We Have Already Built AI Into Production -
Not a Roadmap, a Track Record Track Record
Stacknize does not pitch AI as an add-on. It is part of how we build platforms. Our mHealth platform runs Google MedGemma for live clinical consultation summaries. Our AI Call Centre Agent handles real customer calls over SIP. These are not prototypes — they are production capabilities built alongside the platforms they power. If your product needs AI that actually works in the real world, that is how we approach it.
The Distinction That Matters
Most AI consulting begins and ends with a proof of concept. A demo that works in a controlled environment, with clean data, no edge cases, and nobody depending on it. The distance between that
and production AI — with real users, messy data, latency constraints, and business logic — is where most implementations fail.
Stacknize approaches AI differently because we are a platform engineering company first. We build AI capabilities into platforms we are already engineering, which means we solve the infrastructure,
integration, and reliability challenges that purely AI-focused consultancies often do not touch. Our AI work is already live in production: a medically-specialised LLM generating clinical consultation summaries; a voice AI agent taking real inbound calls over SIP telephony. These are the reference points we build from.
Years of
Platform Engineering
Platform Engineering
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Projects Delivered
Across Countries
Across Countries
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Global Production Footprint
Live mission-critical systems deployed across continents.
What We Do
Not services we offer — platforms we have engineered, deployed, and watched run in
production for real clients across three continents.
LLM Integration into Existing Products
- GPT-4o and Anthropic Claude integration - adding LLM-powered features into existing web and mobile platforms
- Domain-specific LLM selection - matching the right model to the use case (general vs. specialised, e.g., MedGemma for clinical)
- Prompt engineering and evaluation - systematic prompt design, testing, and optimisation for production reliability
- Output validation and guardrails - ensuring AI responses stay within defined business boundaries
RAG — Retrieval-Augmented Generation
- Knowledge base construction - transforming your product docs, policies, and data into a queryable vector store
- RAG pipeline design - embedding generation, semantic retrieval, context injection, and response synthesis
- Document processing - PDFs, structured data, and internal knowledge bases made AIsearchable
- Hallucination reduction - grounding LLM responses in your actual business rules and verified data
AI Agents & Workflow Automation
- Voice AI agents - AI that handles real phone calls over SIP, including our AI Call Centre Agent platform
- Task-oriented AI agents - LLM agents that can take actions, call APIs, and complete multi-step workflows autonomously
- Human-in-the-loop design - configuring when AI operates independently and when it escalates to a person
Domain-Specific & Specialised AI
- Medical AI - transforming your product docs, policies, and data into a queryable vector store
- Speech-to-text pipelines - real-time and batch STT optimised for specific languages, accents, and domains
- AI observability - monitoring AI feature performance, hallucination rates, cost tracking, and latency in production
Live AI in Our Platforms
Five engineering capabilities, each built over years of real production experience — not
assembled from a services catalogue.
01
MedGemma — mHealth
Google’s medically-specialised LLM generates structured clinical
consultation summaries and patient-friendly prescription explanations —
live in a production mHealth deployment in Africa.
02
AI Call Centre Agent
GPT-4o / Claude-powered voice agent handles real inbound calls over
SIP telephony — STT, LLM dialogue, TTS response — with human
handoff and full CRM integration. Production-ready.
03
AI-Powered IVR
Natural language IVR systems built on top of existing telecom voice
infrastructure — replacing rigid DTMF menus with LLM-driven
conversation. Built and deployable now.
Technology Stack
Three continents. Five platform domains. Real clients who trusted us with business-critical
systems. Here is what that produced.
LLMs
- GPT-4o (OpenAI)
- Claude (Anthropic)
- Google MedGemma
- Gemini
RAG
- LangChain
- LlamaIndex
- Pgvector
- Pinecone
- Weaviate
Voice AI
- OpenAI Whisper (STT)
- ElevenLabs / Azure TTS — over SIP / FreeSWITCH
Embedding
- OpenAI text-embedding-3
- Cohere Embed
Deployment
- AWS Lambda
- Docker
- FastAPI — low-latency AI inference endpoints
Evaluation
- LangSmith
- Custom eval harnesses for domain-specific accuracy testing
Who This is For
- Platform product teams that want to add LLM-powered features to an existing product — done properly
- Healthcare, telecom, or finance organisations needing domain-specific AI rather than generic chatbots
- Companies that have tried AI integration elsewhere and found the gap between demo and production too wide
- Engineering teams that need an AI integration partner with platform infrastructure depth, not just model expertise
Have a Specific AI Use Case in Mind?
Tell us what you are trying to solve. We will tell you whether it is the right use case for AI, which approach is most likely to reach production, and how we would build it.
Our Partner for
Software Innovation
