Understand before building
Follow a real task from start to finish. Find the repetitive work, missing context and problems worth solving.
AI FORWARD DEPLOYED ENGINEERINGEmbedded with your team. Invested in the outcome.
AIFDE
Intelligence,
deployed.
Turn one real task into an AI product or working business flow. We work with your team on input data, model behavior, human review and system integration, from a scoped pilot to an agreed production release.
Business context × AI engineering × Product delivery
THE DEPLOYMENT CORE02 / 03
INTELLIGENCEBring models, tools and people together.
Embedded with your teamBuilt around real workflowsFrom validation to productionDesigned for everyday use
THE LAST MILE / WHAT IS FDE
Models have potential.
Businesses need results.
Forward Deployed Engineers work where the business problem lives. We work alongside your team to understand the knowledge that never makes it into a requirements document, then turn it into working AI systems.
From what AI can do, to getting the work done.Connect the whole workflow
Work with your existing data and systems. Define what AI handles and where human judgment matters.
Own delivery and adoption
Evaluate representative and difficult cases with your reviewers. Agree the quality threshold, exception path and operating owner before expanding the release.
AI IN PRACTICE / INDUSTRY SCENARIOS
AI inside the business.
One workflow at a time.
Explore how a specific workflow could move from a business need to delivery across manufacturing, export sales, commerce, logistics, education, hardware and marketing.
MANUFACTURING / QUOTING & PRODUCTION
Manufacturing + AI
AI solutions for quotation and internal production management in traditional factories.
EXPORT SALES / INQUIRY & QUOTING · SCENARIO
B2B trade inquiries & quoting
Follow synthetic inquiry data through extraction, pricing-rule checks and human review to an English quote draft.
E-COMMERCE / OPERATIONS & ANALYSIS
E-commerce + AI
Connect competitor monitoring, listing reviews, product cost estimates and ad analysis in everyday Amazon store operations.
LOGISTICS / EXCEPTION MANAGEMENT
Logistics & supply chain + AI
Bring shipment updates and carrier messages together for exception follow-up, customer replies and clear handoffs.
HARDWARE / VOICE & CONTROLS
Smart hardware + AI
Voice interaction, intelligent controls and data analysis integrated into connected hardware.
EDUCATION / STUDY SUPPORT
Education + AI
Use course materials to guide students, hand unresolved questions to teachers and prepare targeted practice for review.
MARKETING / CONTENT WORKFLOWS
Marketing automation + AI
Automated Xiaohongshu content workflows for marketing agencies, reducing repetitive manual work.
BUILT AROUND YOUR WORK / USE CASES
Start with one
real workflow.
Choose a scenario to explore how AI fits into the work you already do.
Turn experience into a repeatable workflow.
Bring inquiries, specification extraction and historical context into one workflow, so your team can focus on judgment and exceptions.
Quoting assistanceKnowledge retrievalProduction coordination
Business input
Inquiries / specifications
AI + business rules
Extract needs · Apply rules
Human review
Check specifications & price
Business output
Draft quotes / handoffs
Keep people in the decisions that matter.Permissions, review, fallbacks and activity logs belong in the first version.
READY FOR FDE / FOUR CONDITIONS
Is your business
ready for FDE?
FDE brings engineering expertise. Making it work also takes a clear use case, access and commitment from your team. We align on these four conditions before we begin.
- BUSINESS CASE
Frequent, valuable and specific
Start with a recurring problem worth solving. Identify who does the work, where it gets stuck and what a meaningful improvement would look like.
- DATA & ACCESS
Access backed by leadership
Prepare representative samples you can authorize for evaluation, plus data formats, system documentation and deployment constraints. Agree permissions and model-provider data handling before granting access.
- INTERNAL OWNER
An owner who can make decisions
Assign an owner who can prioritize the scope, arrange system access and bring in domain reviewers. Decide who approves outputs and who handles exceptions once the workflow is live.
- WORKING PRACTICES
Willingness to redesign the work
Be ready to adjust workflows, responsibilities and how gains are shared. Align performance measures and incentives so teams can adopt the new way of working.
