Put AI to work on your own data - either as a self-hosted model running entirely inside your infrastructure, or through the Claude and OpenAI APIs when you need frontier-grade quality. We help you pick the right trade-off and ship it to production.
Most AI projects stall at the same question: where does the data go? Sending internal documents, customer records, or contracts to a third-party API is not always acceptable - and for regulated organizations it may not be an option at all.
That is why we build on two foundations. Open-weight models such as Gemma, Qwen, Llama, and DeepSeek can run on your own server or private cloud through Ollama or vLLM, which means prompts and documents never leave your network. Where a task genuinely needs frontier-grade reasoning, the Claude or OpenAI API is the better tool. Many systems end up using both, routed by how sensitive the data is.
We are engineers first, so the work does not stop at a demo. We handle the integration, the retrieval layer, the cost controls, and the monitoring that turn a promising prototype into something your team can rely on every day.
AI development is the right fit when a repetitive, text-heavy process is consuming your team’s time, or when data sensitivity has blocked you from adopting AI so far. Consider this service when:
From the first use-case assessment through to a system running in production.
Open-weight models served from your own server or private cloud with Ollama or vLLM, sized to your hardware and integrated with your existing authentication.
Claude, OpenAI, or Gemini wired into your product with retries, fallbacks, rate limiting, and spend controls so a traffic spike never becomes a surprise invoice.
An assistant for your team, grounded in internal documents, SOPs, and archives through retrieval-augmented generation, with vector search and answers that cite the source they came from.
Need a chatbot for customers? AI ChatbotTurn invoices, contracts, forms, and scanned files into structured data your systems can act on, with human review where accuracy matters most.
Agents that call your internal APIs to complete real tasks - triaging tickets, drafting responses, summarizing meetings - inside the boundaries you define.
Test sets that measure output quality against real examples, plus guardrails, audit logging, and cost dashboards so you can see what the system is doing.
A path that proves value early instead of committing you to a long build up front.
We look at the actual workflow you want to improve, the data behind it, and what a good answer looks like - then say plainly whether AI is the right tool for it.
Self-hosted, API, or hybrid: we choose based on your data sensitivity, quality requirements, expected volume, and budget, and document the reasoning.
A working prototype on your real data, evaluated against a test set so the decision to continue rests on measured results rather than a demo.
Integration with your existing systems, authentication, and infrastructure, with the guardrails, logging, and cost controls a production system needs.
Ongoing quality measurement, tuning as usage patterns emerge, full documentation, and training so your team can operate and extend the system.
Open-weight models, managed APIs, and the infrastructure that runs them.
There is no single right answer - the choice depends on how sensitive your data is and how much reasoning quality the task demands.
| Self-HostedOpen-weight models on your own hardware or private cloud. | Managed APIFrontier models accessed through a provider API key. | HybridRequests routed by data sensitivity. | |
|---|---|---|---|
| Where the data goes | Never leaves your infrastructure | Sent to the provider (mitigated by zero-retention terms) | Sensitive data stays local, the rest goes to the API |
| Models | Gemma, Qwen, Llama, DeepSeek via Ollama or vLLM | Claude, GPT, Gemini | Both, behind one interface |
| Cost model | Upfront GPU investment, no per-token charges | Pay per token, no hardware to buy | Mixed, tuned to your traffic profile |
| Best suited for | Regulated data, internal documents, high request volume | Fast proof of concept, tasks needing the strongest reasoning | Most enterprises, once the first use case proves out |
With a self-hosted deployment, the model runs on your own server or private cloud and prompts, documents, and responses never leave your network - there is no external API call to make. This is what makes the approach workable for organizations that cannot share data with a third party, and it supports compliance with Indonesia’s Personal Data Protection Law (UU PDP No. 27/2022), though compliance always depends on your overall data governance, not the model alone.
Neither is better in general. Frontier models accessed by API still lead on complex reasoning, and they require no hardware. Open-weight models such as Gemma and Qwen keep your data in-house, remove per-token costs, and are more than capable for classification, extraction, summarization, and document Q&A. We assess your specific use case and recommend accordingly - often a hybrid, where sensitive requests stay local and the hardest ones go to an API.
It depends on the model size and how many concurrent users you expect. Smaller models in the 7-9 billion parameter range run on a single mid-range GPU and suit most internal use cases. Larger models need more GPU memory or several cards. We size the hardware during the assessment phase so you know the investment before committing, and a private cloud GPU instance is a common starting point when you would rather not buy hardware yet.
Yes. Most of our AI work is integration rather than a standalone product - connecting a model to your existing ERP, CRM, ticketing system, internal portal, or WhatsApp channel through their APIs, so the AI appears inside the tools your team already uses.
We implement spend limits, caching for repeated queries, routing that sends simpler tasks to cheaper or self-hosted models, and dashboards that show consumption per feature. Self-hosting the high-volume portion of your workload is often the single largest saving once usage becomes steady.
We deliberately start with a proof of concept on a single, well-defined use case so you can judge real output on your own data before committing to a full build. The timeline depends on the complexity of the use case and how ready your data is, both of which we scope during the assessment.
Practical guides from our engineering team on related topics.
AI Development10 min read
How AI reads invoices, receipts, ID cards, and forms and turns them into structured data, how OCR and large language models fit together, why validation and human review still matter, how to measure accuracy, and what to watch for with personal data.
AI Development9 min read
Picking a first use case that can succeed, checking whether the data is ready, choosing between an AI API and a self-hosted model, running a pilot with a test set, the cost components, and the risks to manage from the first week.
AI Development10 min read
How to run open-weight language models on your own servers with Ollama: when self-hosting makes sense, sizing GPU memory, Ollama versus vLLM, securing the API, and evaluating quality on your own data.
AI Development10 min read
How retrieval-augmented generation lets a chatbot answer from your own FAQs, catalogue, and policies: preparing documents, chunking, hybrid search, live system data, human handover, and measuring quality.
Tell us the process you want to improve and what your data looks like - we will tell you honestly whether AI is the right tool and which approach fits.
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