Built right,
working from
day one.
No fragile demos. No science projects. A production system that lives inside the tools you already use.
Once the blueprint is signed off, strategy becomes a running system. We plan, build, test, and deploy AI that integrates cleanly into your stack - engineered for reliability, security, and real-world load. You get weekly demos and a working product, not a deck full of promises.
Production-grade AI,
not throwaway apps.
Many people can ship a custom app for a single internal problem with modern AI-assisted tools. Our focus is different: AI automations and AI-native software products that run in real production environments and serve multiple users, teams, or customers.
Generative AI
Augment or automate business processes by connecting applications, data sources, and AI systems.
Connect existing applications and apply intelligent behavior to workflows - combining two kinds of action. Primarily for more deterministic workflows: predefined rules, structured data pipelines, and repeatable business processes enhanced by AI.
Key technologies: Claude Routines, Mastra Workflows, n8n, RAG, LangGraph, Agno workflows, Pydantic-graph
Autonomous AI agents that understand goals, plan, execute with tools and connected systems, then monitor and adapt - operating within defined constraints to hit business objectives with minimal human intervention. Primarily for more probabilistic workflows.
Key technologies: Claude Managed Agents, Mastra / LangChain Stack, Custom Stack (Vercel AI SDK / Pydantic AI / Agno; Mem0; Temporal; LiteLLM; Evals, Docker; OpenTelemetry; Braintrust; E2B sandbox).
Narrow AI
Train models on your data to find patterns, make predictions, and automate decisions - extracting value from data into intelligent products and services.
Suggest relevant products, content, or actions by learning from user behavior and preferences to personalize experiences at scale.
Techniques: collaborative filtering, matrix factorization, content-based filtering, deep learning embeddings.
Identify suspicious transactions and anomalous behavior in real time, flagging threats before they cause financial or reputational damage.
Techniques: anomaly detection, isolation forests, gradient boosting (XGBoost), graph analysis, autoencoders.
Use historical data and machine learning to anticipate outcomes, uncover patterns, and support smarter, data-driven decisions.
Techniques: logistic/linear regression, random forests, gradient boosting, neural networks.
Project future trends in demand, revenue, or resources using time-series models, enabling better planning and allocation.
Techniques: ARIMA, exponential smoothing, Prophet, LSTM, temporal fusion transformers.
Extract meaning from images and video: detecting objects, recognizing patterns, and automating visual inspection and analysis.
Techniques: CNNs, YOLO, ResNet, Vision Transformers (ViT), segmentation models (U-Net).
Understand, generate, and analyze human language to power chatbots, search, sentiment analysis, and document processing.
Techniques: transformers (BERT, GPT), embeddings, named entity recognition, RAG.
AI-native apps
Software products where AI is a core part of the user experience and business value - modern software development combined with agentic engineering practices.
Frontend development builds intuitive, high-performance interfaces for web, mobile, and AR applications, delivering visually engaging, responsive, and accessible experiences across devices.
Key technologies: React, Next.js, Vue, Svelte, TypeScript, Tailwind CSS, React Native, iOS/Android.
Backend development creates and maintains the server-side systems that power applications: databases, messaging queues, ETL pipelines, and API integrations. It ensures secure, reliable data processing across scalable, maintainable architectures.
Key technologies: Node.js, Java/Spring, .NET, Ruby on Rails, Python, Go, PostgreSQL, MongoDB, Redis, Kafka, GraphQL/REST.
DevOps manages IT operations and automates infrastructure for reliable, scalable, secure systems, covering server administration, cloud platforms, monitoring, logging, and incident management. The goal is to streamline deployment and reduce downtime through continuous integration and automation.
Key technologies: Docker, Kubernetes, Terraform, AWS/Azure, CI/CD (GitHub Actions), Prometheus/Grafana.
Agentic engineering applies AI-native development workflows - spec-driven coding, test-driven evaluation, and iterative agent-based building - to ship production systems faster and with higher quality.
Key technologies: Dev.harness (Claude Code, Codex, OpenCode, PI), Dev.tools (Vercel OpenSrc, Context7).
Engineering, not
prompt-and-pray.
Spec-driven development run through our agentic-coding harness, with test-driven evaluation at every layer. Weekly demos, not nine-month sprints to nowhere.
Translate the blueprint into a concrete build plan - scope, data flows, integrations, and the success criteria we test against.
Embed the AI into your existing stack, auth, and data so it fits how work already happens - no parallel system to maintain.
Build fast, test in real workflows, then harden what works into a production-ready system. Spec-driven, run through our agentic-coding harness.
Access controls, monitoring, and guardrails so systems are safe, auditable, and dependable. Compliance signs off before we ship.
Improve accuracy, latency, and cost efficiency with regression-tested evals before anything rolls out broadly.
You see a working product every week. We write tests against agreed criteria and don’t ship until you sign off.
Work flows
through, hands-off.
The shape of nearly every automation we build: a trigger fires, the agent reads the context, decides, acts in your real systems, and reports back - with a human in the loop only where it counts.
A system you own,
end to end.
The working product, the source code, the docs - and a team trained to run it without us.
A purpose-built system, deployed
Engineered around your actual workflows, data, and tools - not a generic template or a wrapper around someone else’s product.
Full integration with your stack
Lives inside your existing tools, auth, and data. No rip-and-replace. Compliance and audit teams sign off before we ship.
Modular architecture
Every component is independently maintainable, updatable, and replaceable. Swap pieces as the AI landscape shifts - no vendor lock-in.
Acceptance tests you sign off on
Success criteria defined with you up front, tests written against them, and nothing ships until you sign. No hand-wavy deployments.
Handoff docs that let you take over
Architecture, runbooks, troubleshooting guides, and decision logs - detailed enough for your team to run it without us.
Source code + IP, in your name
Repos in your org, deployments in your cloud, models yours or licensed to you. You own everything end to end.
Repos in your org, deployments in your cloud, models in your name. We don't hold the keys.
Ship something
real.
Bring the blueprint - or start from scratch. Either way you'll have a working system in your hands in weeks, with demos every step.