Engineering & design
for the AI era.
From agentic systems to user research, we help organizations build software that is intelligent, reliable, and designed for real people.
Agentic Engineering
Coding agents are changing how software gets built. We help engineering teams adopt them effectively, not as novelty, but as core workflow infrastructure.
AI-powered coding assistants and autonomous development agents can dramatically accelerate how teams write, review, and ship code. But adopting them well requires more than installing a plugin. It means rethinking how tasks are scoped, how code review works, and how quality gates hold up when machines are contributing alongside humans.
We work with engineering organizations to evaluate, integrate, and operationalize agentic development tools. This includes setting up guardrails, establishing evaluation practices, and training teams to work effectively with AI pair programmers, so productivity gains are real and sustainable.
Tool evaluation & adoption
Assess which agentic tools fit your stack, team size, and security requirements.
Workflow integration
Redesign development workflows around agent-assisted coding, review, and testing.
Developer enablement
Train teams to prompt effectively, validate agent output, and maintain code quality.
Guardrails & governance
Establish safety practices, observability, and compliance for agent-generated code.
Agentic Applications
We design and build AI agents that reason, plan, and execute tasks autonomously, moving beyond chatbots into systems that actually do work.
Agentic applications are software systems where AI doesn't just respond, it acts. These are multi-step, goal-directed systems that can use tools, call APIs, manage memory, and coordinate with other agents to complete complex tasks with minimal human intervention.
We build these systems end-to-end: from defining agent architectures and tool interfaces to implementing retrieval pipelines, evaluation frameworks, and production observability. Whether you need a single autonomous workflow or a multi-agent orchestration layer, we focus on reliability, controllability, and real-world usefulness.
Agent architecture
Design agent systems with clear reasoning loops, memory, tool use, and failure handling.
RAG & retrieval systems
Build retrieval pipelines that give agents accurate, grounded access to your data.
Multi-agent orchestration
Coordinate multiple agents with different roles, tools, and permissions to solve complex workflows.
Evaluation & observability
Measure agent performance, trace decisions, and build confidence before going to production.
Software Engineering
Not everything needs AI. Some problems need well-architected, reliable software, and we know how to build that too.
We design and implement backend systems, APIs, and application architectures built for reliability and throughput. This is traditional software engineering done right, clean architecture, proper observability, automated delivery, and systems that scale predictably under load.
With experience spanning cloud platforms (AWS, Azure), microservices, event-driven architectures, and high-availability deployments, we help teams build foundations that hold up in production. Whether you're modernizing a legacy system or designing something new, we bring engineering discipline without unnecessary complexity.
Architecture & design
Microservices, event-driven systems, and API design for maintainability and scale.
Cloud infrastructure
High-availability deployments on AWS and Azure with automated CI/CD pipelines.
Performance & reliability
Load testing, observability, and systems that degrade gracefully under pressure.
Legacy modernization
Incrementally migrate monoliths to modern stacks without disrupting operations.
Machine Learning Consulting
We help you design ML pipelines that solve actual business problems, not just impressive demos that never ship.
Machine learning at scale requires more than a good model. It requires data infrastructure, feature engineering, reproducible training, robust evaluation, and serving systems that handle real traffic. We've built these pipelines for NLP, computer vision, recommendation systems, and anomaly detection across industries from finance to logistics.
We take a problem-first approach: understanding what decision your business is trying to improve, then designing the simplest ML system that delivers that improvement reliably. No over-engineering, no chasing benchmarks that don't matter. Just pipelines that work and teams that can maintain them.
Pipeline design
End-to-end ML pipelines from data ingestion through training, evaluation, and serving.
Model selection & strategy
Choose the right approach, classical ML, deep learning, or LLM-based, for your problem and data.
NLP & computer vision
Document processing, image classification, object detection, and text understanding at scale.
MLOps & monitoring
Reproducible experiments, model versioning, drift detection, and production monitoring.
Design & Research
Good technology solves problems. Good design makes sure people can actually use the solution. We offer the full range of design practice, from interfaces to services to the research that informs both.
UX Design
We design user experiences that make complex systems feel intuitive. This means information architecture, interaction patterns, and flows that reduce cognitive load, especially important when users are working alongside AI. We focus on clarity, control, and feedback so users always understand what the system is doing and why.
UI Design
Visual design that communicates hierarchy, state, and intent at a glance. We create design systems, component libraries, and high-fidelity interfaces that are consistent across products and easy for engineering teams to implement. Our background in front-end development means designs that are precise, buildable, and respect platform constraints.
Service Design
Service design looks at the full picture, not just the screen, but the entire journey across touchpoints, teams, and systems. We map existing processes, identify pain points, and design end-to-end service experiences. This is especially valuable when introducing AI into existing workflows, where the biggest risks are organizational, not technical.
User Research
Decisions should be grounded in evidence, not assumptions. We conduct qualitative and quantitative research, interviews, observation, usability testing, and data analysis, to understand how people actually work, what they need, and where current solutions fall short. Research findings directly inform design and engineering priorities.
Not sure what you need?
That's fine. Tell us the problem and we'll figure out the right approach together.
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