01
Boring tools, used carefully.
We don't chase frameworks. The stack moves when there's a real reason — better DX, lower cost, fewer bugs at 2am.
status // accepting new engagements — Q3 2026
AI-native softwarefor industries that actually ship.
Effekt Studios is a small engineering studio in South Africa. We build cross-platform mobile and web apps with deep AI integration, serverless backends, and multi-tenant white-label delivery — for finance, agriculture, security, and the trades. The kind of software that runs offline in a fruit orchard, syncs back up, and survives a Monday morning.
10+
apps in production
52
white-label tenants
6
industries served
ZA / ZW
markets
/ 01 — proof of life
Tell us what you're trying to build.
We'll mirror it back — scope, risks, a rough plan, and an honest take. Same thing we'd do on a discovery call. Costs you nothing.
describe-project.sh
0/2000
try one →
/ 01 — principles
Four working rules. They show up in every line we ship.
02
Production is the first milestone.
A demo that doesn't survive Monday morning is a liability. Everything we ship is signed, versioned, and traceable from the first build.
03
Errors are first-class.
Every error is copyable, reportable, and tagged with the phase that failed. If a customer can describe what went wrong, we can fix it.
04
Small team, no handoffs.
The person you brief is the person writing the code. No layers, no resourcing meetings, no missing context.
/ 02 — capabilities
Building AI-native apps, all the way to production.
We don't pitch end-to-end without meaning it. Every line below is running in something a customer pays for.
AI integration
We're an AI-native studio. Frontier large language models are wired into our products as first-class tools — extraction, summarisation, classification, conversational interfaces, structured data generation. Not chatbot bolt-ons.
- ▹Provider-agnostic: Claude, GPT, Gemini, open models
- ▹Structured outputs with strict JSON schemas
- ▹Prompt caching + batch APIs for predictable costs
- ▹RAG, embeddings, semantic search, and tool use
- ▹PII stripping before model invocation (POPIA-aligned)
Cross-platform mobile
One codebase, every store. Built for the field — offline-first, multi-flavor, signed and shipped. We drop into native when the hardware or platform demands it, and stay cross-platform everywhere else.
- ▹Cross-platform (Flutter, React Native, Capacitor)
- ▹Native Android & iOS where it counts
- ▹Offline-first with conflict-aware sync
- ▹Signed CI/CD pipelines, store-ready releases
Web platforms & admin consoles
Customer-facing web apps and the operations dashboards behind them. Server-rendered for SEO when it matters, edge-deployed for speed everywhere.
- ▹Shared codebase with mobile when it makes sense
- ▹Server-rendered React for marketing & SEO surfaces
- ▹Edge-deployed for low-latency global access
- ▹Role-based access (Owner → Manager → Admin → User)
Serverless backends
Cloud-managed databases, serverless functions, and edge compute. Tuned for predictable bills, regulator-grade data handling, and AI-call orchestration with retries and observability.
- ▹Document store schema design with hierarchical queries
- ▹Serverless functions (Node + Python) with phase-tracked errors
- ▹AES-256 field-level encryption for PII
- ▹Audit logging on every write that matters
Multi-tenant & white-label
One codebase, many brands. Bundle IDs, cloud project configs, theming, feature flags, store metadata — managed as data, not as a fork-per-customer. Per-tenant model selection and AI cost accounting baked in.
- ▹52-tenant deployments shipping today
- ▹Build flavors, dynamic theming, per-tenant assets
- ▹Per-tenant cloud projects & analytics isolation
- ▹White-label store listings (Play, App Store, Huawei AppGallery)
Hardware & integrations
When the app needs to talk to the world — RFID readers, biometric scanners, SIP phones, payment terminals, gate controllers — we handle the protocol layer.
- ▹UHF RFID via UART, BLE peripherals
- ▹Biometric SDKs (fingerprint / face)
- ▹Realtime: WebRTC, SIP / VoIP
- ▹Messaging: WhatsApp Business API, email, SMS
Compliance & operability
Software that lives in regulated industries needs to behave. We design for POPIA, SANS-grade documentation, and 2am-incident-readable logs — including AI inference logs you can audit.
