Works in your rhythm.
Your repositories, your tickets, your standups. Decisions are made where your team already makes them, with nothing lost in a handoff between sales, product and delivery.
Forward-deployed engineering
We build it with your team.
Then we keep it running.
A forward-deployed team works inside your repositories, standups and release process. It builds the agent platform or AI product, sets up or strengthens the harness your engineers use with coding agents, then stays on monthly to run, evaluate and improve what it built.
How it works
Review. Build. Run.
Start with a review or go straight to a build. Either way, we stay on after launch to run what we built.
- 01 · Review
Architecture & Harness Review
Two to three weeks
An independent read of your AI system or your team's agentic engineering workflow, ending in a prioritized plan.
- 02 · Build
Forward-deployed build
Initial three-month engagement
A forward-deployed team embeds with yours and builds the agent platform, AI product or the systems behind it. Scope, milestones and acceptance criteria are agreed before work starts.
- 03 · Run
Forward-deployed operations
Monthly retainer
After launch, we stay on to run the evaluations, keep the harness current as models change, respond to incidents and ship improvements.
The forward-deployed model
Inside your team, not across the table.
Builds with a harness.
Agents implement bounded tasks. Engineers own the architecture, the review and the release. Your team inherits the harness along with the software.
The methodStays after launch.
Deployment is not the end of the engagement. Evaluations, cost tracking, incident response and model upgrades continue on a monthly retainer.
A good fit
Built for teams that need it shipped and kept running.
- You have an agent or AI product to ship, and the roadmap can’t wait for a hiring cycle.
- Your engineers use coding agents, and you want that work reviewable, mergeable and maintainable.
- The system touches customer data, money or clinical workflows, so permissions, evaluations and audit trails matter.
- You want it run and improved after launch, not just handed over.
Forward-deployed operations
A monthly retainer to run what we built.
Models, tools and coding agents change every month, and a live agent has to keep up. We keep repository context, evaluations and operating knowledge current as the system evolves, and keep shipping improvements.
- Evaluations
- Scheduled evaluation runs and regression checks when models, prompts and tools change.
- Harness upkeep
- Repository context, permissions and verification gates kept current as models and coding agents change.
- Operations
- Cost and latency tracking, incident response and recovery for the systems in scope.
- Improvements
- New capabilities, integrations and fixes as the product evolves.
- Readout
- A readout for leadership: what shipped, what the evaluations show and what comes next.
AI-Native SaaS / selected work
Agent Platform Engineering for Enterprise AI
- The problem
- Give AI agents a way to find, create and update a content platform's structured information.
- What we built
- Backend AI with Anthropic, OpenAI and Google models, 50+ MCP tools, content APIs and semantic search.
- What it made possible
- Agents that find information, act on it and power interactive experiences inside compatible AI clients, for a unicorn startup and Fortune 500 companies.
- 50+
- MCP tools built
Backend AI, API and infrastructure engineering, from architecture through launch.
Read case studyAgent platform engineering
- Backend AIAnthropic, OpenAI and Google
- 50+ MCP tools builtDiscover, create and update content
- APIs and searchStructured content and semantic retrieval
- Platform infrastructureSupports agents and MCP Apps
Who we work with
Forward-deployed for your kind of team.
Start a conversation
What are you building—or deciding?
Tell us where you are. We reply within one business day, and the first conversation ends with a recommended next step.