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Discuss a workflow
Discuss a workflow

Forward Deployed AI Engineering

AI that fits the way your business works.

We study how your organisation operates, find where time and attention are being lost, and bring AI into the systems your people already use.

Discuss a workflowDownload brochure
System diagramWorking

Instruction from an employee“Prepare the weekly compliance report and send it for approval.”

Existing operation
  • Teams
  • Email
  • ERP
  • Documents
  • Tools
Context layer
  • Rules
  • Roles
  • Permissions
  • Knowledge
  • Approvals
AI at work
  • Retrieve
  • Prepare
  • Coordinate
  • Execute
  • Escalate
Business result
  • Less manual work
  • Faster decisions
  • Clearer control

Retrieving last week’s compliance records

Organisations we have worked with

500+Certificates processed daily

Vessel certificate information handled through NavCert in production.

5+Engineers trained in the method

Forward Deployed Engineers trained to study an operation before choosing a technology.

3+Years building operational systems

Building products and systems for real organisations, not demonstrations.

~6Weeks of research before deployment

Time spent inside one organisation studying its industry, systems, and options before building.

  1. A
  2. B
  3. C
  4. D

Chapter A

We work inside real operations.

Our engineers enter the organisation. They speak with leadership and sit with the people doing the work.

We do not begin with demonstrations. We begin with operational problems.

EvidenceWhere we start
First weekWith the people performing the work, not with a specification
We studySystems, documents, decisions, delays, and exceptions
We avoidA demonstration built away from the operation

Chapter B

We understand the business before choosing the technology.

They study the systems, documents, decisions, delays, and exceptions behind the process. Then they research the possible interventions.

Sometimes the answer is an AI agent. Sometimes it is automation, integration, conventional software, or a better process. Often, it is a combination.

EvidenceInterventions we consider
Option 01An AI agent with defined responsibilities
Option 02Automation or integration between existing systems
Option 03Conventional software, or an existing product
Option 04A better process, with no new software at all

Chapter C

We bring AI into the tools people already use.

If your employees work through Microsoft Teams, we can place AI there. If decisions move through email, AI can work through email.

We begin where the work already happens. That is how adoption becomes natural.

EvidenceWhere the intervention sits
Microsoft TeamsWhere delegation and coordination already happen
EmailWhere decisions and approvals already move
Existing ERPBuilt around, rather than replaced

Chapter D

We stay until the result is visible.

The work is not complete when software is deployed. It is complete when the system is being used and the outcome can be seen.

We measure what changed. When the result is clear, we move into the next workflow.

EvidenceWhat completion means
DeployedRunning inside the real working environment
AdoptedUsed by the people the work belongs to
MeasuredCompared against the original process

Recognition

Most companies do not need another AI tool.

They already have ERPs, databases, documents, spreadsheets, email, and processes built over many years.

The problem is not always a lack of technology. The problem is that these systems do not work together.

Information gets scattered. Employees repeat the same work. Decisions move slowly.

Every new application creates another login, another interface, and another process to learn.

ERPThe system of record
  • SAP
  • Zoho
  • Oracle
No shared context
SpreadsheetsTracking kept outside the system
  • Excel
  • Google Sheets
Manual copy
EmailWhere decisions actually move
  • Outlook
  • Gmail
Waiting for approval
ChatWhere work is delegated
  • Microsoft Teams
  • Slack
Document foldersCertificates, contracts, and records
  • SharePoint
  • Google Drive
Manual copy
EmployeesExperience, exceptions, and workaroundsInformation held by one person
ApprovalsWho is allowed to decideWaiting for approval
Every new applicationAnother login, another interface, another process to learnNo shared context
A typical operation before an intervention. Product names are examples of what a team already runs, not partnerships. The problem is rarely a missing tool.

The principle

Do not make people work around AI.Make AI work around people.

If employees already know how to ask, approve, assign, and respond, we use those habits as the interface.

We begin where the work already happens. That is how adoption becomes natural.

Microsoft Teams

If your employees work through Microsoft Teams, we can place AI there.

Email

If decisions move through email, AI can work through email.

An existing ERP

If an existing ERP remains important, we can build around it instead of replacing it.

Business context

The model is intelligent.The missing piece is your business.

