A Los Angeles–based engineering firm

Not just products. Whole systems.

We design, build, and run enterprise-grade systems, end to end.

Lifecycle

How we work with you.

You'll always know where things stand, what comes next, and why.

  1. Plan

    Phase 01

    The challenge is rarely the tech. It's understanding how your business runs and what it actually needs. So we start by interviewing leadership and the teams doing the work to learn how things get done, where they get stuck, your compliance obligations, and what you're already running. From there we build a roadmap you can act on, with us or without us.

  2. Design

    Phase 02

    This is where your business needs become a technical blueprint: architecture, tech stack, data model, integrations, security controls, and where AI belongs and where it doesn't.

  3. Build

    Phase 03

    Our engineers build it in short increments, with updates you can see and test every two weeks. You'll have access to the code repository from the first day of the build.

  4. Launch

    Phase 04

    We handle migration, integrations, rollout, training, and go-live support. When we're replacing an existing system, the cutover runs incrementally and in parallel, with automatic rollback, so your business never goes dark. Nothing goes live until it's verified against the plan, cleared against your compliance requirements, and signed off by stakeholders.

  5. Run

    Phase 05

    Two ways to finish. We hand the system over to your team, fully documented, or we keep running it as a managed platform: hosted, monitored, patched, supported, and covered by an SLA, for one predictable monthly fee.

AI-native since 2024

While our human engineers own system architecture and every material decision, we use AI to write much of the routine code. Every change is gated by engineer review and automated tests before it ships. For you, that means cleaner code, thorough documentation, and working software in about half the usual time, from a team that knows how to harness AI.

Engagements

From advice to fully managed.

PlanDesignBuildLaunchRun

Strategy & Advisory

Most engagements start here. We assess strategy and architecture so you get an honest answer before committing to a build, and provide interim technology leadership if needed. Already have a systems plan, or need one specific product or AI integration? Skip ahead.

Typical length: One to three months

  1. 01 Interim and fractional CTO: operating model, hiring plan, release process, and engineering SOPs
  2. 02 Technology and systems roadmaps, architecture and code reviews
  3. 03 Compliance and data-handling reviews
  4. 04 Build-vs-adopt assessments and vendor selection
  5. 05 AI readiness assessments, AI use policies, and governance frameworks

Design & Build

New systems and legacy replacements, designed, built, delivered to production, and handed off to your in-house team.

Typical length: Four to nine months

  1. 01 AI-native architecture and system design
  2. 02 Built by our engineers and held to strict code quality standards, with senior architecture oversight throughout
  3. 03 Integration with your existing tools and data
  4. 04 Full knowledge transfer to your internal team, with documentation, runbooks, and training
  5. 05 Zero-downtime migrations from legacy systems, run in parallel and fully reversible
  6. 06 Coordinated reviews with your compliance, legal, and leadership teams before launch
  7. 07 Launch support and stabilization, with the option to have us run it long term

Applied AI

Integrate AI safely into the systems and workflows you already run: governed, auditable, and measurable.

Typical length: Two to four months

  1. 01 LLM and agent integration into existing workflows and platforms
  2. 02 AI data gateways (MCP) that give your team authenticated, PII-safe access to company data through AI assistants
  3. 03 Document processing, fraud and anomaly detection, intelligent routing, AI-assisted reporting
  4. 04 Guardrails, evaluation, and human-in-the-loop review by design
  5. 05 Data boundaries and audit trails that satisfy regulated-industry requirements
  6. 06 AI use policies and governance frameworks

Managed Platforms

We build the system your business needs, then host, maintain, and support it for one monthly fee.

Typical length: Five years

  1. 01 A full build engagement, then ongoing operation instead of a handoff
  2. 02 Hosted, monitored, patched, and upgraded by us, under a defined SLA
  3. 03 Security and compliance controls included and kept current
  4. 04 No infrastructure to own, no engineers to hire, and no on-call rotation, all at a fraction of in-house cost

Systems

Some of what we’ve built.

012026

Business Messaging Agents

White-label iMessage, SMS, and WhatsApp agents on top of any business API

Private beta

A multi-tenant messaging platform that puts an AI agent in front of a business's own systems. Customers text the business over SMS, RCS, iMessage, or WhatsApp, and the agent answers by calling the business's API, with every request signed to RFC 9421. Input and output guardrails check every turn, everything lands in a full audit log, and when a person needs to step in, the conversation hands off to Zendesk, Salesforce, or Intercom. Designed for HIPAA-regulated workloads.

TypeScriptNestJSBullMQPostgresRedisS3MCP
022026

Video Extraction

Structured extraction for video

Private beta

A video and a schema in, typed JSON out. Transcription, scene detection, frame dedup, vision, and a temporal knowledge graph, with every value pointing back to the second and the modality it came from. Exposed as an API, an MCP server for agents, and apps.

TypeScriptCloudflare WorkersD1PythonMCP
032026

Document AI Extraction

Four modules, one metric at the end

In production

Turns an unstructured document into a metric an investment team can act on. The parsing lane reads whatever the file actually holds, text and tables but also charts, images, audio and video, and lands it with a trace of every step. A multi-agent extraction lane runs one versioned agent per metric behind an eval gate, writing results into a staging layer. A desktop cockpit lets the owner of a document correct its metadata. A web app is where a human confirms before anything promotes. Telemetry on every agent run, and nothing promotes on its own.

SnowflakeCortex AgentsPythonReactElectronSPCS

Client system. The names, the internals, and the data stay with the client.

042026

Macro Economics Research Agents

Multi-agent system with security-level access and parallelization

Shipped

Fixed income and currencies research, searchable in one place. A named agent object owns routing and search strategy, the backend executes its tools, and a citation composer links every claim back to the report and the page it came from. One agent run resolves a conversation turn. Runs as a container service beside the warehouse, with a desktop client and trace-level observability over every run.

PythonFlaskNext.jsCortex AgentsSPCSElectron

Client system. The names, the internals, and the data stay with the client.

052025

Contract management system

Contract lifecycle out of the inbox

In production

Contract records with verified status, period selection, and an in-app PDF viewer, plus a sync that pushes contract metadata into the data catalog. Replaced a process running on shared spreadsheets and email threads.

PythonReactSnowflakeAtlan

Client system. The names, the internals, and the data stay with the client.

Contact

Start with a conversation.

Thirty minutes on your systems, constraints and timeline. You leave with a clear next step.

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