Customer-Facing Assistants
Agents that handle inquiries, guide purchasing decisions, and resolve support tickets — not scripted chatbots, but agents that understand context and take action.
Agentic AI Systems
Build AI agents that
Build AI agents that
actually do the work.
Not chatbots. Not copilots. Autonomous agents and coordinated multi-agent systems that handle complex tasks, make decisions, and operate at scale — built to integrate with your stack and grow with your business.
—— What we build
Agents for every layer of your business.
From customer-facing assistants to internal automation engines, we design and build agents that handle real work.
Operations Automation Agents
Agents that automate repetitive workflows — document processing, data extraction, scheduling, routing, and coordination tasks that currently eat human hours.
Data Analysis & Insight Agents
Agents that continuously monitor your data, surface anomalies, generate reports, and provide actionable intelligence without being asked. Proactive, not reactive.
Coordinated Multi-Agent Systems
Complex workflows where multiple specialized agents coordinate to accomplish tasks no single agent can handle — orchestrated through our Enterprise Orchestration Platform.
—— Where agents go wrong
A demo is easy. Production is hard.
Most agents look great in a controlled demo and fall apart the moment they meet real data, real users, and real stakes. These are the failure modes we build against.
Demos that die in production
A polished demo proves nothing. Real inputs and edge cases break the happy path the moment it leaves the room.
Agents that take wrong actions
An agent that can act can act wrongly — updating the wrong record or firing the wrong call, confidently and at speed.
No visibility into what ran
When an agent fails silently, you can't tell what it decided or why — so you can't fix it, only switch it off.
Autonomy you can't trust yet
Without guardrails and proof, full autonomy is a leap of faith — so the agent stays locked behind a human and never earns its keep.
—— Services
What goes into a real agent build.
Each piece stands on its own and feeds the next — from the architecture underneath to the observability that keeps it honest in production.
Agent Architecture
The reasoning loop, memory, and decision boundaries designed before a line of code is written.
Tool & API Integration
Wiring the agent into your real systems so it can read, write, and act — not just talk.
Multi-Agent Orchestration
Specialized agents that coordinate on work no single agent can handle alone.
Guardrails & Safety
Validation layers and human-in-the-loop checkpoints that keep high-stakes actions safe.
Evals & Observability
Measured accuracy, traced decisions, and dashboards that show exactly what the agent did.
Production Deployment
Shipping the agent live with monitoring, alerting, and fallback logic from day one.
—— How it's built
Not just an LLM with a prompt. A real system.
Most "AI agent" offerings are thin wrappers around ChatGPT. Ours are production systems — real error handling, fallback logic, monitoring, and the ability to take real actions in your environment.
1
Design
Agent Design Workshop
Define the agent's purpose, decision boundaries, data sources, tool access, and guardrails — every edge case mapped before we write code.
2
Build
Build & Integrate
A proper architecture — LLM backbone, RAG knowledge base, tool integrations, and monitoring — wired into your systems via documented APIs.
3
Validate
Test with Real Data
Run it against real scenarios with human oversight — measure accuracy, catch failure modes, tune prompts, and validate the guardrails.
4
Launch
Deploy & Monitor
Production deployment with full observability — real-time monitoring of agent decisions, performance dashboards, and alerting for edge cases.
—— The build, week by week
An agent build, plotted out.
A typical build, week by week. Phases overlap — integration starts while the architecture is still settling, and evals run alongside the build.
DESIGN
BUILD
VALIDATE
LAUNCH
WEEK 1Apr 06
WEEK 2Apr 13
WEEK 3Apr 20
WEEK 4Apr 27
WEEK 5May 04
WEEK 6May 11
WEEK 7May 18
WEEK 8May 25
WEEK 9Jun 01
WEEK 10Jun 08
WEEK 11Jun 15
WEEK 12Jun 22
—— Selected work
Products we've shipped that run in production.
A few of the products we've built and shipped — the same engineering discipline we bring to every agent we put in production.
—— Where agents lead
Every agent we build connects to what comes next.
Agentic systems are powerful on their own — and even more powerful as part of a full engagement.
AI Audit
Know exactly where AI fits before you spend a dollar building. Strategy, data readiness, and a prioritized roadmap.
Explore the AI Audit →AI Prototype Sprint
Validate your idea with a clickable prototype in three weeks. Real answers, not guesses.
Explore the Prototype Sprint →AI-Native Development
Production-grade AI products — designed, built, and shipped by a senior team in weeks, not months.
Explore AI-Native Development →Intelligent Automation
AI-powered automation for the repeatable work eating your team's day — document processing, data pipelines, compliance checks, and more.
Explore Intelligent Automation →AI Growth Engine
Data-driven growth that compounds — analytics, optimization, and AI-powered insights that scale your product after launch.
Explore the Growth Engine →—— Common questions
What teams ask first.
Agents vs chatbots, which LLMs we use, accuracy and guardrails, integrations, and when you need a multi-agent system.
What's the difference between an AI agent and a chatbot?
A chatbot responds to messages; an agent takes action. Our agents query databases, call APIs, create documents, update records, trigger workflows, and coordinate with other agents — operating autonomously within defined boundaries.
What LLMs do you use?
We're model-agnostic and pick the best LLM for the task — usually Claude or GPT-4o for complex reasoning, with smaller models for classification and routing. We can also deploy open-source models on your infrastructure.
How do you ensure agents don't make mistakes?
Every agent ships with guardrails, validation layers, and human-in-the-loop checkpoints for high-stakes decisions. We define clear boundaries for what the agent can and can't do, and monitoring catches anomalies in real time.
Can agents connect to our existing tools?
Yes — through APIs, webhooks, database connections, and file systems. Common integrations include Salesforce, HubSpot, Slack, Google Workspace, Jira, and custom internal tools.
Do we need an AI Audit first?
If you already know the agent you want and have a clear use case, you can start directly. If you're exploring where agents could add the most value, an AI Audit is the right starting point.
When do we need a coordinated multi-agent system instead of a single agent?
When the workflow involves multiple steps needing different skills, data sources, or decision types. Specialized agents that coordinate are more accurate, easier to debug, and simpler to extend than one do-everything agent.
—— Field notes
What we’re writing about.
Field notes from the studio — what we’re learning about AI products, agent UX, and the messy reality of shipping software in 2026.