Modern AI can read, write, reason, search, and take action. But it does not automatically understand:

  • How your company creates value
  • Where important information is stored
  • Why employees follow a certain process
  • Who is allowed to make a decision
  • What must be approved
  • Which systems cannot be disturbed
  • What a useful outcome looks like

That knowledge lives inside the organisation. Our work is to understand it, structure it, and connect it with the right technology.

The modelReads, writes, reasons, searches, and takes action.
Ahum LabsAdds business knowledge, permissions, responsibilities, workflow rules, and human control.
Your operationProduces useful work inside the real business.

Questions only the organisation can answer

  • Who may do this?
  • Which data can be used?
  • When is approval required?
  • What does success mean?

We turn business knowledge into working systems.

Our engineers enter the organisation. They speak with leadership and sit with the people doing the work. They study the systems, documents, decisions, delays, and exceptions behind the process. Then they research the possible interventions.

Sometimes the answer is an AI agent. Sometimes it is automation, integration, conventional software, or a better process. Often, it is a combination.

We do not begin with a product. We begin with the problem.

Method

Context before code.

Learn. Choose. Deploy. Prove. Expand.

Every stage produces something the organisation can read, question, and keep.

  1. 01Learn the operationWe study how the business actually works—not only the official process, but the real one shaped by employees, workarounds, approvals, exceptions, and experience.Interview notes and a map of the real process
  2. 02Find the constraintWe identify where work slows down, information gets lost, employees repeat themselves, decisions wait, or an existing system prevents progress.A marked process map showing where time is lost
  3. 03Study the optionsWe research available tools, products, models, frameworks, and industry practices. We look at what exists before deciding what must be built.A written comparison of what exists and what it costs
  4. 04Choose the right interventionWe explain the available approaches, show the trade-offs, and recommend what best fits the organisation.A recommendation with the trade-offs written down
  5. 05Deploy inside the workflowWe build or integrate the system where employees already work. Permissions are defined, human control is preserved, and change is introduced gradually.A permission model and a controlled production deployment
  6. 06Prove and expandWe measure what changed. When the result is clear, we move into the next workflow.A comparison of the new workflow against the original process

The full method

Selected deployments

Work inside real organisations.

We do not begin with demonstrations. We begin with operational problems.

Each deployment starts by understanding the business and ends with a system designed for real use.

Deployment 01Enterprise operationsDeployed

Building an AI workforce inside Microsoft Teams

An organisation wanted AI to become part of its daily operation—not a small experiment or another isolated application, but a working capability inside the company.

Research period
Approximately six weeks
Working environment
Microsoft Teams, email, internal systems
Ahum Labs’ role
Research, system design, agents, integration, permissions, deployment
Outcome
Outcome pending client approval

Read the case study

System diagram — governed AI workerDeployed
Operations lead · Microsoft Teams

“Prepare the weekly compliance report and send it for approval.”

Handled by
AI worker · Compliance operations

Works with an organisational identity, approved tools, and controlled access to internal systems.

  • Defined responsibility
  • Approved tools
  • Controlled access
  • Operating limits
  • Escalation rules
Escalates to
Named approver · Human decision

The action is not completed until a person with authority confirms it.

The AI workforce sits inside Microsoft Teams, where delegation and coordination already happened. No new interface was introduced.
Deployment 02Maritime and shippingDeployed

Making complex maritime work conversational

A maritime organisation needed a simpler way for employees to work across a complicated internal process involving documents, systems, rules, permissions, and several steps.

Industry
Maritime and shipping
Ahum Labs’ role
Workflow research, conversational interface, integration, deployment
Outcome
Outcome pending client approval

Read the case study

System diagram — natural-language interfaceDeployed
Employee · Plain language

Describes what needs to be done, in the words they would use with a colleague.

Behind the conversation
  1. Understand the intentWhat is actually being asked
  2. Retrieve relevant informationFrom approved sources
  3. Check permissionsMay this person do this?
  4. Complete the required stepsAcross several systems
  5. Return the resultIn the same conversation
The employee sees a simple conversation. The engineering remains behind it.
Deployment 03Maritime operationsDelivered

When the correct solution used less AI

A major shipping organisation approached us after a poor experience with an earlier technology provider. We did not begin by proposing an AI platform. We began by asking what had gone wrong.

