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Metevix

02 — Automate · AI Integration

AI inside the product andprocesses you already have

The most valuable AI is rarely a new app. It is a capability added to the tools your customers and team already use — answering, classifying, extracting and drafting in the flow of work.

Explore AI solutionsHow it works

In this service

  • Retrieval-augmented generation
  • Classification & routing
  • Extraction
  • Drafting & summarisation
  • Semantic search

The problem

Demos are easy. Production AI is not.

A prototype built in an afternoon rarely survives real data. Accuracy drops on edge cases, costs spike with usage, sensitive data leaks into prompts and nobody can tell whether the model is getting better or worse.

  • Impressive prototypes that never reach production
  • Unclear accuracy and no evaluation process
  • Concerns about data privacy and vendor lock-in
  • Unpredictable per-request costs

Our approach

Engineering discipline for AI features

We pick the use cases where AI measurably helps, ground models in your own data with retrieval, build evaluation sets before shipping and design the fallback for when the model is unsure. Provider-agnostic architecture keeps you free to switch models as they improve.

  • Measured quality
  • Private by design
  • No lock-in
  • Controlled costs

Capabilities

What we deliver

Integrate large language models into existing products and processes — search, summarisation, classification, drafting and extraction — with guardrails and measurable quality.
  • 01

    Retrieval-augmented generation

    Answers grounded in your documents, knowledge base and product data.

  • 02

    Classification & routing

    Categorise tickets, emails, leads and documents automatically.

  • 03

    Extraction

    Turn PDFs, forms and messages into structured data.

  • 04

    Drafting & summarisation

    Assist your team with first drafts, summaries and replies.

  • 05

    Semantic search

    Search that understands meaning, not just keywords.

  • 06

    Evaluation & monitoring

    Test sets, quality metrics, cost tracking and human review loops.

How it works

A clear pathfrom brief to results.

Every engagement follows a defined sequence with a visible output at each step.
  1. 1

    Use-case scoring

    Rank opportunities by value, feasibility and risk.

  2. 2

    Data & grounding

    Prepare sources, permissions and retrieval pipelines.

  3. 3

    Prototype & evaluate

    Measure quality against a test set before production.

  4. 4

    Ship with guardrails

    Fallbacks, human review, logging and cost controls.

See it working

An AI workflow,step by step.

Pick a scenario and click through each stage to see what the AI does, which systems it touches and where people stay in control.
  1. 01UserMessage, form or event
  2. 02AI AgentUnderstands intent
  3. 03Decision EngineRules + scoring
  4. 04AutomationRuns the workflow
  5. 05CRMRecords & context
  6. 06Follow-upEmail · WhatsApp · tasks
  7. 07ConversionCustomer
A user interaction is interpreted by an AI agent, evaluated by a decision engine, executed by automation, recorded in the CRM, followed up automatically and converted into a customer.

Interactive demo

See an AI workflow step by step

Every enquiry answered, qualified and routed to sales in minutes.

Step 1 of 7

Website Visitor

A visitor lands on a service page from search and opens the assistant.

WebsiteAnalytics
// example data (illustrative)
page: "/services/seo"
source: "organic"
intent: "pricing question"

Technology

Tools we use

  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Open-source models
  • Vector databases
  • PostgreSQL + pgvector
  • Python
  • TypeScript

Use cases

Where it makesthe biggest difference

  • 01

    Support knowledge assistant

    Agents get suggested answers with sources from your help centre.

  • 02

    Document intake

    Invoices, applications or medical forms extracted into your system.

  • 03

    Product search

    Customers describe what they need in plain language and find it.

Benefits

Why it mattersto the business

  • Measured quality

    Evaluation sets show accuracy before and after every change.

  • Private by design

    Data minimisation, access control and provider data-retention settings.

  • No lock-in

    Swap model providers without rewriting the product.

  • Controlled costs

    Caching, model routing and usage limits.

FAQ

AI Integrationquestions

Is our data used to train the models?

We configure enterprise and API tiers where providers contractually do not train on your data, minimise what is sent in prompts and can use self-hosted open-source models where required.

Which model do you use?

Whichever performs best on your evaluation set at an acceptable cost. We design integrations so the model can be changed as the market evolves.

What if the AI gets it wrong?

Every integration has a defined failure mode: confidence thresholds, citations, human review or a safe fallback. We design for the wrong answer, not just the right one.

Works well with

Related services

Next step

Where could AI remove friction in your product?

Tell us about your goals. We'll reply with clear next steps — no obligation.

Explore AI solutions