Your one-stop AI partner for business transformation: custom ML models, LLM applications, autonomous agents and document AI, from first prototype to production scale.
Trusted by teams worldwide
Navigating AI transformation takes specialised expertise. Six service lines cover every critical aspect of enterprise AI, from first strategy workshop to production operations.
The AI that pays for itself looks different in a hospital than it does on a production line. These are the six sectors we scope against most often, and every capability listed is something we have actually shipped rather than a slide we can produce on request.
Six engineering disciplines behind every solution we ship, and the tools we use for each.
Custom LLM applications with RAG pipelines, fine-tuning, evaluation harnesses and prompt infrastructure, grounded in your private data.
Multi-step reasoning agents that use tools, call APIs and operate inside your CRM/ERP, with planning loops and observability baked in.
Invoices, purchase orders and contracts read, validated and posted into your ERP, with an approval queue for anything that does not match.
Time-series forecasting, churn prediction, fraud detection and lead scoring. Classical ML where it beats LLMs, and we tell you which is which.
Real-time voice agents with sub-second latency. Multilingual ASR + TTS, IVR replacement and sentiment-aware support bots.
Model registries, evaluation pipelines, drift monitoring, cost optimisation and on-prem deployment. The 80% that decides if AI ships.
A short technical audit shows exactly where your data, systems and governance stand, so your first AI investment ships instead of stalling.
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Five phases take an idea to a model running in production. The risky parts get proven early, so nothing turns into a science project you cannot cancel.
We run a two-day workshop with your team to find the use cases worth funding, audit whether your data can actually support them, and agree the metric that decides success. You leave with a shortlist ranked by return, not by novelty.
Your data gets assessed, cleaned and shaped into feature pipelines you can rebuild yourself. We build the ground-truth set here too, because without one there is no honest way to say later whether the model improved.
Training, fine-tuning and evaluation run in the same cycle as the application around them, with a human in the loop reviewing output. Accuracy is tracked against the evaluation set from week one rather than reported at the end.
The model is wired into the systems it has to live with, through secure APIs or an event-driven path into your CRM, ERP or product. We test the failure paths deliberately: bad input, missing data, a provider outage mid-request.
Monitoring for quality and drift, retraining pipelines when results slip, and a dashboard that shows what the system is doing and what it costs. Models decay quietly, so the point is to notice before your customers do.
SaaS client: AI chatbot resolves 6 of 10 tickets with no human in the loop.
Voice AI receptionist answers in seconds, down from a 4-minute human queue.
Document-processing AI replaced 11 FTEs of manual extraction at a logistics firm.
Edge vision model running on shop-floor cameras at a manufacturing plant.
Real applications, real users, real numbers, across healthcare, mobility, delivery and education.
AI mood tracking, personalised CBT recommendations and clinician-reviewed wellness plans powered by custom NLP.
Real-time dispatch engine with surge prediction, smart driver allocation and ML-based ETA estimation.
Smart dispatch and route optimisation that cut delivery times by 40% across 200+ partner restaurants.
ML-based coach-student matching, personalised study plans and adaptive progress tracking for 10K+ students.
We are not tied to one provider. The model is chosen on evaluation results against your data, your latency ceiling and your residency rules, and it is swappable later because we keep the application layer independent of it.
Reasoning, extraction and agent workloads where breadth matters most.
Long-context analysis and tool use, where a large document set has to stay in view.
Multimodal work spanning text, image and video in one pipeline.
Open-weight deployments you host yourself when data cannot leave your estate.
Smaller open models where latency and cost per call decide the architecture.
Cost-efficient reasoning, useful when volume rather than peak capability drives spend.
Strong multilingual coverage, including languages the Western models handle poorly.
Task-specific open models for classification, embedding and speech.
Claude
Gemini 2.0
Llama 3.3
Mistral
DeepSeek
AWS Bedrock
Azure OpenAI
GCP Vertex AI
LangChain
LlamaIndex
HuggingFace
PyTorch
TensorFlow
MLflow
Pinecone
“Implementing an ERP across our sugar manufacturing operations felt daunting at first, but Appther made the transition seamless. Every department now works off a single source of truth.”
“Appther built our student competition app and portal exactly the way we envisioned it: easy registration, smooth judging, and real-time results that keep participants engaged.”
“Building a mental health app means every detail has to be handled with care, and Appther got that from day one. The product feels warm, simple, and genuinely helpful.”
“Appther exceeded every expectation, on time and on budget, and the final product has been instrumental in scaling our business. A rare engineering partner.”
Field notes from real builds. No hype, just what worked and what didn't.
A practical framework for deciding when to ground a model in your data versus retraining it outright.
Beyond the demo: tool use, guardrails, observability and the failure modes nobody shows you.
Latency, cost and reliability tradeoffs we weigh before choosing where a vision model runs.
How we help leadership teams cut through the noise and commit budget to the right AI bets.
Walk away from one call with a prioritised list of AI use cases for your business, a realistic budget range and a 2–4 week PoC plan. No pitch deck, no obligation.
Tell us what you're trying to solve. No sales pitch, just a straight answer on scope, timeline and cost.
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