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AI Automation Services for Companies

AI Automation for processes you can measure

Start with the work you want to improve: handling time, output quality, and steps requiring approval. Model selection and budget follow those requirements.

I am Dicky Ibrohim, a Technical Lead and AI Automation Solution Architect. I help map operations, design architecture, and build automation within an agreed scope. You discuss the requirements directly with me.

This service can help when AI experiments are disconnected from everyday work, or when a company wants to assess its first process for automation.

From business requirements to implementation, with technical review and clear success measures.

Start with a process you can assess

Choose one workflow, record its baseline, identify who reviews the results, and test before expanding. The proposal follows those requirements, including running costs and responsibilities after launch.

See how I work
Audit first, solutions after Not a reseller of any tool You talk directly to the engineer Independent, in personal capacity
Written by Dicky Ibrohim — Technical Lead & AI Automation Solution Architect

Before Selecting Tools

Define the problem, the process owner, and how results will be assessed.

Questions for the initial audit

  • Is there recurring work with measurable volume and handling time?
  • Can existing tools exchange data through authorized access?
  • Which steps require human judgment, and who approves system actions?
  • Is data quality sufficient for the required output?
  • Who will use, monitor, and handle failures after launch?
  • Are development and running costs proportionate to the problem being addressed?

A basis for decisions

Model quality, data, access, cost, and team readiness can affect results. An audit helps assess how these factors interact in your process.

Define the required output, who checks it, and which actions require approval. Record the baseline so test results can be compared meaningfully.

Evidence supports the decision to continue, revise, or stop an experiment. A polished demo alone is not enough.

Three Areas to Assess

Choose a suitable area of work to automate first.

The examples below support discussions about sales, customer service, and internal operations. Relevance depends on processes, work volumes, data, and available permissions. We select priorities around company requirements rather than adding every function at once.

Layer 1

Sales automation: prospect research, proposals, and qualification

The Problem

Preparation work can limit the time available for conversations with prospects.

  • Company research is repeated before drafting messages
  • Tender responses or quotations draw on scattered documents
  • Initial prospect information is not organized for sales review

What I build

Depending on the requirements, I can design a workflow that uses approved sources, prepares draft responses, and organizes qualification under business rules. Prices, commitments in proposals, and message sending require clear approval rules.

Work you can assess

  • Research and drafts are organized for review
  • The team can inspect qualification reasons and correct unsuitable results
  • Preparation time and correction effort can be compared with the original process

Assessing value

Value is assessed through material quality, handling time, and team adoption. Transactions still depend on the product, pricing, market, and prospective customers’ decisions.

Layer 2

Customer service automation: account context and follow-up

The Problem

Scattered information makes service history harder for staff to understand.

  • Customer data resides in applications with different permissions and formats
  • Staff must consult several sources before responding
  • Service or contract reminders depend on manual checks

What I build

I can discuss account summaries from authorized sources, draft reports, and reminders based on agreed rules. Completeness and update frequency depend on access, APIs, data quality, and the source systems’ capabilities.

Work you can assess

  • Staff share a reference for the sources successfully connected
  • Draft communications can be checked before sending
  • Follow-up status and delivery failures can be made visible within scope

Assessing value

The aim is to help staff follow up consistently. Integration does not guarantee instant access to all data, receipt of every reminder, or contract renewal.

Layer 3

Operational automation: reporting, documents, reconciliation, and triage

The Problem

Repeated collection and transfer of information consumes working capacity.

  • Reports are assembled from spreadsheets or operational systems
  • Routine documents require the same information to be entered again
  • Data discrepancies and incoming tickets are not organized for review

What I build

I design data collection, output preparation, and exception checks around the process. The team continues to review steps requiring judgment, especially consequential documents or data changes.

Work you can assess

  • Recurring work has a defined workflow and responsible owner
  • Errors and exceptions can be routed for review
  • Handling time, corrections, and processed volume can be measured

Assessing value

Before expanding the system, compare results with the baseline and include usage and maintenance costs. Time reductions or capacity gains are not presented as figures that apply to every project.

One measurable workflow provides a basis for assessing value before adding functions. Testing may also show that process changes are more useful than adding AI.

Why Me

Discuss processes and implementation with the same engineer.

I help connect operational requirements with architecture, development, and testing. Audit deliverables, implementation, and support are distinguished in the proposal so you know what you are purchasing and how to assess it.

  • No agency layers.
  • Clear scope and responsibilities.
  • Tool costs discussed in the proposal.