Industry
Maritime operations
Ahum Labs’ role
Research, product thinking, workflow design, engineering
Outcome
Outcome pending client approval

Read the case study

Intervention mix — as deliveredDelivered

What the answer actually required

ExpectedA replacement AI platform
RecommendedBetter workflow design, product judgement, and disciplined engineering
RejectedThe larger, more commercially convenient system
Our responsibility is not to maximise the amount of AI inside a company. It is to find what best fits the business.

Explore our work

The role

We call this Forward Deployed Engineering.

A Forward Deployed Engineer is more than a software developer. They combine engineering with business understanding and work directly with executives, operators, and internal technology teams.

They do not wait for a perfect requirements document. They help discover the real requirement. Then they design the intervention, build it, introduce it into daily work, and remain accountable for the result.

The work is not complete when software is deployed. It is complete when the system is being used and the outcome can be seen.

  1. 01

    Engineering

    The ability to build reliable systems.

  2. 02

    Business understanding

    The ability to connect technology with cost, time, risk, capacity, and growth.

  3. 03

    Communication

    The ability to work clearly with people across the organisation.

  4. 04

    Practical judgement

    The ability to know what to build, what to integrate, and when not to use AI.

Role profile
Works acrossProduct · AI · Integration · Adoption
Sits withExecutives, operators, and internal technology teams
DecidesWhat to build, what to integrate, and when not to use AI
Accountable forA working business outcome

Control

AI workers with responsibilities, access, and limits.

The systems we build are not unrestricted chatbots.

An AI worker can work through email, Microsoft Teams, documents, databases, internal software, and business systems. It performs only the work it has been authorised to perform. People remain in control of important decisions.

What an AI worker receivesBefore it acts
  • A defined role
  • A company identity
  • Approved tools
  • Controlled access
  • Clear responsibilities
  • Permitted actions
  • Approval required
  • Escalation rules
Permissions are defined before autonomy is granted, not afterwards.

Responsibility is earned gradually.

Begin as an assistant. Become automation after trust is earned.

  1. AI
    First, AI retrievesIt finds information from approved sources.
  2. AI
    Then, AI preparesIt drafts reports, responses, classifications, and actions.
  3. People
    People reviewEmployees confirm important outputs.
  4. AI
    AI executesThe system performs clearly defined tasks.
  5. People
    People manage exceptionsEmployees focus on judgement, relationships, and unusual situations.

Natural interfaces

Complex systems should feel simple to use.

Employees already know how to send a message, ask a question, write an email, assign work, and approve a request. We use these familiar behaviours as the interface.

Behind one request
  • Retrieve information
  • Check permissions
  • Process documents
  • Prepare the report
  • Identify the recipient
  • Begin the approval
The employee sees a simple conversation. The engineering remains behind it.

Maritime

Deep work in maritime technology.

Maritime organisations operate across vessels, regulations, documents, people, approvals, and software developed over many years.

The work cannot stop while new technology is introduced. That changes how systems must be built.

We have spent time with maritime professionals, studied real workflows, built systems for operational use, and learned where technology helps—and where it creates unnecessary complexity.

Our experience includes

  • Vessel documentation
  • Certificate management
  • Compliance workflows
  • Recruitment and crewing
  • Operational communication
  • Internal information retrieval
  • Natural-language systems
  • Legacy software modernisation

Explore our maritime work

Products

Some repeated problems become products.

Client work teaches us where the same problem appears again and again. When a pattern becomes clear, we turn what we have learned into a reusable system.

Field problems become infrastructure. Infrastructure becomes products.
SorchLive product

AI workforce for recruitment operations.

Sorch helps recruitment teams manage high-volume operational work: candidate screening, calling, follow-ups, scheduling, qualification, and structured handovers.

NavCertIn production

Maritime certificate intelligence.

NavCert helps maritime organisations extract, organise, monitor, and act on vessel certificate information.

500+ · Certificates processed daily

How these products came from the field

Research

We research before we recommend.

Before we recommend an intervention, we study the organisation, its industry, existing products, modern models, proven engineering methods, adoption risks, and long-term trade-offs.

