Zaply
Zaply
Services · AI & Data

ChatGPT is generic. Your business isn't.

Off-the-shelf AI doesn't know your products, your tone, your history, or your domain. We build LLM applications that are trained on your data, speak your language, and produce outputs that fit directly into your workflows.

Talk to the teamModel-agnostic · GPT-4, Claude, Llama, Mistral

01What we build

Applications that know your domain

  • Custom LLM applications

    Applications on GPT, Claude, Llama, Mistral or other models, chosen by use case, latency, data privacy and cost.

  • RAG systems (Retrieval-Augmented Generation)

    Your documents, manuals and policies become searchable. Answers come from your content and include the source so they can be checked.

  • Fine-tuned domain models

    When a general model isn't enough, we fine-tune it on your historical data so it uses your terminology, tone and formats.

  • Structured output & workflow integration

    Each application produces structured output (JSON, forms, CRM updates, classified tickets) that connects directly to your systems.

02Architecture

How we choose the architecture

Each approach has different costs and limits. We choose based on the task, the available data and the budget.

  • Prompt Engineering

    Best when: Task is well-defined and consistent

    Fastest to build · Works within model knowledge cutoff · No custom data needed

  • RAG

    Best when: You need answers from your own documents or database

    Answers from your sources · Updates when documents change · No fine-tuning required

  • Fine-Tuning

    Best when: You need the model to behave consistently in a specialized domain

    Best for tone, format, and domain accuracy · Requires labeled training data

  • Agentic + LLM

    Best when: The task requires multi-step reasoning and actions

    Handles multi-step tasks · Higher complexity · Requires full observability

03How it works

From use case to deployed application.

  1. 01

    Define scope

    We identify the exact task, the data sources available, the required output format, and the success criteria before choosing any architecture.

  2. 02

    Choose architecture

    Based on your data, latency and budget, we recommend prompting, RAG, fine-tuning or agents, and explain why.

  3. 03

    Build & evaluate

    We build the application, run evals against your real data, measure accuracy and latency, and iterate until it meets your standards.

  4. 04

    Deploy & monitor

    Production deployment with full observability: response logging, quality dashboards, cost tracking, and a process for continuous improvement.

04Areas of application

Where does this apply in your organization?

Examples of applications by business area.

Customer Service

  • RAG-powered support knowledge base
  • Multilingual intent detection & routing
  • AI agent for multi-step ticket resolution

Sales & Marketing

  • Draft content for SEO and social media
  • Personalised outreach sequences
  • Quotes generated from the product catalogue

Document Management

  • Contract drafting & clause analysis
  • Multi-document Q&A and synthesis
  • Regulatory compliance scanning

IT & DevOps

  • Internal documentation RAG assistant
  • Automated code review & PR summaries
  • Incident postmortem generation

Finance & Accounting

  • Automated P&L narrative generation
  • Invoice & contract data extraction
  • Audit trail summarization

Human Resources

  • Policy handbook Q&A assistant
  • Job description & interview kit generation
  • Performance review synthesis

Have a use case in mind?

Tell us the problem and we'll recommend an architecture with an estimate of what it takes to build.

Start the conversation