An enterprise AI initiative
Identify a workflow with a measurable cost, connect the relevant data and tools, and test an improvement with the people doing the work.
Engineers embedded with your business. We work alongside your operators and technical team to find the right problem, build the solution, and make it work in production.
Talk about your projectSit with the operators. Follow a real request. Agree on the first useful release.
Work in your repositories, show working software, and adapt to what users tell us.
Release together. Watch the real workflow. Make the next change with evidence.
Forward-deployed engineering is a delivery model that places engineers close to the people, systems, and decisions they are building for. Discovery and implementation happen together, with one team responsible for a defined outcome.
The most important requirements rarely arrive in a tidy specification. They show up in a conversation with an operator, an exception in a workflow, or an integration nobody has documented. We stay close enough to find them and technical enough to do something about them.
Identify a workflow with a measurable cost, connect the relevant data and tools, and test an improvement with the people doing the work.
Join your product and engineering rhythm. Own a defined feature, integration, or release, with the design and delivery work needed to finish it.
Translate a product capability into a working customer solution. Build the interfaces, integrations, and feedback loop that connect product teams to actual use.
From the first decision to delivery
Best when the problem crosses teams or systems, the requirements are still taking shape, and you need people who can both investigate and build.
Talk to users and operators. Review the current systems, data access, constraints, and prior attempts. Agree on a baseline before proposing a solution.
Define the outcome, decision makers, and first useful release. Keep the work visible in your tools, with direct access to the engineers building it.
Build, integrate, and release in reviewable increments. Test with actual users and representative data, with permissions and rollback plans in place.
Document decisions, pair with your team, and agree on ongoing ownership. Use production feedback to prioritize the next improvement.
Useful outputs. Clear ownership.
Scope varies with the project. These are the practical outputs we agree on and review with your team.