Engineering teams — part-time or full-time, monthly — who combine AI tools with senior dev expertise to build, ship, and operate systems that would normally take a team twice the size and three times the budget.
The best engineers cost $200K+, take months to find, and your competitors are bidding on the same people. Meanwhile, projects stall, backlogs grow, and the business waits.
AI handles the volume. Engineers handle the judgment. Together, a 4-person team delivers what used to take 12 — at a fraction of the cost, on a 24-hour cycle that never stops.
What happens when you pair AI tools with engineers who know how to use them — versus either one alone.
AI tools let a small team do the work of a large one. Our engineers know how to wield those tools. Your business gets faster timelines, lower costs, and systems that actually work in production.
Slow FDA submissions? Manual claims processing? Forecast errors costing millions? We start with what the business needs — not which AI framework is trending on Hacker News.
AI tools handle the grunt work — data pipelines, boilerplate code, document processing, pattern recognition. Our engineers handle the architecture, domain logic, integrations, and everything that requires judgment and context.
Your system goes to production in weeks, not quarters. Our Hanoi team monitors, improves, and extends it overnight. You wake up to performance reports, updated models, and new features ready for review.
We design and build AI systems that solve real business problems — demand forecasting, document processing, defect detection, alert triage, fraud detection. Not demos. Production systems with human oversight at every critical decision point.
Embedded dev pods that extend your team — not replace it. Senior engineers fluent in the AI/ML stack who write code, review AI output, build integrations, and ship features. 12-hour timezone advantage means your pipeline never stops.
AI doesn't ship and forget. We monitor model performance, catch drift, retrain on new data, and redeploy — overnight. Fine-tuning, inference pipelines, and production observability across Azure, AWS, and on-prem environments.
AI outputs need validation — whether it's code, regulatory documents, or patient data. We run security audits, compliance checks (FDA, HIPAA, SOC 2), data quality validation, and domain-specific QA across every AI-generated output.
Our engineers don't just use AI tools. They understand the architecture, failure modes, and best practices behind each one.
Code gen · Review · Agents
AI-native development
Inline AI completion
Inference · Training · Deploy
Observability · Drift · Evals
Agent orchestration
Experiment tracking
Managed model hosting
Open models · Fine-tuning
Model lifecycle mgmt
Streaming · Edge deploy
Container orchestration
More output per engineer when AI tools are used effectively
Average cost reduction vs. building the same team in-house
Average time from kickoff to production system
Continuous dev cycle — Irvine days, Hanoi nights
Hiring freezes, budget cuts, broken AI pilots. Here's what happened when these companies brought us in.
Irvine heart valve manufacturer with 3 devices stuck in 510(k) limbo. Regulatory team cut in half. Couldn't backfill — experienced RA specialists wanted $200K+ with 3-month notice periods. We built a RAG pipeline trained on 12 years of their approved submissions. Our overnight team reviews every AI-generated section so the US team wakes up to pre-validated drafts. Zero Refuse to Accept letters since deployment.
Irvine wireless chipmaker bleeding cash on silicon respins. 14,000 test cases and still shipping bugs. Couldn't hire more verification engineers — the 4 they needed would cost $1.2M/yr and take 6 months to onboard. We deployed RL agents that explore the design state space overnight. Found a race condition that would have bricked 100K units in the field. Zero respins since.
Newport Beach life insurer drowning in a 34-day claims backlog. Customer satisfaction at a 5-year low. Experienced adjusters demanding $95K+ and getting multiple offers. Tried outsourcing to a BPO — quality tanked so badly they pulled back in 6 months. We built AI claims pre-screening that handles document extraction, policy cross-referencing, and routing. Adjusters now handle 2.3x more cases each.
Irvine endpoint security firm monitoring 50M endpoints. Rule-based detection caught known threats but missed novel attacks. Adding new detection rules took 2-3 weeks — attackers innovated daily. After a customer called their response time "unacceptable," they needed a fundamentally different approach. Our AI correlation engine analyzes behavioral patterns across the entire fleet in real time.
3-hospital network losing $12K per diversion. Patients boarding 8+ hours in the ER. Two experienced charge nurses left for outpatient clinics. Budget for new beds: zero. We built admission prediction models that forecast volume 24-48 hours out using ER census, flu surveillance, and community event data. Bed management algorithms optimize discharge timing. Staff overtime dropped 25%.
We build, deploy, and monitor AI agents that plug into your existing ERP, CRM, and back-office systems — with human oversight at every critical junction.
