. That's what we engineer.
Product Engineering for AI Systems, Product Engineering for Data Intensive Systems
Data Platform Modernization
How much of your engineering team's capacity is spent firefighting infrastructure instead of shipping product? In our experience, most teams lose 20-30% of capacity to unplanned operational debt — databases that can't handle load without manual intervention, pipelines that break silently at midnight, queries that time out during demos, and configuration only one person understands because it was set up under deadline pressure and never documented. We replace legacy data warehouses designed for batch reporting with architectures built for real-time analytics and AI. That means migrating from PostgreSQL, Redshift, or Snowflake to ClickHouse with measured performance improvements of 10x to 100x on common workloads. Setting up Kafka with proper partitioning and consumer lag monitoring for reliable streaming. Implementing TTL policies that tier data across storage classes, reducing costs 40-60% without impacting query performance.
Replace Legacy Data Warehouse Consulting, Reduce Data Latency in Production Systems
Trusted by USA startups and enterprises
0%
Performance improvements in 30 days
3x Faster
AI deployment cycles
Zero
Downtime across migrations
0-80%
Infrastructure cost reduction
Ready to scale your AI infrastructure?
Ready to stop firefighting infrastructure and start shipping product? We help USA startups and enterprises build data infrastructure that actually works under load — ClickHouse clusters handling 200K events per second at 12ms P99 latency, production RAG systems serving millions of documents with 99.9% retrieval accuracy, Kafka platforms ingesting terabytes daily without data loss. Every engagement begins with a free technical audit: we analyze your current architecture, review query patterns and infrastructure configuration, identify the specific bottlenecks costing you time and money, and deliver a written roadmap with measurable performance and cost targets — a prioritized action plan customized to your stack, team, and business constraints. Whether you need a full platform migration, deep query optimization, production AI infrastructure, or engineering capacity for a critical project — we deliver systems your team can operate confidently.
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