Forward Deployed Agentic Engineers
Turn AI experiments into production systems
Ziverge embeds senior Agentic Coding engineers into your team to discover, build, and deploy AI-enabled workflows inside your real stack, with human-owned quality from design through production.
AI deployment works when it is built close to the real system.
Ziverge engineers work inside your environment, apply production engineering discipline, and keep senior human ownership over what gets shipped.
Built inside your environment
Your stack, repos, tools, workflows, data boundaries, and business rules shape the implementation from day one. We do not build generic AI demos outside the system your team actually operates.
Production-grade AI delivery
Every workflow is designed for review, testing, security, evals, observability, integration, and human approval paths. Speed comes from agentic execution, but production readiness stays owned by an accountable engineer.
Senior engineers, not AI tourists
Ziverge embeds production engineers who use agentic coding with discipline. They can reason through architecture, failure modes, maintainability, delivery risk, and the human handoff required to ensure longevity.
AI ideas are plentiful. Production deployment is the constraint.
Tools are available. Pilots are easy to start. The hard part is turning useful AI opportunities into systems that work inside real workflows, with clear ownership, controls, and maintenance.
01
Problem
AI tools are spreading without an operating model
Teams are using AI in different ways, with uneven standards for context, review, testing, security, and ownership. The result is activity without a reliable path to production value.
02
Problem
Prototypes are not becoming production systems
Demos and pilots prove what might be possible, but they often stall before real users, integrations, permissions, observability, and human approval paths are in place.
03
Problem
The right capacity is hard to find
Internal teams are already committed to core roadmap work. The profile needed is senior, technical, product-minded, and AI-capable enough to discover the opportunity and ship the implementation.
The Role
A senior engineer embedded where AI has to work.
A Ziverge Forward Deployed Agentic Engineer works inside your team to find high-value AI opportunities, build them in your existing stack, and leave behind systems your team can operate, inspect, and extend.
Embeds with your team
They join the operating context where decisions happen: engineering workflows, product priorities, delivery ceremonies, technical constraints, and stakeholder expectations.
Maps the real workflow
They learn your codebase, tools, data boundaries, approval paths, user needs, and failure modes before deciding what should be automated or AI-enabled.
Builds inside your stack
They create production-ready software, automations, or agentic workflows using your existing systems, repos, integrations, and deployment patterns.
Transfers the pattern back
They document the work, explain the implementation, train the relevant team members, and establish the review and maintenance path after launch.
What We Build
From AI opportunity to deployed workflow.
Ziverge engineers build the software, automations, and operating patterns that move AI from promising idea to usable system.
Engineering acceleration
Agentic coding workflows, onboarding tools, migration support, test generation, review automation, and internal developer tools that help teams move faster with control.
AI-enabled product workflows
Copilots, intelligent search, summarization, document understanding, domain-specific assistants, and AI-assisted workflows built into your existing products.
Internal automation
Intake, triage, reporting, document processing, routing, approvals, scheduling, support workflows, and operations tools built around your business rules.
AI operating infrastructure
Context systems, eval harnesses, review flows, observability, deployment patterns, governance workflows, and human-in-the-loop controls for production AI.
Process
A focused path from context to production.
Ziverge engineers embed, identify the right opportunities, build inside your stack, and transfer the system back with the documentation and controls needed to keep it running.
01
Embed
We join your team's operating context: repos, tools, ceremonies, roadmap priorities, architecture constraints, and delivery standards.
02
Discover
We map workflows, bottlenecks, data boundaries, approval paths, risks, and AI opportunities worth turning into production systems.
03
Build
We design, implement, test, and integrate software, automations, or agentic workflows inside your existing stack.
04
Transfer
We document the implementation, train the right owners, define review paths, and leave your team with a system they can maintain and extend.
Why Ziverge
Built for real deployment, not demo-ware.
Agentic workflows
Built around your real operating model
Agents are shaped around your workflows, tools, business rules, and handoffs, so they match how work actually gets done.
Guardrails before deployment
Safety built into every build
Every build accounts for review paths, permissions, testing, evals, observability, and human escalation before it reaches users.
Engineering ownership
Senior judgment stays accountable
AI accelerates implementation, but a senior engineer owns architecture, correctness, maintainability, and production readiness.
Find the first AI workflow worth deploying.
Start with a focused conversation about where AI can create production value inside your current workflows, systems, and constraints.