Engineering Credential

My experience as a Technical Lead informs discussions about system relationships, access, testing, and maintenance. My profile page provides background; the case studies below describe completed implementations.

AI review and action boundaries

AI can assist with code and output preparation, but results may be incorrect or unsafe. OWASP Misinformation discusses these risks and human review. I assess outputs, functions, and business rules within scope; this does not guarantee freedom from errors or vulnerabilities.

Definitions

AI Automation Solution Architect
A role that translates operational requirements into architecture, component choices, and an implementation plan for AI or automation. The work is scoped with the company.
AI Audit
An assessment of processes, data, access, and user requirements to establish priorities and a testing plan. It may recommend process improvements without AI.
Build & Deploy
Execution of the scope agreed after an audit, from design through testing and release. Integration depends on technical feasibility, permissions, licenses, and access to the relevant systems.

You have a technical counterpart with whom to discuss options and their consequences directly. Work allocation, approvals, and operational responsibilities remain matters for agreement.

Implementation Experience

Three real systems I have built.

These summaries describe completed work. Organizations are anonymized; further information can be discussed only where permissions and confidentiality terms allow.

The outcomes belong to these specific cases and are not promises for a new project. Application elsewhere requires a fresh assessment of processes, data, technology, and human responsibilities.

Case 1

Automated Lead Generation System

Area: Sales Operations Automation

A sales team at an organization spent productive hours sourcing prospects, researching target companies, and writing personalized messages one at a time. Lead volume was constrained by available time — not by market size. The actual addressable market was far larger than the team could handle.

What I built

A pipeline that handled sourcing from public sources, automatic enrichment (industry, team size, need signals), first-message personalization based on the prospect's actual business context, and engagement tracking for qualification feedback loops.

Results

  • Daily lead generation output increased many-fold without additional staff
  • Personalization remained high and reflected each prospect’s business context in this implementation
  • Sales reps only handled prospects that already passed automatic scoring
  • Productive team capacity returned to conversations with hot prospects, not administrative work

Case 2

Engineer Performance Monitoring System

Area: Operational Automation — management overhead

In this project, preparation for engineering performance reviews was manual and took days of manager time. Evidence of performance was scattered across dozens of systems: Git, ticket tracker, code review, deployment log, observability dashboard. The result: managers relied on impressions, not data — and still spent the time that should have gone to strategic work.

What I built

A system that automatically pulled performance signals from every relevant source — code contributions, review quality, task throughput, incident response, impact on production metrics. Managers received a per-engineer summary complete with trends and anomalies, ready as material for 1:1 discussions.

Results

  • Evaluation cycles that used to take days dropped to a few hours of review
  • Assessments were based on documented data, not subjective impressions
  • Engineers received more specific feedback — promotion and compensation decisions became more accountable
  • Manager time returned to coaching, architecture, and hiring

Case 3

Hundreds-of-Customer-Websites Automation

Area: Operational Automation + Customer Lifecycle

An enterprise partner managed hundreds of customer websites in parallel: bug fixes, custom feature requests, plugin compatibility, performance issues. The work targeted for automation consumed dozens of engineer hours — bug reproduction, root cause diagnosis, fix, regression test, standardized deployment. With hundreds of active websites, the backlog grew faster than team capacity. Customers waited longer. The workload put pressure on the partner’s margin.

What I built

A system that fundamentally changed the unit economics of this work — automatic initial diagnosis from log and symptom report, fix candidate generation with the engineer as final reviewer, and standardized deployment to target websites. Not replacing the engineer — accelerating the phase that used to consume the most hours.

Results

  • Tasks that used to take dozens of hours were completed in minutes to hours
  • The partner's team handled significantly larger volume without additional headcount
  • Engineers returned to work that requires judgment — designing complex solutions, not routine execution

Agree on access, data use, and running costs.

For an agent that can change data or take actions, define each tool’s authority and approval points. OWASP Excessive Agency describes the risks of excessive functions, permissions, or autonomy. Read access need not include deletion rights; consequential actions need review. Backups and recovery also require assessment, since backups exposed to the same deletion permissions may be affected.

Public documents differ from customer data, employee evaluations, internal pricing, and credentials. OWASP Sensitive Information Disclosure informs discussion of sensitive inputs and outputs. We assess necessary data, processors, purposes, logs, retention, and access rights. Choosing private cloud or a local model requires examining the entire data flow, not just the server location.

