Development andAI Integration for Business

Build AI solutions connected to your systems, data, documents, and business processes.

AI Agent, RAG, knowledge base, conversational AI, OCR, document intelligence, computer vision, and automation can be added to existing applications or built as part of new solutions.

Consult on AI Needs View Development Scope

AI That Works WithinBusiness Processes

Solutions are selected based on the work to be supported, available data, systems to connect, and the level of human control required.

AI Agent & Automation

Agents that read context, use tools, execute multi-step tasks, and hand off work to other systems with rules and approvals.

Workflow
Tool Calling
Approval
Audit Log

RAG & Knowledge Base

Turn SOPs, regulations, contracts, manuals, and internal knowledge into information that is easier to search, query, and reuse.

Document Search
Source Citation
Role Access
Knowledge Index

Conversational AI

Chatbots, voice assistants, and conversational interfaces for customers, partners, employees, and internal app users.

Web Chat
WhatsApp
Voice
Internal Assistant

Computer Vision & Document AI

OCR and vision models to read documents, extract data, classify files, recognize objects, and assist with visual inspection.

OCR
Extraction
Classification
Visual Check

Two Implementation Paths,One Operational Goal

AI can enhance existing applications or be developed alongside new systems per company needs.

Integrate AI into Existing Systems

Add AI capabilities without replacing the entire application that is part of operations.

ERP, POS, PMS, CRM, portals, and dashboards
API, webhook, database, file exchange, or message queue
Access rights and workflows follow the main system
Feasibility is assessed based on access and architecture

Build New AI Solutions per Your Needs

When no suitable system exists, applications, database, workflow, and AI can be developed as one solution.

Web, mobile, and management dashboards
Database, documents, users, and access rights
Workflow, approval, monitoring, and audit log
API and deployment infrastructure

Examples of AI inOperations

Implementation starts not from features but from problems, decisions, and work that needs to be faster or measurable.

Customer Service

Answer questions, find customer context, create tickets, and route cases to the right team.

Administrasi

Read files, fill in initial data, check completeness, and prepare summaries and draft documents.

Knowledge Management

Search SOPs, regulations, policies, and technical documents using natural questions.

Finance & Procurement

Extract invoices, compare documents, and assist with initial checks before user review.

Sales & CRM

Summarize interactions, group prospects, and prepare context for sales team follow-up.

Hospitality

Assist with guest questions, reservation info, service recommendations, and operational communication.

Retail & Multi-Outlet

Read transaction trends, detect anomalies, and prepare outlet performance summaries.

Public Services

Assist with regulation search, application classification, initial document checks, and information services.

Data and Knowledge Becomethe Foundation

AI solution quality is not determined by models alone. Documents, metadata, official sources, access rights, and evaluation data must be prepared so results are trustworthy and maintainable.

For RAG, answers can include document references so users know the information source used.

Source Inventory

Map documents, databases, applications, data owners, formats, and information versions.

Curation & Structure

Clean data, organize metadata, define official sources, and separate information by access level.

Retrieval & Context

Manage indexing, search, reranking, context, and knowledge updates in a measured way.

Evaluation Dataset

Use representative case examples to measure quality, consistency, and solution limits.

AI Stays WithinHuman Control

Automation level is adjusted to risk. Not all processes need or should run without user review.

Recommendations

AI provides summaries or suggestions, while decisions remain with the user.

Drafts for Review

AI prepares responses, reports, or draft documents that must be reviewed before use.

Approval Before Execution

Actions are only executed after approval from authorized parties.

Automated with Constraints

Certain tasks can run automatically as long as they meet defined rules and conditions.

Escalation to Users

Uncertain, sensitive, or out-of-scope cases are forwarded to responsible staff.

Security, Privacy, andAccess Management

Design considers what data is processed, processing location, who can access it, and how long information is retained.

Model and deployment choices adapt to data classification, performance, organizational policy, and project budget.

Authentication dan role-based access

Data separation by organization

Encryption at rest and in transit

Sensitive information filtering

Audit trail and controlled logging

Backup, recovery, and data retention

Usage and cost monitoring

Human approval untuk tindakan penting

Models and TechnologySelected per Needs

Solutions are not locked to one provider or stack. Selection considers accuracy, language, speed, privacy, cost, integration, and deployment environment.