- ▹POPIA-aligned data handling & retention
- ▹Industry-spec generators (e.g. SANS 10142-1)
- ▹Crash reporting + cloud-side error logging
- ▹Copyable, reportable errors at every surface
Engagement model
We work like an internal team, not an outsourced one. One feature at a time, deployed, measured, then the next. AI features ship behind flags so you can A/B them.
- ▹Discovery → prototype → production in weeks, not quarters
- ▹Direct line to the person writing the code
- ▹Versioned releases, every build traceable
- ▹Handover or long-term partnership, your call
/ 03 — stack
The toolkit, declared.
We pick boring, durable tools and use them well. The list moves when there's a reason — not when there's a tweet.
ai integration
Claude·GPT·Gemini·Open models·Prompt caching·Batch API·Structured outputs·Embeddings & RAG
cross-platform
Flutter·React Native·Capacitor·Native iOS·Native Android
web
React·Next.js·TypeScript·Tailwind
serverless
Firebase·Cloud Functions·Cloud Run·Edge compute·Node.js·Python
data
Firestore·PostgreSQL·SQLite·Vector stores
realtime & comms
WebRTC·SIP / VoIP·Push notifications·WhatsApp Business API
hardware
UHF RFID (UART)·BLE·Biometrics·GIS / mapping
devops
CI/CD pipelines·Store deployment·Crash reporting·Structured logging
/ 04 — how we work
Discovery to production, narrated honestly.
01
Discovery
We map the problem with you — users, integrations, regulatory edges, the existing tech you can't throw away. One week, fixed scope.
02
Prototype
A real, runnable thing — not a Figma. Two to four weeks. End of this phase you can put it in front of customers.
03
Production
Iterative builds, dated versions, every release tagged and traceable. We deploy when it's worth deploying, not on a calendar.
04
Operate
Crash reports, audit logs, customer support escalation paths. Optional retainer or clean handover to your team.
/ 06 — questions
Things people ask before getting in touch.
If yours isn't here, the answer to "can you build that?" is probably yes — write to franco@effektstudios.com.
Do you build apps with AI integrated?
Yes — that's our specialty. We integrate frontier large language models into mobile and web apps for extraction, summarisation, classification, conversational interfaces, and structured data generation. We've shipped LLM features into financial advisory tools, agricultural assessment apps, and customer-facing chat surfaces, all in production.
Which AI providers do you work with?
We're provider-agnostic and pick the model per task. We work across all the frontier providers (Claude, GPT, Gemini) and with open models when a project demands self-hosting or stricter data residency. For cost control we lean on prompt caching, batch APIs, and tiered model selection — fast cheap models for high-volume calls, frontier models for the hardest reasoning.
Can you integrate AI into an existing app?
Yes. A common engagement is a discovery week where we map your existing app, identify two or three high-leverage AI features, and ship them as a first phase. We don't require you to rewrite anything — we build integration layers that sit alongside your existing code.
What does it cost to build an AI-powered app?
It depends on scope, but typical engagements range from a 4-week prototype (~R150–250k) to a multi-month production build (R500k+). We use prompt caching, batch APIs, and on-device fallbacks to keep ongoing inference costs low — usually a few rand per active user per month.
Where are you based and who do you work with?
We're based in South Africa and work in GMT+2. Our clients are primarily South African and Zimbabwean — finance, agriculture, security, hospitality, trades — but we work remotely with anyone, with overlap-friendly hours for Europe, the Middle East, and the eastern US.
How is your AI work different from a generic dev shop?
We treat LLMs as a tool, not a feature. That means: structured outputs with strict schemas (no string-parsing hacks), prompt caching to keep inference cheap, on-device fallbacks where latency matters, and POPIA-aligned data handling for regulated industries. We've shipped AI features into production at scale, not as demos.
What's the typical timeline from kick-off to live?
Discovery week one. Runnable prototype by week four. First production deployment usually inside eight to twelve weeks. We ship one feature at a time — each version is dated, tagged, and traceable, so you always know what's in production.
Do you sign NDAs and handle sensitive data?
Yes to NDAs. For data, we design POPIA-aligned systems with AES-256 field-level encryption for PII, audit logging on every meaningful write, and role-based access control. For AI calls, we strip or hash PII before any model invocation and never use customer data for training.
/ 05 — contact
Let's build
something that ships.
Tell us what you're trying to do and where it's stuck. We'll come back with an honest answer — even if that answer is "you don't need us."
contact.sh