Cut SaaS Fees: Build Custom AI Software in SA

A lot of South African e-commerce and digital service businesses are paying a “convenience tax” every month: licensing fees for tools that only solve 70% of the job. The rest gets handled with spreadsheets, manual workarounds, and someone on the team who knows the process in their head. That gap is where margins quietly disappear.

The licensing problem gets sharper as you grow. Per-seat pricing creeps up, “advanced” features sit behind higher tiers, and you end up paying for three different products because no single platform fits your reality—local delivery rules, POPIA compliance, VAT, returns, call-centre workflows, and WhatsApp-heavy customer support.

Here’s the stance I’ve landed on after seeing how teams actually operate: if software is central to your customer experience or your unit economics, renting generic tools forever is a costly plan. Building your own software—especially with AI baked in—can reduce licensing spend and give you better customer engagement, faster operations, and a data advantage you don’t get from off-the-shelf products.

This post is part of our series on how AI is powering e-commerce and digital services in South Africa. The thread that runs through the whole series is simple: AI isn’t “extra.” It’s becoming the easiest way to run lean, respond faster, and personalise at scale.

Why licensing fees hurt more in e-commerce than you think

Licensing fees don’t just cost money—they shape how your business works. When your tooling is priced per agent, per store, per channel, and per integration, growth becomes more expensive than it should be.

The hidden cost: paying twice (or three times) for the same workflow

Most teams end up stacking tools:

• A helpdesk platform for email tickets
• A separate WhatsApp or social inbox tool
• A CRM add-on for customer history
• A returns platform
• A review tool
• A fraud tool
• A product content tool

Each one is “reasonable” on its own. Together they create duplicate data, duplicate processes, and subscription sprawl. Someone still exports CSVs. Someone still copy-pastes order numbers. Someone still tags conversations by hand.

The local reality: South African edge cases don’t fit global defaults

Global SaaS products are designed for the median customer. South African e-commerce has a different mix:

• Higher dependence on couriers with varied service quality
• A wide range of address quality and delivery instructions
• Customers who prefer WhatsApp-first support
• Basket sensitivity to delivery costs and returns friction
• Compliance expectations under POPIA

When your systems can’t model those specifics, you either change your operations to suit the tool (bad) or you layer workarounds (also bad). Custom software is often the cleaner answer.

Build vs buy in 2025: the smarter decision framework

“Build your own software” doesn’t mean rebuilding Shopify or a payment gateway. It means owning the parts that create differentiation and reduce operational cost.

Here’s a practical framework that works for South African digital businesses.

What you should almost always buy

Buy what’s commoditised, regulated, or infrastructure-heavy:

• Payments and tokenisation
• Identity verification (where required)
• Core e-commerce platform (for most retailers)
• Email delivery infrastructure
• Cloud hosting primitives

You’re not saving money by recreating these. You’re taking on risk.

What you should strongly consider building

Build the workflow that’s unique to your business and expensive to run manually:

• Customer service automation (triage, routing, suggested responses)
• Returns and refunds orchestration aligned to your policies
• Inventory and fulfilment decisioning (which warehouse, which courier, which packing rule)
• Personalisation logic across web, email, WhatsApp, and ads
• Internal dashboards that answer operational questions quickly

A simple rule: if you can describe the workflow on a whiteboard and it touches profit or retention, it’s a build candidate.

Where AI makes custom software cheaper (and more valuable)

AI reduces the cost of building and maintaining custom software because it shrinks the manual effort in both development and operations. It also makes the end product more useful—because you can automate decisions, not just store data.

AI use case 1: Support automation that fits your real channels

South African support teams often live in WhatsApp and email, with occasional voice escalations. A custom AI layer can:

• Classify incoming messages (delivery issue, refund request, product question)
• Extract order numbers even when customers type them inconsistently
• Suggest responses in your brand tone
• Detect urgency (fraud signals, chargeback risk, “cancel now” intent)
• Route to the right queue, not the default queue

This matters because every unnecessary handoff adds minutes, and minutes add headcount.

A useful internal metric: “minutes to first meaningful action.” If AI can cut that from 12 minutes to 3, your ticket capacity jumps without hiring.

