Dedicated dev teams — part-time or full-time, monthly — who are expert in Claude, Cursor, Copilot, and the full modern AI stack. We review, harden, and ship what AI agents draft.
AI coding tools generate impressive code at speed. But without expert human oversight, that code accumulates silent risks that compound over time.
Our engineers don't replace AI tools — they supercharge them. Every line gets reviewed, hardened, tested, and monitored by developers fluent in the full AI/ML stack.
AI-generated code can look correct and still be dangerously wrong. Here's a real-world pattern our engineers catch every day.
We plug into your existing AI-powered workflow. Your agents write code, our humans make it safe, scalable, and shippable — on a continuous 24-hour cycle.
Your team (or ours) uses Claude, Cursor, Copilot, or any AI coding tool to generate features, refactor code, and build prototypes at speed.
Every AI-generated PR goes through security audit, architecture review, test coverage analysis, and performance profiling by senior devs fluent in the AI stack.
Hardened code gets deployed through proper CI/CD with monitoring via Arize, Datadog, or your observability stack. We set up guardrails so AI output stays safe.
Ideal for startups and lean teams using AI coding agents. Get a dedicated senior engineer (or a pod of 2–3) who reviews all AI-generated code, hardens it for production, and guides your architecture — without full-time overhead.
A fully embedded engineering pod that owns your AI-augmented development cycle end-to-end. From writing prompts for Claude to deploying on Azure, our team handles the full stack — with human judgment at every critical decision point.
Focused on testing and securing AI-generated codebases. SAST/DAST scans, OWASP compliance, prompt injection testing, model output validation, and regression suites — all from engineers who understand how AI tools think (and where they fail).
Need to fine-tune models, set up inference pipelines, or build observability into your AI products? Our ML engineers handle training workflows, model serving on Azure/AWS, and production monitoring with Arize, W&B, and MLflow.
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
of AI-generated code has at least one security flaw*
Timezone advantage — we review while you sleep
Continuous dev cycle with human checkpoints
Production incidents from unreviewed AI code
From fintech to manufacturing floors, our human-in-the-loop teams have hardened AI-generated code for production at scale.
A mid-market fintech used Copilot to build a real-time fraud scoring API. Our part-time team (2 engineers, 20 hrs/week) caught 23 critical vulnerabilities in the AI-generated code — including unencrypted PII in logs, missing rate limits on scoring endpoints, and a model inference pipeline with no drift monitoring. We added Arize observability, parameterized all queries, and deployed on Azure AI with proper key rotation.
An industrial equipment manufacturer used Claude to generate a predictive maintenance system analyzing sensor data from 200+ machines. Our full-time pod rewrote the AI-generated inference layer to handle edge cases Claude missed — null sensor readings, out-of-range values, and network timeouts. We added MLflow model versioning and Weights & Biases experiment tracking to ensure model accuracy over time.
A Tier 1 automotive supplier needed to build a connected vehicle telematics platform processing 50M events/day. Their engineers used Cursor and Claude for rapid prototyping. Our security QA pod caught API auth bypasses, unsanitized VIN inputs, and missing encryption on vehicle location data. We hardened the pipeline and set up LangSmith tracing for their AI-powered diagnostic chatbot.
A digital health startup built an AI-powered patient intake assistant using LangChain and Claude. Our part-time team found the AI-generated code was logging full patient conversations (including PHI) to unencrypted storage, had no prompt injection guardrails, and lacked audit trails. We rewrote the data layer, added PII redaction, built prompt injection testing suites, and deployed with Arize monitoring for hallucination detection.
A regional utility company used Copilot to build demand forecasting models for grid load balancing. The AI-generated training pipeline had data leakage issues, the inference API had no authentication, and model predictions were drifting with no alerting. Our ML ops team restructured the pipeline, added Arize for drift detection and model performance monitoring, and deployed on AWS Bedrock with proper IAM policies.
A mid-size telco built an AI customer service agent using Cursor and OpenAI APIs. The Cursor-generated code had hardcoded API keys, no conversation memory management, and was sending full customer account details to the LLM with no PII masking. Our full-time team rebuilt the agent orchestration with LangChain, added PII detection, implemented proper conversation windowing, and set up W&B for tracking response quality metrics.
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, architecture, and production readiness. Our 12-hour timezone advantage means reviews happen overnight — you wake up to hardened, shippable code.
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.
Get a dedicated dev team — part-time or full-time — that makes your AI-generated code production-ready. Start monthly, scale anytime.
Talk to a Dev Lead →