Align the conditions before bringing in the technology.
If some conditions are still missing, start by mapping the use case and running a small validation, while agreeing on access, ownership and collaboration.
FROM INSIGHT TO IMPACT / OUR APPROACH
Go deep.
Ship together.
One delivery team, from understanding the problem to running the system. Something tangible at every stage.
01Immerse
Get inside the work
Walk through one task with the people doing it. Record sample inputs, expected outputs, systems, permissions and constraints. Agree the pilot boundary, decision owner and acceptance criteria.
What you take away
- Workflow and dependency map
- Pilot scope and exclusions
- Acceptance criteria and reviewers
02Build
Prove it with real tasks
Use authorized sample data to build a working prototype. Review representative, missing-data and difficult cases; measure output quality, latency and usage cost. Agree what needs human approval and what happens when the model or integration fails.
What you take away
- Working prototype
- Reviewable evaluation cases and results
- Human approval and failure paths
03Deploy
Put it into everyday use
Connect the agreed systems and permissions. Add logs, monitoring and safe fallbacks. Define the operating owner, support process and rollback conditions, then release to a small pilot group before expanding.
What you take away
- Agreed production integrations
- Operating and exception-handling notes
- Pilot rollout and handover
04Evolve
Improve with the business
Learn from usage and operational feedback. Improve workflows, evaluations and knowledge. Keep what the team learns in a system they can maintain.
What you take away
- Operational feedback
- Iteration plan
- Code & knowledge handover
BEFORE WE START / QUESTIONS
Let's make things clear.
Can we start without a defined AI brief?
Yes. Bring a real workflow that is slow, repetitive or error-prone. We will break it down together, assess the data and identify where AI could help before defining the first pilot.
How does AI FDE differ from conventional outsourcing?
FDE includes deeper involvement in discovery and business validation. We define the problem, build and iterate with the people using the system. Delivery covers implementation, workflow integration and team adoption.
We need FDE, not just software development. How can you help?
We can work alongside your business and technical teams on an ongoing basis around a shared business goal: understanding frontline workflows, testing approaches with your existing systems and AI tools, and building additional software where needed. The engagement covers validation, system integration, team training and continued iteration, with results assessed against business metrics we agree together. We define how we work together, the engagement duration and any on-site needs before starting.
Can you integrate with our existing systems?
We first assess your APIs, data formats, permissions and deployment environment. We can start with a standalone pilot and progressively connect existing tools. The integration scope is agreed at the outset.
How do you handle data, permissions and AI errors?
Before using real data, agree authorized sources, permissions and model-provider handling. Validate the output against use-case rules and evaluation samples. Define which actions need a person’s approval, when to retry or escalate, what to log and how to continue if a provider fails. Sensitive or consequential actions require explicit controls appropriate to the workflow.
How do you price an engagement?
We quote in phases based on the use case, data readiness, system integration and deployment requirements. We usually start with a clearly scoped pilot, agreeing on fees, deliverables and acceptance criteria upfront. After validation, we define the investment for production deployment and expansion. Ongoing maintenance and improvement can be arranged monthly. Model usage, cloud resources and other third-party costs are listed separately in the quote, and new requirements are agreed separately.
What does an initial AI pilot include?
A pilot is agreed around one workflow and a limited set of inputs and integrations. Deliverables can include a working prototype, evaluation samples and results, human-review points, failure paths and a recommendation for production. A pilot does not automatically include every enterprise integration, custom model training, a full data cleanup or ongoing operations; those are separately scoped.
How do we decide whether the pilot is ready for production?
Agree acceptance criteria before building: representative output quality, review effort, latency, provider cost and important failure cases. Domain reviewers assess the recorded examples. Before release, confirm permissions, monitoring, exception ownership and a fallback or rollback path. If the criteria are not met, revise the scope or approach before expanding.
YOUR NEXT MOVE / START HERE
Your business.
Our starting point.
Share one workflow, anonymized input and output examples, the systems it touches and who will review results. We can define a first pilot, its dependencies and acceptance criteria in English or Chinese.
One workflow · Authorized sample data · Agreed review and release criteria