Service
Generative AI Integration
Seditio Asia integrates generative AI — large language models, retrieval-augmented search and document intelligence — into existing products and business systems for Asia-Pacific organisations, engineering the data pipelines, guardrails and user experience that turn a model API into a dependable feature.
Calling a language-model API takes an afternoon. Integrating generative AI so that it is accurate on your data, safe in front of your customers, affordable at your volumes and maintainable by your team is a substantial engineering project — and it is the difference between an AI feature customers trust and one your support desk apologises for. Generative AI integration is that second discipline.
Our credentials are running systems. Biologit's platform applies AI screening across more than 120 million medical abstracts in a regulated pharmacovigilance context. SectorSift combines LLMs with Google APIs and proprietary ranking to qualify B2B prospects, and was developed through NDRC and Enterprise Ireland programmes. We bring the same architecture, evaluation and cost discipline from those platforms to integrations with clients' products across Asia-Pacific.
Where generative AI earns its place
The strongest integrations target work that is language-heavy, high-volume and currently manual. We help you identify those seams in your product or operations, then build the integration properly: grounding the model in your own data so answers are accurate, shaping outputs so they slot into existing workflows, and instrumenting quality from day one.
- In-product assistants and conversational interfaces over your data
- Retrieval-augmented search across documents, knowledge bases and records
- Document intelligence — extraction, classification and summarisation at scale
- Content and communication drafting with brand and compliance controls
- AI-assisted analysis and reporting inside existing dashboards
Integration engineering, not API decoration
A production integration has layers a demo never shows: data pipelines that keep the model's context current; retrieval systems tuned for your documents and languages; prompt and output contracts that survive model version changes; caching and batching that keep unit costs viable; and evaluation harnesses that tell you — with numbers — whether this month's system is better than last month's.
We also design for reversibility. Model providers, prices and capabilities are shifting quarterly, so we architect integrations behind clean abstractions that let you switch or mix providers without rewriting the product. Senior architects review every integration design, consistent with how we run our own platforms.
Generative AI for Asia-Pacific products and teams
Generative AI in this region has to handle what global demos gloss over: customers and documents in multiple languages, data-residency and confidentiality expectations that vary by market, and cost sensitivity at Asia-Pacific price points. We tune retrieval and prompts for the languages your business actually operates in, deploy within appropriate cloud regions, and engineer usage costs to fit regional unit economics — with a Cebu-based team working your time zone rather than answering overnight.
Frequently asked questions
- Can generative AI work accurately on our company's own data?
- Yes — that is precisely what retrieval-augmented generation is for. Instead of relying on what a model memorised in training, we ground its responses in your verified documents and records at query time, which sharply improves accuracy and makes answers traceable to sources. Building and tuning that retrieval layer is the core of most integrations we deliver.
- How do we stop the AI inventing things in front of customers?
- Hallucination is managed with engineering: retrieval grounding, constrained and structured outputs, confidence thresholds that route uncertain cases to humans, and automated evaluation against known-correct answers before every release. No system is perfect, so we also design the UX to show sources and make errors easy to catch and report.
- Will our data be used to train someone else's model?
- Not under the architectures we deploy. We select providers and configurations with appropriate data-handling terms, keep sensitive data within your cloud environment where required, and document the data flows so your security and compliance teams can verify exactly what leaves your perimeter and why.
- How long does a generative AI integration take?
- A focused first integration — one use case, grounded in your data, with evaluation in place — typically takes a small number of months rather than weeks, depending on data readiness and integration complexity. We scope precisely after discovery, and we will say plainly if your data needs preparation work first.
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Add generative AI your customers can actually rely on
Bring us the product or process you want to enhance. We will design an integration grounded in your data and engineered for production.
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