Development fees are separate from model/API usage, infrastructure, storage, licenses, and maintenance. If the system uses paid services, OWASP Unbounded Consumption describes excessive usage and abuse risks. Access controls, request limits, monitoring, and stopping mechanisms need discussion for the selected service. Budget alerts do not necessarily stop charges.

Ready-made components may be sufficient when their workflows and terms fit. Custom architecture is relevant when data relationships, approval rules, or company processes require adaptation. Both approaches retain dependencies: assess price changes, API or feature limits, data policies, and export options if a service changes or stops. I help evaluate these technical choices within scope, rather than promising vendor independence or compatibility with every destination system.

Engagement Model

Three engagement models, priced through a custom proposal.

AI Audit

Custom proposal

Map operational problems and assess whether AI or automation is needed.

  • Process and ownership mapping
  • Assessment of data, access, and technical constraints
  • Priorities based on value and effort
  • Testing plan and success measures

Scope, deliverables, fees, and schedule are agreed once the requirements are understood. Your internal team can use the audit output under the agreement.

Build & Deploy

Custom proposal

Build a priority area that has been assessed and approved.

  • Architecture and workflow design
  • Implementation and feasible integrations
  • Testing and approval before release
  • Documentation and handover within scope

An audit precedes development. Integrations, acceptance criteria, and operational responsibilities are detailed in the proposal.

Ongoing Partner

Custom proposal

Provide continued assistance and development after the system enters use.

  • Periodic work prioritization
  • Review of operational results
  • Modular changes by agreement
  • Allocation of maintenance responsibilities

Capacity, period, support channels, and fees are agreed; this is not unlimited assistance or continuous monitoring.

An audit precedes development to clarify scope and acceptance criteria. Fees, schedule, access, documentation, and support are discussed for the project; no fixed rate applies to every requirement.

Just as Important to Know

Working boundaries to agree before starting.

  1. 1. I assess requirements before recommending tools; if process improvements are more appropriate than AI, we discuss them.
  2. 2. I do not promise percentages for ROI, revenue, retention, or savings. Operational measures and the method for comparing results with the baseline are agreed.
  3. 3. Implementation planning needs to cover users, testing, acceptance, failure handling, and maintenance. A demo alone does not establish production readiness.
  4. 4. Employment decisions remain subject to judgment by authorized people. Performance summaries support human consideration; they do not automatically determine promotion, compensation, or termination.
  5. 5. Account access, material rights, licenses, documentation, and code are different matters. Code is not included by default; supplying it requires a separate proposal if available and is not always possible. Moving a system requires assessment of access, compatibility, time, and cost.

Before You Decide

Questions before selecting a service.

Does our team need to understand AI before starting?

No. Start with the process you want to improve, sample tasks, frequency, and responsible people. I help translate these into technical requirements; your team still needs to supply and approve data and business decisions.

How much does AI Automation cost?

There is no fixed package price. A custom proposal reflects audit or implementation scope, data sources, integrations, access, testing, and support. Model/API, infrastructure, licensing, and maintenance costs are itemized according to the requirements and who pays them.

How long until the system is in use?

The schedule depends on data readiness, access, approvals, integration complexity, and test results. Stages and acceptance criteria are agreed in the proposal. Previous case results do not promise timing or financial impact for a new project.

Will this replace our team?

The aim is to assist with recurring work so the team can focus on judgment and actions requiring people. Role changes, employee data use, and employment decisions require company policies and assessment.

Is this a service from the company where you work?

No. This is an independent personal service discussed directly with me, separate from the company where I work as a Technical Lead.

Can every system be integrated, and will all data remain private?

Integration feasibility depends on APIs, permissions, licenses, data formats, and system capabilities. Data flows, processors, logs, retention, and access are discussed before implementation. On-premise or private cloud options can be assessed where appropriate, but do not automatically mean no third party processes any data. Confidentiality terms follow the agreement.

Next Step

Audit first.
Solutions after.

If your company is already serious about AI and automation but isn't sure which part is worth executing first - or is about to start and wants to begin in the right place - send these four things:

  1. 1. Company name and industry
  2. 2. Team size and a brief operational structure
  3. 3. Tools or systems you already use
  4. 4. The bottleneck or internal complaint that comes up most often

I will review the process outline, then we can discuss an audit scope. Do not send credentials, customer data, or confidential documents in the first message; anonymized examples are sufficient to start.

Dicky Ibrohim

DESIGN × ENGINEERING

From an idea to a part of your business.

Repetitive work taking up your team's time?

Describe the process slowing you down. We can discuss what to automate and where human review still matters.

Discuss your workflow
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