Forward-Deployed Engineering
Your engineering org has real problems that need real solutions. We scope, architect, and build AI systems in your actual environment, with your actual data, alongside your team. Then we hand off working software and the knowledge to maintain it.
A strategy consultant delivers a roadmap. The recommendations sit in a slide deck. When you want to build what they described, you’re starting from scratch.
A demo-focused AI agency builds a proof of concept in a sandboxed environment. The demo impresses. The production system doesn’t exist yet, and the gap between demo and production took three months you weren’t expecting.
Hiring a Head of AI takes four to six months to hire and another six months to ramp. By the time they’re productive, the tools they were hired to implement have changed significantly.
We embed directly with your team and build the production system in the first engagement. Your engineers work alongside ours so the knowledge transfers as the system gets built.
What we build
AI agents that handle multi-step tasks autonomously: research, drafting, analysis, data processing, coordination. We design agent systems that give models full context and appropriate tools, without boxing them into rigid scripts that break when anything changes. We know the difference between a workflow and an agent — and choose the right architecture for each problem.
Your organization’s most valuable data is locked in documents, wikis, databases, and email threads. We build retrieval systems that make that data queryable by AI agents and employees alike — with appropriate access controls and citations, so answers are traceable.
Classifying, routing, and drafting responses for incoming support volume. We build these systems against your actual ticket data, test against real edge cases, and measure accuracy before deployment.
Research, personalization, and outreach workflows for sales and marketing. We build systems that produce output your team actually uses rather than needing to heavily edit before sending.
We don't start with a multi-week discovery phase that ends with a recommendation for what to build. We use what we learn in discovery to inform what we build in the same engagement.
We map your environment — data sources, existing systems, team structure — and design the architecture. We bring an opinion on what to build and how to build it. You push back where you have context we don’t. The output is a specific, scoped system with clear success criteria.
We build in your environment, on your infrastructure. Your engineers participate in code reviews, architecture decisions, and integration work. The transfer of knowledge is continuous, not a handoff at the end.
You get a production system, documentation, and a team that understands how it works. The engagement ends when you can maintain and extend it without us.
Our architecture principles
These principles come from running 50+ hackathons and deploying production systems across multiple industries. They're not hypothetical.
Agents produce better work when they can see everything they need in one place. Fragmented data across twelve SaaS tools produces fragile agents that need constant orchestration. We push for data consolidation as a prerequisite to useful agent deployment.
AI capability has been doubling roughly every four to seven months. A system built around current model weaknesses is a system you’ll rebuild in six months. We design for the model’s trajectory, not its current constraints.
Codebases that agents can understand and work with effectively are organized differently than codebases designed purely for human developers. Consolidated structure, clear patterns, minimal external dependencies. We build systems that agents can extend.
We don’t deploy AI systems without evaluation frameworks in place. You can’t know what your system will do until users interact with it in production. We build measurement in from the start.
Go deeper
From coding agent deployment to Level 3 autonomous loops. Sandbox environments, security boundaries, CI integration, and the orchestration layer for agents that write code, run tests, and merge autonomously.
Evaluation frameworks built on real production traces. CI/CD monitoring, LLM judges tuned to your use cases, and the infrastructure to know what your AI systems actually do.
We work best with organizations that:
We work with engineering teams from early-stage to Fortune 500. The engagement scales with the size of the problem.
Engagement options
A scoped engagement targeting one specific production system. Defined scope, success criteria, and handoff plan. Works best when you know what you want to build.
Ongoing
An ongoing retainer for organizations that want continuous AI system development. Dedicated engineering capacity, monthly shipping cadence. Works best after an initial build establishes the patterns.
We map your environment, identify the highest-value system to build first, and scope the engagement. Most teams have a clear candidate within the first conversation.