We do not build from excitement alone. We build from evidence.

  • Field notes
  • Engineering notes
  • Maritime research
  • Frameworks
  • Experiments

Read our research

Company

We started by building things.

For three years, we built products, explored new technology, and competed in international hackathons.

Then companies began giving us real operational problems.

We expected the hardest part to be writing the software. It was not. The hardest part was understanding the business well enough to know what should be built. That changed the way we worked.

Today, Ahum Labs trains Forward Deployed Engineers who combine engineering, business understanding, communication, and practical judgement. We still begin every engagement in the same way: by learning how the work is really done.

Read our story

Judgement

Serious engineering begins with good judgement.

What we choose not to do shapes the result as much as what we build.

01

We do not begin with a sales demonstration

We begin with the people and process behind the problem.

02

We do not use AI everywhere

We use it where it improves the result.

03

We do not force immediate replacement

We can initially work around the systems the organisation already depends on.

04

We do not disappear after deployment

We remain involved through adoption, measurement, and improvement.

05

We do not hide the trade-offs

We explain the possible approaches before recommending one.

06

We do not separate engineers from the customer

The people understanding the problem remain closely involved in building the intervention.

Engagement model

Begin with one important problem.

You do not need a technical specification. You need a workflow worth improving.

Stage 01

Understand

We study the workflow, the people, the systems, and the present cost of the problem.

You receive · A clear view of what is happening and why.

Stage 02

Recommend

We research possible approaches and explain their trade-offs.

You receive · A practical recommendation suited to your business.

Stage 03

Deploy

We build or integrate one focused system inside the real working environment.

You receive · A controlled production deployment with clear responsibilities and human oversight.

Stage 04

Measure

We compare the new workflow with the original process.

You receive · Evidence of what improved, what did not, and what should happen next.

Stage 05

Expand

We move into related workflows only after the first result becomes clear.

You receive · A gradual path towards a wider AI capability.

We work best where the work is complex.

We may not be the right fit when the requirement is only temporary development capacity or an AI demonstration without a real business purpose.

Ahum Labs may be a strong fit when

  • Existing systems cannot be replaced overnight.
  • Important work still depends on email, spreadsheets, documents, and manual coordination.
  • Employees spend significant time searching, copying, checking, or following up.
  • Leadership wants AI to become part of the operation.
  • The organisation can give engineers access to the real workflow.
  • The outcome can be measured.

Questions

Practical answers before a conversation.

Do we need to replace our existing software?

Usually, no. We often begin by working around the systems the organisation already uses.

Replacement is considered only when the existing system becomes the clear obstacle to further improvement.

Do you only build AI systems?

No. Some problems require integration, automation, workflow redesign, conventional software, or an existing product.

The business problem decides the technology.

What is a Forward Deployed Engineer?

A Forward Deployed Engineer works closely with the customer’s operation.

They understand the business problem, study the workflow, build the system, support adoption, and remain responsible for the result.

Can your systems work inside Microsoft Teams or email?

Yes, where appropriate. We can introduce AI through the systems and communication channels employees already use.

How do you control what an AI worker can do?

Each AI worker can receive clear responsibilities, approved tools, access limits, human approval requirements, and escalation rules.

Will employees need extensive training?

Our aim is to reduce the learning curve. Where possible, we use familiar behaviours such as messages, email, approvals, and natural-language requests.

How do you decide where to use AI?

We study the workflow first. Then we evaluate whether AI, automation, integration, conventional software, or process improvement is the most useful intervention.

How does an engagement begin?

Bring us one workflow creating meaningful delay, repetitive work, cost, or operational risk. We will study it and recommend a practical first step.

Control by Ahum Labs

Turn your operating context into a system.

Control is a private AI workspace designed to connect the tools, information, and decisions your operation already depends on.

  • CRM and customer context
  • Tasks and operating workflows
  • WhatsApp and everyday communication
  • Content, knowledge, and internal tools

Do not begin with a software requirement.Begin with the work that is not working.

Tell us where people lose time, information becomes difficult to find, decisions slow down, employees repeat the same work, or an existing system holds the organisation back.

We will study the operation before deciding what should be built.

Discuss a workflowSee how we work

No technical specification required