AI & ML Development

Intelligence Built
Into Your Product

We don't bolt AI on as an afterthought. We architect it into the core of your product — where it creates real, measurable value.

25+AI Systems Shipped
94%Avg Model Accuracy
15msAvg Inference Latency
Avg ROI in Year 1
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Overview

From fine-tuning large language models to building computer vision pipelines and recommendation engines from scratch, our AI team has shipped production ML systems that are accurate, explainable, and maintainable. We work across NLP, computer vision, predictive analytics, and generative AI — always with a focus on measurable business outcomes, not demo-ware.

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📦 What You Get

  • Trained & validated ML model(s)
  • Production inference API
  • Model evaluation report & benchmarks
  • Data pipeline & preprocessing code
  • MLOps retraining pipeline
  • Explainability dashboard
  • Model card & documentation
  • 90-day post-deployment monitoring

What We Deliver

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Large Language Models

GPT-4, Claude, Gemini, and Llama fine-tuning. RAG pipelines, function calling, structured outputs, and multi-agent systems.

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Computer Vision

Object detection (YOLO), image classification, OCR, medical imaging analysis, and real-time video inference on edge hardware.

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Predictive Analytics

Time-series forecasting, churn prediction, demand planning, and anomaly detection on your operational data.

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Explainable AI

SHAP values, LIME, and attention visualisations that make model decisions transparent to regulators and end users.

Edge AI Deployment

TensorRT, ONNX, and Core ML optimisation for on-device inference — no cloud round-trips, no privacy concerns.

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MLOps & Retraining

MLflow experiment tracking, Airflow pipelines, automated retraining triggers, and model drift monitoring in production.

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Federated Learning

Train models across distributed data sources without centralising sensitive data — ideal for healthcare and finance.

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AI API Integration

OpenAI, Anthropic, Cohere, and Replicate API integration with rate limiting, fallback chains, cost tracking, and caching.

Our Process

01

Data Audit & Problem Framing

We assess your data quality, volume, and labelling — and translate your business problem into a machine learning objective.

02

Proof of Concept

A 2–4 week spike to validate feasibility with your real data. You see accuracy benchmarks before committing to a full build.

03

Model Development

Architecture selection, training, hyperparameter tuning, cross-validation, and bias evaluation.

04

API & Integration Layer

Wrapping the model in a production-grade API with authentication, rate limiting, monitoring, and A/B testing hooks.

05

Evaluation & Hardening

Adversarial testing, edge case cataloguing, fairness audits, and latency benchmarking against SLA targets.

06

MLOps & Handoff

Automated retraining pipelines, drift detection alerts, model versioning, and runbook documentation for your team.

Technology Stack

Frameworks

PyTorchTensorFlowHugging Facescikit-learnLangChain

LLM & GenAI

OpenAI APIAnthropic APILlamaIndexPineconeWeaviate

Computer Vision

YOLOv8OpenCVMONAINVIDIA TensorRTONNX Runtime

MLOps

MLflowDVCApache AirflowWeights & BiasesSageMaker

Infrastructure

NVIDIA A100/H100CUDAAWS SageMakerGCP Vertex AIDocker
FEATURED CASE STUDY

MedSync AI — Clinical Diagnostic AI System

See how we applied our AI & ML Development expertise to solve a real-world challenge with measurable business outcomes.

READ THE CASE STUDY →
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Frequently Asked

Ready to build something exceptional?

Tell us about your project and we'll put together a clear plan, timeline, and proposal — within 24 hours.