Service
LLM Application Development
Seditio Asia builds applications on large language models for Asia-Pacific organisations — assistants, retrieval-augmented search, document intelligence and LLM-powered products — engineering the retrieval, evaluation and cost controls that separate production systems from prototypes.
Large language models are a new kind of computing primitive: software that works with meaning rather than only structure. Building real applications on that primitive is its own engineering discipline — prompt and context design, retrieval over your data, structured outputs downstream systems can consume, evaluation that catches regressions, and cost management at production volumes. Teams that treat an LLM as just another API tend to discover these layers in front of their users.
Seditio builds LLM applications as products with commercial consequences. SectorSift, the B2B prospecting platform developed by our team through NDRC and Enterprise Ireland programmes, combines LLMs with Google APIs and proprietary ranking at its core. Clear Talent's Ava assistant applies LLM capability to performance management, and Biologit applies AI screening across 120 million-plus medical abstracts. LLM integration, RAG and agentic frameworks sit in our core technology stack, alongside the TypeScript and Python engineering that surrounds them.
What we build on LLMs
An LLM application is mostly conventional software with a reasoning engine at a carefully chosen point. We build the whole system: the interface and workflow around the model, the data pipelines feeding its context, the retrieval layer grounding its answers, and the integrations carrying its output into your systems of record.
- Conversational assistants and copilots over business data
- Retrieval-augmented generation (RAG) applications with source citation
- Document analysis, extraction and summarisation products
- LLM-powered search, ranking and classification systems
- Structured-output pipelines feeding downstream applications and APIs
- Full SaaS products where LLM capability is the core value
The production disciplines that make LLM apps dependable
Four disciplines separate an LLM application you can sell from one you can only demo. Context engineering: getting the right data in front of the model, which usually matters more than the model choice. Evaluation: automated test suites over real cases, so quality is a measured number before and after every change. Output contracts: structured, validated responses that downstream code can trust. And cost architecture: model tiering, caching and batching that keep unit economics viable as usage grows.
We build all four in from the first sprint, on model-agnostic foundations — so when providers change prices or a better model ships, you switch behind an abstraction rather than rebuilding. It is the same senior-reviewed, cloud-native engineering standard we apply across our platforms.
LLM applications for Asia-Pacific users and data
LLM applications serving Asia-Pacific must perform beyond English: retrieval tuned for multilingual documents, prompts and evaluation sets covering the languages your customers actually write in, and latency acceptable from regional locations. Data governance matters equally — we deploy within appropriate cloud regions and select providers whose data-handling terms satisfy the residency and confidentiality expectations of markets like Singapore and Australia. Our Cebu engineering base keeps iteration cycles inside the region's working day.
Frequently asked questions
- Which LLM should we build on?
- Usually more than one. Different models lead on reasoning quality, speed, language coverage and price, and the rankings shift quarterly — so we benchmark candidates against your actual use cases during discovery and architect the application so the model is a swappable component. The durable investment is your data, retrieval and evaluation layers, not any single provider.
- Do we need to fine-tune a model on our data?
- Usually not as a first step. Retrieval-augmented generation over your documents delivers most of the accuracy gain at far lower cost and complexity, keeps knowledge current without retraining, and makes answers traceable to sources. Fine-tuning earns its place for specialised tone, format or classification tasks — and we will tell you if your case genuinely warrants it.
- How do you keep LLM running costs under control?
- Cost is an architectural decision. We tier requests so cheaper models handle routine work and stronger models handle hard cases, cache repeated computation, batch where latency allows, and monitor per-feature unit costs from launch. Clients see exactly what each capability costs to run, so pricing and scaling decisions rest on data.
- Can you add LLM features to our existing application?
- Yes — much of our LLM work is integration into products that already exist, alongside building LLM-native systems from scratch. We work with your codebase and cloud environment, and design the feature so it degrades gracefully if the model layer is ever slow or unavailable.
Related services
Build your LLM application on production-grade foundations
From first use case to a system your customers rely on — engineered by a team with LLM products live in the market.
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