Large Language Model

OpenAIAnthropicGoogleOpen SourceLocal Model

AI & Machine Learning

PythonPyTorchTensorFlowVisionOCRReranker

Backend & Orchestration

GolangPythonNode.jsPHPRustWorkflow Engine

Web & Mobile

ReactNext.jsVueFlutterKotlinPWA

Data & Retrieval

PostgreSQLMySQLRedisObject StorageVector Database

Infrastructure

CloudVPSLocal ServerPrivate EnvironmentHybrid

Commercial models can be combined with open-source or local models per security level, capability, and operational cost needs.

Beyond JustConnecting AI APIs

Model integration is only one part. Production systems still need workflow, context, validation, access, evaluation, monitoring, interfaces, and failure handling.

The goal is not to produce answers that merely look smart, but to build systems that are usable, controllable, evaluable, and extendable.

Business processes

Data sources

Access rights

Workflow & approval

Output validation

Failure handling

Cost monitoring

Quality evaluation

User interface

Stable infrastructure

System Experience Becomesthe AI Integration Foundation

AI implementation requires understanding applications, databases, workflows, users, access rights, and integrations - not just models and prompts.

Web · Mobile · ERP · Transaksi · Hospitality · Public Services

The Solunesia team has experience building operational systems, dashboards, databases, portals, mobile apps, and API integrations. This foundation helps place AI in the right processes and connect it to production environments.

WorkflowRole AccessDatabaseAPI IntegrationDashboardInfrastructureAudit Trail

AI portfolio items are only shown as AI projects when implementation is in use and verifiable. Other system projects remain evidence of integration and engineering capability.

View Related Projects

Scope is Based onProblems and Data Readiness

AI services do not use packages with fixed scope. Once needs are mapped, Solunesia will prepare the approach, proof of concept if needed, data requirements, timeline estimates, and implementation costs.

API, model, OCR, cloud, server, WhatsApp, phone, or third-party service costs are explained separately per project design.

Factors that influence scope

Goals and processes to automate
Systems to integrate
API availability
Data type and volume
Document condition
Number of users and access rights
AI models used
Real-time requirements
Quality and evaluation targets
Human review and approval
Deployment infrastructure
Usage monitoring

Development ProcessAI Solutions

Each stage checks feasibility, risk, data quality, and solution readiness before use in real operations.

Problem Mapping

Study processes, manual work, users, risks, and desired outcomes.

Data Assessment

Examine documents, databases, APIs, access rights, volume, quality, and infrastructure limits.

Solution Design

Prepare architecture, models, workflow, user controls, quality indicators, and deployment.

Proof of Concept

Test the most critical parts using representative sample data before production systems.

Development & Integration

Connect the solution to agreed applications, data, documents, channels, and systems.

Evaluation, Go-Live & Monitoring

Test real scenarios, launch gradually, then monitor quality, costs, and refinements.

FrequentlyAsked Questions

Brief answers to help assess readiness before consultation and needs mapping.

Can AI be added to existing applications?+

Yes. Integration can be done via API, webhook, database, file exchange, or other supported methods. Architecture, documentation, access, security, and system limits are examined first.

Does the company need to have large amounts of data?+

Not always. RAG can use curated document collections, while custom machine learning typically needs more data. Data quality, consistency, and relevance are often more important than quantity.

Are AI answers or actions always correct?+

No. That is why solutions need source references, validation, action constraints, evaluation datasets, human review, and escalation mechanisms per process risk level.

Must the solution use OpenAI?+

No. Models are selected based on language, accuracy, speed, privacy, cost, deployment, and organizational policy. Solutions can use commercial providers, open-source models, local models, or a combination.

How are AI usage costs calculated?+

Development costs and operational costs are different components. Operations can be affected by tokens, documents, requests, images, audio, storage, servers, and third-party services. Estimates are provided after usage patterns are mapped.

Share the Process You Want Supported

Describe manual work, documents, recurring questions, or systems you want to improve.

Assess Data, Risk & Integration

We help assess data sources, access, human controls, and technical feasibility.

Plan the Implementation Approach

Solution, proof of concept, scope, timeline, and costs are based on real needs.