We build production-grade AI systems — from large language model integrations and computer vision pipelines to predictive analytics and intelligent automation. Not demos. Real systems that run at scale.
98.4%
Accuracy
12ms
Latency
99.9%
Uptime
From raw data to deployed models, we cover the entire AI lifecycle.
Custom LLM integrations, fine-tuning, RAG pipelines, and intelligent agents powered by GPT-4, Claude, Llama, and Mistral.
Image classification, object detection, OCR, and visual inspection systems trained on your domain-specific data.
Demand forecasting, churn prediction, risk scoring, and time-series models that turn historical data into foresight.
Document extraction, workflow orchestration, and AI-driven decision systems that eliminate manual bottlenecks.
Context-aware chatbots, voice assistants, and support automation with memory, tool use, and escalation logic.
Bias audits, explainability layers, privacy-preserving inference, and compliance-ready model governance.
Pick your industry and see what purpose-built AI actually delivers.
Automated property valuation models, tenant risk scoring, predictive maintenance alerts, and smart document extraction from leases and contracts — all deployed as white-label APIs for your platform.
A rigorous, repeatable methodology for delivering AI that works in the real world — not just in notebooks.
We catalogue your data sources, assess quality, handle PII scrubbing, and design the ingestion pipeline — structured, semi-structured, and unstructured.
Statistical exploration, feature selection, and domain-specific transformations. This is where patterns become predictive signals.
We benchmark multiple architectures — from classical ML to fine-tuned LLMs — and train against your objectives with rigorous evaluation.
Performance metrics, bias detection, adversarial testing, and compliance alignment before a single line of model code reaches production.
REST / gRPC inference APIs, containerised with Docker, orchestrated on Kubernetes or serverless — with blue-green deployments and zero-downtime rollouts.
We set up data drift detection, performance dashboards, automated retraining triggers, and SLO alerting so your model stays sharp long after launch.
The 8 engineering principles that separate our AI deliverables from vendor fluff.
We bridge the gap between experimental notebooks and battle-hardened production systems.
We choose the right tool for the job — OpenAI, open-source, or custom-trained — not the fashionable one.
On-premise deployments, private cloud clusters, and data residency controls built into every engagement.
Quantisation, batching, caching, and edge inference. We optimise until it's fast enough to feel instant.
Version-controlled models, reproducible experiments, and CI/CD for ML — not bolted on at the end.
Multi-region inference with failover, CDN-fronted APIs, and compliance-ready data handling across jurisdictions.
Drift triggers, scheduled retraining jobs, and A/B evaluation pipelines so your model improves over time.
Terraform-provisioned clusters, auto-scaling inference pods, and full observability stacks configured by us.
These are the numbers behind our production AI systems — not benchmarks, not toy demos.
AI models in production
Running live across client platforms
Median inference latency
Real-time APIs under load
Average model accuracy
Across classification pipelines
Inferences served
Cumulative across deployments
Whether you have clean data and a clear goal, or just a problem you want to throw AI at — we scope, architect, and deliver without the hype.