Connect to SAP, Oracle, Salesforce, ServiceNow, or any API
Claude / GPT agent interprets, classifies, and routes tasks
Senior dev reviews agent decisions on high-value actions
Approved actions pushed back to ERP / CRM / database
Arize tracks accuracy, drift, and anomalies in real-time
Built a Claude-powered agent that reads incoming PO emails, extracts line items, validates against SAP MM master data, and creates purchase orders automatically — with human approval required for orders over $50K. Reduced manual data entry by 85% and cut PO cycle time from 3 days to 4 hours.
Deployed an AI agent that matches incoming invoices against Oracle ERP purchase orders, flags discrepancies, and auto-approves matches within tolerance thresholds. For a multi-subsidiary energy company processing 12,000+ invoices/month. Human reviewers only handle the 8% flagged as exceptions — down from 100% manual review.
Built an agent that ingests Salesforce leads, enriches them with firmographic data via API, scores using a fine-tuned model, and routes to the right sales rep — all within 90 seconds of lead creation. Human sales managers review AI scoring weekly via a dashboard, with W&B tracking model accuracy against closed-won outcomes.
Replaced a manual L1 triage process with a Claude-powered agent that reads ServiceNow tickets, classifies by category and urgency, suggests resolution from the knowledge base, and escalates to the right team. Handles 3,500+ tickets/week for a Fortune 500 telco. Human-in-the-loop reviews all P1/P2 escalations before routing.
Built an AI agent that pulls SAP S/4HANA sales history, combines with external market signals, and generates weekly demand forecasts per SKU per region. The agent auto-adjusts safety stock levels and flags anomalies. ML engineers set up Arize drift monitoring so when forecast accuracy drops below thresholds, human planners are alerted immediately.
Created a multi-system orchestration agent that triggers from Workday new-hire events and automatically provisions Active Directory accounts, assigns Okta SSO apps, creates Jira onboarding tickets, schedules orientation in Google Calendar, and orders equipment via ServiceNow — all with human HR approval gates for access-level decisions.
Every automation agent we build includes human approval gates, observability dashboards, and rollback capability. AI handles the volume — humans handle the judgment calls.
Discuss Your Automation Needs →Start with a part-time engineer reviewing your AI-generated code. Scale up to a full pod when you're ready. Cancel monthly.
A senior dev who reviews all AI-generated PRs, conducts security audits, and provides architecture guidance. Perfect for teams already using Claude or Copilot who need a human safety net.
An embedded pod (2–5 engineers) that owns your entire AI-augmented dev workflow — from prompting and code gen to testing, CI/CD, and production monitoring. Your team, extended.
We audited 500+ AI-generated pull requests. Here's where Claude, Copilot, and Cursor consistently fail — and the human review checklist that catches every flaw.
Read article →How we monitor model drift, hallucination rates, and inference latency in production AI apps — step by step.
Read article →AI agents are the copilots. Humans are still the pilots. Here's the framework that lets you ship 3x faster without the risk.
Read article →Everything you need to know about working with our AI-augmented dev teams.
Our engineers are fluent in Claude (Anthropic), Cursor, GitHub Copilot, and OpenAI APIs for code generation. For MLOps, we use Arize for observability, Weights & Biases for experiment tracking, MLflow for model lifecycle, and deploy on Azure AI, AWS Bedrock, or GCP Vertex depending on your stack. We also work extensively with LangChain/LangSmith for agent orchestration.
You get a dedicated senior engineer (~20 hours/week) who integrates with your GitHub/GitLab workflow. They review every AI-generated PR for security, architecture, and correctness. Billed monthly, cancel anytime. Most clients start here and scale up as they see results.
Absolutely. We help teams configure Cursor workspaces, write custom Claude system prompts for their codebase, set up Copilot enterprise policies, and build internal AI coding guidelines. We also train your existing devs on prompt engineering best practices for code generation.
Yes. Our ML engineers handle fine-tuning workflows on Azure ML, AWS SageMaker, or custom GPU infrastructure. We set up training pipelines, manage datasets, run evals, and deploy models to production with proper monitoring via Arize and W&B.
Two things: AI fluency and the human-in-the-loop model. Traditional shops write code from scratch. We leverage AI tools to move 3–5x faster, then apply senior human judgment for security, compliance, domain validation, and production readiness. Whether it's AI-generated code, ML model outputs, or automated document processing — our team validates everything before it ships. Our 12-hour timezone advantage means reviews, retraining, and monitoring happen overnight — you wake up to hardened, validated results.
Every PR goes through our security checklist: SAST scanning, secrets detection, OWASP Top 10 review, dependency auditing, and prompt injection testing for AI-facing code. We also set up automated security gates in your CI/CD pipeline so nothing ships without passing these checks.
Tell us what you're working on. We'll get back to you within one business day.