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SERVICES

Ourservices

From small LPs to full production builds — scale up as your needs evolve.
Each service ships with scope, duration and reference cases.

01
Duration ／ 2–4 weeks

Site & LP build

Corporate sites, hiring pages and service LPs — production-ready in as little as two weeks.
AI chat or inquiry automation can be layered in through the same pipeline.

Features
  • Responsive build on Next.js / React
  • Design, copy and engineering handled by one team
  • Contact forms, analytics tags and baseline SEO included
  • Optional AI chat and inquiry automation in the same pipeline
Who it's for
  • Startups shipping a service or refreshing brand under time pressure
  • Companies that want an AI-augmented LP live quickly
  • Teams refining conversion paths and SEO on an existing site
Scope
  • Information architecture and wireframes
  • Design comps and prototypes
  • Next.js implementation and deployment
  • Contact forms and analytics setup
Related cases
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02
Duration ／ ~1 month

AI chatbot

RAG-based chatbots that search across internal docs, FAQs and product data — covering customer auto-response and internal knowledge lookup.
Each deployment is tuned to your operational workflow.

Features
  • End-to-end ingestion, chunking and vector search
  • Cross-source retrieval — FAQs, policies, product masters
  • Human escalation, answer logs and ops dashboards
  • Flexible deployment on Azure, GCP or your own environment
Who it's for
  • Enterprises looking to streamline internal inquiries and policy lookups
  • EC or support teams shifting inbound questions toward auto-response
  • Teams validating RAG accuracy and operational fit in real settings
Scope
  • Requirements and data audit
  • RAG pipeline (ingestion, chunking, vector search)
  • Chat UI and human escalation flow
  • Answer-quality evaluation and ops dashboards
Related cases
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03
Duration ／ 1–2 months

PoC development

Validate the value and feasibility of an AI theme before committing to a full build.
RAG, process automation and LLM integration — scoped tightly to the operational reality.

Features
  • Tight scoping around themes where value is visible
  • Evaluation axes and a report that supports the go / no-go call
  • A working prototype plus decision material in 1–2 months
  • Seamless handoff into full build and operations
Who it's for
  • Enterprises sizing up AI impact before a full build
  • Teams testing LLM / RAG fit against real operations
  • Leadership prioritising AI initiatives across the organisation
Scope
  • PoC design and evaluation criteria
  • Prototype build (RAG / automation / LLM integration)
  • Accuracy and operational-fit evaluation
  • Issue mapping and roadmap toward full build
Related cases
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