01
The workflow
What actually happens, who owns each handoff, what cannot change, and what “better” means in dollars, time, risk, or throughput.
AI · EMBEDDED · PRODUCTION
AI Forward Deployed Engineering
Put a senior AI builder inside the problem—not outside it writing recommendations.
ZipLyne maps the workflow, connects AI to the systems your team already uses, ships the production software, and stays through adoption. One accountable technical lead from discovery to measurable operational value.
THE LAST MILE
The model is rarely the whole problem.
Production value lives in everything around it.
02
The systems
Databases, internal APIs, documents, identity, permissions, vendors, and the legacy behavior nobody wrote down.
03
The controls
Evals, structured outputs, audit trails, approval gates, fallbacks, privacy boundaries, monitoring, and rollback.
04
The adoption
A tool that fits where people already work, survives real inputs, earns trust, and remains useful after the launch demo.
WHAT WE SHIP
One engagement. Four owned outcomes.
01DELIVERABLE
Enterprise AI workflow discovery
Map the people, systems, approvals, data, failure modes, and measurable outcome before choosing what to build.
02DELIVERABLE
AI integration and agent development
Build AI agents, MCP servers, retrieval systems, internal tools, and model-powered workflows around the systems the business already uses.
03DELIVERABLE
Production AI deployment
Ship with evaluations, permissions, observability, human approval points, audit trails, fallbacks, and operating documentation.
04DELIVERABLE
Adoption, iteration, and handoff
Work with the operators using the system, fix what breaks in real conditions, measure adoption, and leave the team able to run it.
Common artifacts AI agentsMCP serversInternal toolsRAG and searchEvalsApproval workflowsAudit and monitoring
THE DIFFERENCE
Not a deck. Not a demo. Not staff augmentation.
A forward deployed engagement owns the result end to end. We can advise, but the deliverable is working software: integrated with your environment, tested against real cases, operated by the people who need it, and measured against the outcome agreed before the build.
THE ENGAGEMENT
From “AI could help” to a system people use.
- 01
Discover
Walk the last real example. Surface constraints, prior failures, owners, objections, costs, and the threshold for success.
- 02
Scope
Choose one bounded workflow. Define the baseline, production metric, non-goals, risk controls, and smallest useful release.
- 03
Build
Implement against real systems and representative data. Put ambiguity into deterministic code instead of asking the model to guess.
- 04
Prove
Run evaluations, failure tests, permission checks, and operator acceptance. Compare the result with the baseline before expanding access.
- 05
Deploy
Roll out with monitoring, fallbacks, approvals, and a responsible owner. Keep consequential automation bounded until the evidence supports it.
- 06
Adopt
Watch the workflow in use, repair the week-two failures, document it, and hand control to the client team.
BEST FIT
Bring us the messy, valuable workflow.
This is a fit when…
- You have an AI pilot that never became part of daily operations.
- Your team knows the workflow but lacks one owner across product, code, and rollout.
- The solution must connect to existing data, vendors, permissions, or legacy systems.
- Accuracy alone is not enough; governance, adoption, and measurable impact matter.
We will say no when…
- The project is an AI demo without a user, workflow, or success measure.
- A smaller deterministic automation solves the problem better.
- The required data access or consequential actions cannot be made safe.
- The team wants a strategy deck but nobody owns implementation.
RELATED PRODUCTION WORK
AI systems built for real operating environments.
Selected technical work relevant to forward deployed delivery.
PRODUCTION AI WORK
baba
AI translation across web, iOS, Android, Chrome, and publisher workflows.
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Ringwell
Multi-tenant AI voice operations with encryption and campaign controls.
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MentionWell
Agentic research and SEO workflows with measured search visibility.
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Shehebar Law
AI-assisted legal operations for exhibits, research, and document handling.
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TradeSight
Factory-quote parsing and landed-cost calculation inside import workflows.
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Witz Agents
Durable multi-step agent pipelines with retries, routing, and handoff.
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AI forward deployed engineering questions.
Direct answers for technical leaders, operators, and teams evaluating an embedded AI engineering partner.
What is AI forward deployed engineering?
AI forward deployed engineering embeds a senior technical builder close to the business team to discover a valuable workflow, integrate AI with the company’s real systems, deploy it safely, and improve it until people use it in production. It combines software engineering, product judgment, and operational change.
What does an AI forward deployed engineer build?
Typical deliverables include AI agents, MCP servers, retrieval and search systems, internal applications, document workflows, support and sales tools, data integrations, evaluation suites, approval flows, audit logs, and production monitoring.
How is this different from AI consulting?
Traditional consulting can end with recommendations or a roadmap. ZipLyne’s engagement ends with working software in production, connected to the real workflow, measured against an agreed business outcome, and handed over in systems the client controls.
Can ZipLyne work with our existing engineering team and legacy systems?
Yes. The service is designed for existing environments. ZipLyne can work alongside internal engineering, security, operations, and domain teams, integrate with documented or messy systems, and keep the change as narrow as the business outcome allows.
Do we need to know which AI model or agent framework to use?
No. The engagement starts with the workflow and constraints, not a preferred model. ZipLyne selects models, tools, and architecture only after the required accuracy, latency, privacy, cost, and approval boundaries are clear.
How do you keep enterprise AI systems safe and reliable?
Risk is handled in the system design: least-privilege access, structured outputs, schema validation, evaluations, audit logs, bounded actions, human approval for consequential steps, fallbacks, monitoring, and a clear way to stop or roll back the workflow.
Who owns the code and infrastructure?
The client does. ZipLyne builds in client-controlled repositories and accounts where practical, documents the system, and avoids creating unnecessary platform lock-in.
START WITH THE WORKFLOW
Show us the process that should work better.
No architecture theater. Walk us through the last real example, the systems involved, and what a useful outcome would change.