AI use case 2: Product content that doesn’t sound generic

Many retailers still rely on supplier descriptions that are inconsistent and often duplicated across competitors. Custom tooling can generate:

• SEO-friendly product descriptions
• Attribute completion (materials, sizing, compatibility)
• Category-specific bullets
• On-site FAQs based on actual support queries

The advantage of building your own workflow is control: you can enforce rules like no unsupported claims, local sizing conventions, and POPIA-friendly data handling.

AI use case 3: Fraud and risk triage tuned to your patterns

Fraud tooling is a classic licensing sinkhole, and it’s rarely perfect out of the box. A custom risk layer can:

• Flag suspicious patterns (address reuse, velocity checks, mismatch signals)
• Auto-request extra verification only when needed
• Feed outcomes back into your model (approved, cancelled, chargeback)

You can still buy baseline fraud services, but own the decision logic that reflects your risk tolerance and customer experience.

AI use case 4: Smarter ops—courier selection and delivery promises

Customers don’t want “2–5 business days.” They want a believable promise. Custom software can combine:

• Historical courier performance by suburb
• Warehouse cut-off times
• Stock location
• Peak season load (yes, December is brutal)

…and produce delivery estimates that reduce “Where is my order?” contacts. That’s a direct cost saving.

A practical roadmap: how to build without creating a monster

The fastest way to fail at custom software is to start with a giant rebuild. The better way is to pick one high-friction workflow and ship value in weeks.

Step 1: Audit your licensing fees—and map them to workflows

Make a list of every tool and answer two questions:

1. What workflow does it support?
2. What breaks when the tool is removed?

You’ll usually find that 20% of tools drive 80% of value—and the rest are expensive glue.

Step 2: Choose a “thin slice” you can ship in 30–45 days

Good thin slices are narrow but high-volume:

• Auto-triage + routing for customer messages
• Returns portal with automated policy checks
• Unified customer timeline (orders + tickets + messages)

If you can’t explain the first release in one sentence, it’s too big.

Step 3: Build around your data, not the vendor’s data model

Custom software wins when it becomes your operational memory:

• Orders, fulfilment events, courier scans
• Customer conversations across channels
• Refunds, returns, replacements
• Product attributes and content history

This is also where AI performs better—models are only as useful as the context you provide.

Step 4: Put POPIA and security into the design, not the backlog

If you’re using AI for customer engagement, you’re handling personal info. Treat this as a baseline:

• Minimise stored personal data
• Role-based access for staff
• Audit trails for sensitive actions (refunds, address changes)
• Clear retention rules

You can build powerful systems without turning your database into a liability.

Step 5: Measure outcomes like an operator, not a technologist

Track a small set of numbers that reflect profit and customer experience:

• Cost per resolved ticket
• Refund cycle time
• Return-to-restock time
• Contact rate per 100 orders
• Repeat purchase rate (30/60/90 days)

AI projects that don’t move these are expensive hobbies.

Common objections (and the honest answers)

“Building is too expensive for us.”

Building everything is expensive. Building the right 1–2 workflows is often cheaper than 24 months of licensing. The best ROI usually comes from replacing manual work and tool overlap.

“We don’t have the in-house skills.”

You don’t need a 20-person engineering team. You do need:

• A product owner who understands operations
• A senior engineer/architect (even fractional)
• A reliable delivery partner or small dev team
• Someone accountable for data quality

I’ve found that skills gaps are manageable if scope is tight and success metrics are clear.

“AI feels risky—what if it says the wrong thing?”

Valid concern. The fix is design:

• Use AI to suggest, not auto-send, in early phases
• Lock AI responses to approved policy and knowledge sources
• Add guardrails (no financial promises, no personal data leakage)
• Log decisions and review failures weekly

Treat AI like a junior team member: helpful, fast, and supervised.

What this means for SA e-commerce teams in 2026

The companies winning in South Africa aren’t the ones with the most software—they’re the ones with the least waste. Custom AI-powered software is becoming the practical way to reduce licensing fees while improving customer engagement and operational speed.

If you’re planning for 2026 growth, pick one area where licensing and manual work are stacking up—support, returns, product content, fulfilment decisioning—and build a focused internal product that plugs into what you already use.

Start small, measure hard, and keep the software tied to outcomes. Could AI reduce your software licensing costs? Yes—when it’s paired with custom workflows that match how your business actually runs. The real question is: which workflow would you stop renting first?