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QLTech
Applied AI

AI Implementation & Integration

Most AI pilots stall because nobody scoped the use case, wired it into real data, or measured whether it worked. We implement AI the way we build everything else: pick one measurable problem, prove it on your data, then take it to production with an evaluation harness, guardrails, monitoring and a cost model you can defend to the board.

Sound familiar?

The situations we get called into

01

The pilot never left evaluation

A demo impressed everyone six months ago. It still has no owner, no metric and no path to production.

02

The assistant answers confidently and wrongly

No retrieval over your real data, no evaluation set, no guardrails. Trust evaporated after the first bad answer in front of a customer.

03

Costs are a surprise every month

Token spend nobody forecast, no caching, no model routing, and no telemetry to explain the bill.

04

Legal has questions

Where does the data go, who can see it, and what does POPIA say? Nobody wrote it down before the pilot started.

How it works

From first call to handover

  1. 1

    Discovery

    A short, fixed-price engagement to pick one narrow, measurable use case, assess your data, and agree on what success looks like in numbers.

  2. 2

    Proof of value

    A working system on your real data with an evaluation harness, so you know how often it is right before anyone depends on it.

  3. 3

    Production

    Guardrails, human-in-the-loop where it matters, monitoring, cost telemetry, and integration into the systems your team already uses.

  4. 4

    Operate and expand

    Track quality and cost over time, tune prompts and models as they change, and take the next use case from the backlog.

What's included

Use-case discovery and ROI scoping
LLM integration: Claude, OpenAI, Gemini, Amazon Bedrock
RAG chatbots over your company documents and data
AI agents for business automation, with human-in-the-loop controls
Document processing, classification and extraction
Evaluation harnesses, guardrails and cost telemetry
POPIA and GDPR-aware data handling
AI strategy and roadmap consulting
What you get

What you walk away with

One use case, measured

A scoped problem with a baseline and a target, not a slide about "AI transformation".

An evaluation harness

A test set and scoring you can rerun every time a prompt, model or document source changes.

A cost model

Per-request and monthly cost projections, with caching and model routing where they pay off.

Production integration

Wired into your CRM, ERP, ticketing or internal tools, with access control and audit logging.

Technologies we use

  • Claude
  • OpenAI
  • Gemini
  • Amazon Bedrock
  • LangChain
  • pgvector
  • Python
  • TypeScript
  • AWS
FAQ

Questions we get asked

Which AI models and providers do you use?+

We are deliberately multi-provider: Claude, OpenAI, Gemini and Amazon Bedrock, chosen per use case on quality, cost, latency and data-residency needs. When residency matters we typically run models through Amazon Bedrock inside your AWS account. We do custom development around the models, not just prompt wrapping.

How long does an AI implementation take?+

Discovery is typically two weeks. A proof of value on your real data usually lands in six to ten weeks. Production timelines depend on integration depth, but you will have a written scope with dates before we start.

How do you handle POPIA and data privacy?+

Data minimisation first: we keep personal information out of prompts wherever the use case allows. We use provider settings and tiers that do not train on your data, choose regions deliberately, and put access control and audit logging around every AI feature. We work alongside your information officer or legal counsel rather than replacing them.

Can you integrate with the systems we already run?+

Yes. Integration is most of the work. We connect AI features to your existing databases, CRMs, document stores and internal tools rather than asking you to move to a new platform.

Are you an AI automation agency?+

In effect, yes, with one difference: we are a software engineering company first. We automate workflows with AI agents, document processing and RAG chatbots, and we build the integrations, data pipelines, monitoring and access control around them so the automation survives contact with your real systems and your auditors.

What does an AI project cost?+

Discovery is a fixed price. The proof of value and production phases are quoted from the discovery output, so the scope, team and price are written down before you commit. Use the proposal form and we will reply within three business days.

Ready to get started?

Send us a short brief. We'll reply with a written scope, team and price within three business days.