Vibe Code Repair
Refactor fragile prototypes into production-grade, typed architectures.
Database schema design, query cleanup, data modeling, migrations, performance planning, and architecture for systems that need reliable data instead of fragile tables.
Founders, operators, SaaS teams, agencies, and businesses with messy data, slow queries, disconnected tools, or unclear database structure.
Unmonitored scripts, random compute latency spikes, high memory bloat, and manual restart loops.
Typed invariants, sub-millisecond execution, persistent state machines, and bounded memory usage.
Your data lives across spreadsheets, apps, CRMs, and random exports.
The database schema was built quickly and now blocks new features.
Reports are slow, inaccurate, or require manual cleanup.
Nobody trusts the numbers because records are duplicated or inconsistent.
Your app slows down as more users, records, or workflows are added.
You want to add AI or analytics but the underlying data is not ready.
We implement modular, fault-tolerant subsystems engineered to survive traffic surges and complex operations.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Engineered with memory-safe invariants, defensive bounds checking, and end-to-end telemetry traces.
Primary Outcome: Cleaner data systems
Best Fit: Apps, dashboards, AI, reporting
Common Fix: Schema + query cleanup
Yes. We can audit the current structure and recommend whether to clean it up gradually or redesign key parts.
That depends on the app, data size, team workflow, hosting, and reporting needs. The architecture should follow the use case.
Yes. AI systems perform better when the underlying data is organized, searchable, and documented.
Schedule a confidential Architecture Strategy Session. We will audit your current system, map state boundaries, and deliver an exact execution roadmap.
Claim Your Architecture Audit →Local service catalog
General pages explain the system. The Local Intel catalog is where visitors can intentionally select a published market variation—without being redirected by IP location.
Verified revenue control planes tailored to the regulatory, market density, and unit economic constraints of active regional metropolitan markets.
Engineered revenue infrastructure for scaling operators in San Francisco County. Deploying sub-second routing and closed-loop attribution near Golden Gate Bridge.
Engineered revenue infrastructure for scaling operators in Dane County. Deploying sub-second routing and closed-loop attribution near Wisconsin State Capitol.
Engineered revenue infrastructure for scaling operators in Laramie County. Deploying sub-second routing and closed-loop attribution near Wyoming State Capitol.
Engineered revenue infrastructure for scaling operators in Hamilton County. Deploying sub-second routing and closed-loop attribution near Cincinnati Zoo.
Engineered revenue infrastructure for scaling operators in Minnehaha County. Deploying sub-second routing and closed-loop attribution near Falls Park.
Engineered revenue infrastructure for scaling operators in Anchorage Municipality. Deploying sub-second routing and closed-loop attribution near Tony Knowles Coastal Trail.
Autonomous workflows, vector intelligence, and memory-safe systems built to scale business operations without fragility.
Refactor fragile prototypes into production-grade, typed architectures.
Bespoke web portals, client dashboards, and automated intake engines.
Problem Addressed
Your team spends hours on manual data entry or copy-paste tasks.
HNSW vector indexes and multi-tenant collection clustering at scale.
Problem Addressed
Search results feel random even though embeddings are being stored.
Deterministic Tokio worker pools and event-driven control plane loops.
Problem Addressed
The orchestration logic is hidden inside prompts or scattered helpers.
Decompose bottlenecked monolithic services into rock-solid Rust binaries.
Problem Addressed
The prototype works but falls apart under concurrent usage.
Zero-copy deserialization engines sustaining 50,000+ RPS with flat p99s.
Problem Addressed
Retrieval logic is scattered across scripts, notebooks, and temporary endpoints.
Deterministic Architecture Discovery: Showing specialized subsystems suited to your active workflow context.