A Forward Deployed Engineer (FDE) works alongside the customer team to understand real workflows and constraints before designing and building the right system for that organisation. At MALIPS, one important outcome is turning routine work into automation and giving the team more time to grow the business—with a personal AI assistant running on infrastructure the customer controls.
Stop drowning in routine work. Hand it to your personal AI agent, available 24/7
How much time does your team lose each day to repetitive work? MALIPS Forward Deployed Engineers work alongside your organisation to design and deploy a personal AI assistant that learns its documents, data, and workflows. It takes on tedious operational work so you and your team can focus fully on growing the business.
An AI agent does not belong in every step. An FDE helps separate what should be automated, what needs human review, and which data should remain behind permissions or on infrastructure the organisation controls.
What should an AI Agent automate?
Start with work that repeats, has clear inputs, can be checked afterwards, and has an accountable owner. Do not start with a list of what an AI model can do.
| Automate | AI drafts, human reviews | Human decision |
|---|---|---|
| Classify documents, move validated data, route work against defined conditions, and monitor known states | Draft replies, summarise permitted data, prepare reports, or propose a next action | Approve money, change access, make legal decisions, assess people, or handle cases without enough evidence |
A workflow before and after an AI Agent
Before automation, a team might open an email, download a document, check fields, copy data into a CRM, notify an owner, and return later to chase the status. After an FDE maps the work, the system can receive the document, validate its format, extract defined fields, route exceptions to a reviewer, write approved data, and notify the owner—with a log for every step.
The goal is not to remove people from the workflow. It is to give them more time for exceptions, decisions, and customer relationships instead of copying data.
Is the organisation ready for an AI Agent?
- The work repeats and the starting trigger and expected result can be described.
- The source documents or data are known and have an accountable owner.
- Permissions and data that must not leave the system are identified.
- Exceptions are available as examples, with rules for when a person reviews them.
- Acceptance criteria can be checked without relying on the phrase “looks intelligent.”
If the immediate problem is bringing customer data and work states into one place, CRM and internal tools may be a better first step than adding AI.
For a wider view of use cases, measurement, and risk, read how AI can help a business.
Frequently asked questions about FDE and AI Agents
Must an AI Agent always run in an external cloud?
No. The architecture depends on the data type, permissions, model, and organisational requirements. Some components may stay on infrastructure the business controls, while external services receive only approved data.
Can the organisation choose the AI model?
Yes. Model choice can follow data, language, quality, cost, and latency constraints. An FDE defines the workflow and acceptance criteria first so the technology does not replace the business problem.
What determines the operating cost of an AI Agent?
Cost can include integration work, model processing, infrastructure, logs, monitoring, and maintenance when the workflow changes. It should be estimated from actual volume and operational importance.
When is conventional software a better fit than AI?
When rules are fixed, inputs are structured, and the same result is required every time, deterministic software is usually easier to inspect and operate. An AI Agent can be limited to the part that must interpret language or variation.
Explore the business AI Agent implementation service or tell Malips where the workflow is stuck.
How is this different from taking requirements?
A conventional handoff assumes the problem has already been defined. In many businesses, written processes differ from the real workflow, exceptions are hidden, and teams do not share the same definition of success.
An FDE begins by asking:
- Who uses this process, and what information drives a decision?
- Where does the work wait, repeat, or require correction?
- Which constraints are technical and which are operational?
- What is the smallest useful outcome the team can test?
A typical working loop
| Stage | Work | What the team should receive |
|---|---|---|
| Observe | Study workflows and talk to users | An evidence-based problem picture |
| Align | Map priorities, risks, and assumptions | A first scope everyone understands |
| Build | Ship and test in short loops | Working software that creates feedback |
| Launch | Check critical production paths | A system ready to use and observe |
| Learn | Capture decisions and hand over | Documentation, context, and next steps |
When does a business need an FDE?
The model works well when a project connects several teams, data sources, or operational exceptions—for example a specialized CRM, operations tool, marketplace, or product still finding product–market fit.
If the job is a small website with a finished structure and content, a clearly scoped web team may be the better fit. Forward deployment should not be added merely to make an engagement sound more complex.
A real-system example: “add auctions” is not a requirement
In THBid, “auction functionality” expands into draft, active, reserved, sold, shipped, and completed states. It also includes timed auctions, reserve prices, buy now, realtime bids, payments, and permissions across buyers, sellers, and operators. Starting with screens before defining each transition and owner would move unresolved decisions into payment and fulfillment.
That is the FDE pattern in practice: study the workflow and exceptions first, then turn the findings into a state model, permissions, and testable delivery steps.
Questions to ask before starting
- How will the team access real users and workflows?
- How will scope and trade-offs be decided together?
- When will you first see working software?
- Who owns security, testing, and production readiness?
- How will the system and knowledge be handed over?
If the problem is still difficult to describe but the current process is clearly stuck, explore Forward Deployed Engineering or tell Malips what is happening.