For AI and machine learning, do you need internal data first?

Usually no. Applied AI integration can start from an existing model interface and a clear workflow. For custom AI, machine learning, or native LLMs, evaluation is clearer when internal data, documents, SOPs, case examples, targets, and usage boundaries are agreed from the start.

What most affects web systems engineering cost?

Website cost is most affected by page count, design complexity, CMS or admin-panel needs, copywriting, SEO, integrations, forms, payments, security, and timeline targets. Simple websites can be built faster, while business websites with special features need a more detailed scope.

When is a desktop systems engineering more suitable than a website?

Desktop applications are more suitable when a system must run reliably on local computers, use special devices, access local files, operate on an internal network, require specific performance, or remain usable when internet access is limited.

Do mobile and field systems need to be built for Android and iOS at the same time?

Not necessarily. Platform priority can be determined by the majority of users, budget, release target, and required device features. A project can start from Android, iOS, PWA, or a cross-platform approach depending on business needs.

What is included in routine maintenance?

Routine maintenance can include backups, minor updates, security patches, basic monitoring, hosting or server checks, small bug fixes, and system condition reports. New feature development is usually discussed separately as enhancement or reserved engineering work.

When should a company use a reserved engineering capacity?

Reserved engineering capacity fits when a system is already running but still needs feature updates, technical evaluation, performance optimization, consultation, work prioritization, and ongoing roadmap development without starting a new project from zero each month.

How quickly can an issue be investigated through troubleshooter service?

Investigation can begin after access, issue chronology, screenshots, logs, and reproduction steps are available. Handling usually starts remotely to find the root cause, stabilize the issue, and provide further repair recommendations if needed.

Can technical continuity be done onsite and remotely?

Yes. Remote support fits system configuration, user assistance, system checks, and technical coordination. Onsite support can be discussed for device, network, installation, operations training, or issues that require a technician to be present.

Can engineering scope be adjusted to requirements?

Yes. Scopes on this page are initial estimates. Final scope and budget depend on service type, feature count, integrations, data, user roles, AI needs, infrastructure, security, and implementation targets.

How long does AI and machine learning work take?

API-based applied AI integration usually starts from 3-8 weeks. Internal-data custom AI, RAG, light fine-tuning, evaluation operational intelligence interfaces, or workflow integrations generally take 2-4 months. Native LLMs, large-model training, security evaluation, GPU deployment, and MLOps can take 4-12 months or more.

What is the difference between applied AI integration, custom AI, and native LLM?

Applied AI integration connects model capability to governed workflows, data boundaries, and operational review. Custom AI adapts open-source or existing models with data, SOPs, documents, and business workflows through RAG, fine-tuning, instruction tuning, embeddings, and system integration. Native LLM work covers model ecosystem control across datasets, training pipelines, inference servers, deployment, security, observability, and cost optimization.

Is project and client data confidentiality protected?

Yes. Project information, internal needs, system access, datasets, and work files are handled professionally. For sensitive projects, access mechanisms, data restrictions, work documentation, and supporting agreements can be discussed from the start.

Can every type of AI project be handled?

Not all. PT XIPTOR SOFTWARE SERVICE only accepts projects that are legal, safe, and have a clear intended use. The company refuses malware, phishing, spyware, fraud automation, data theft, system manipulation, or technology that could harm others.

AI & Machine Learning Solutions | PT Xiptor Software Service

Training LLM workspace and monitoring dashboard for PT XIPTOR SOFTWARE SERVICE

Category 01

AI & Machine Learning

AI & machine learning solutions for business, from applied model integration and internal-data custom LLMs to native LLMs with scoped control over data, model, and infrastructure.

User

App / Model Workflow Frontend + Backend

Provider AI

Claim: must be positioned as an API/provider-based AI solution, not as a proprietary AI model.

Difficulty 1 / 5

User

App + RAG Knowledge Base

Custom LLM

The model is adapted to documents, SOPs, databases, and business workflows so output becomes more accurate and relevant.

Estimated timeline: 1-2 months.

Claim: may be positioned as brand/company-owned AI while still disclosing the base model used, such as Llama or Mistral for conversational model execution, Mistral for lightweight efficient use, Code Llama or StarCoder for coding, and Ollama + Llama/Mistral models for fast local experiments.

Difficulty 3 / 5

Data

Training Cluster Governance + Serving

Native LLM

Dedicated model with scoped control over data, training, infrastructure, security, deployment, and monitoring.

Estimated timeline: 2-6 months.

Claim: can be claimed as the user/buyer's native LLM, with license, model weights, data, and usage rights governed by the project contract.

Difficulty 5 / 5

APPLIED AI INTEGRATION

API-based AI product layer for fast launch, with secure prompt routing, usage controls, and measurable business workflows.

Provider orchestration for OpenAI, Claude, Gemini, DeepSeek, or other AI APIs with fallback-ready adapter layer
Secure prompt, context, and tool-calling workflow designed around one business process
Production UI, backend endpoint, rate limiting, API key isolation, and audit logging
Knowledge ingestion for curated FAQs, SOPs, product docs, or support scripts
Analytics dashboard for usage, cost, failed intents, and improvement backlog
Launch checklist, model-disclosure copy, and technical handover documentation

USD 450.000 - 1.500.000 IDR 7.500.000.000 - 25.800.000.000 Request Scope

Featured Product

CUSTOM LLM

A company-specific AI system trained around internal documents, workflows, policies, and reviewer feedback.

Data audit, taxonomy, retrieval strategy, and evaluation set from internal documents
RAG or fine-tuning pipeline with vector database, ranking logic, and prompt governance
Role-based workspace for users, reviewers, admins, and content maintainers
Guardrails for hallucination reduction, source citation, PII handling, and escalation
Monitoring dashboard for quality, latency, cost, drift, and reviewer feedback
Integration API for CRM, ERP, ticketing, document storage, or internal portals

USD 6.000.000 - 24.000.000 IDR 103.800.000.000 - 415.200.000.000 Request Scope

NATIVE LLM

Strategic native model program with dedicated data, training, serving, governance, and long-term ownership controls.

Proprietary model roadmap covering tokenizer, dataset governance, training schedule, and license boundaries
Secure data pipeline for collection, deduplication, filtering, redaction, and lineage tracking
Dedicated training or fine-tuning infrastructure with experiment tracking and model registry
Evaluation suite for accuracy, safety, latency, domain reasoning, and release acceptance
Model serving stack with access control, observability, rollback, and capacity planning
Strategic handover for model weights, documentation, IP ownership, and ongoing MLOps

USD 45.000.000 - 120.000.000 IDR 777.000.000.000 - 2.076.000.000.000 Request Scope