AI / ML Integration
Wire
Intelligence
Into Everything
We embed production-grade AI and machine learning directly into your existing systems pipelines, workflows, products. Not demos. Not experiments. Deployed, measured, and compounding.
Live Inference Feed
model.predict(customer_data)
97.3%
pipeline.classify(invoice_text)
94.1%
llm.extract(document_batch)
88.7%
anomaly.detect(timeseries)
queued
97%+
Model Accuracy Floor
<100ms
Inference Latency
Zero
Rip-and-Replace
Any Stack
Framework Agnostic
LLM Integration
Computer Vision
Predictive Analytics
NLP Pipelines
Autonomous Agents
Anomaly Detection
Document AI
Model Fine-tuning
MLOps & Deployment
LLM Integration
Computer Vision
Predictive Analytics
NLP Pipelines
Autonomous Agents
Anomaly Detection
Document AI
Model Fine-tuning
MLOps & Deployment
The Opportunity
Most businesses have the data. Most businesses have the problem. Almost none have AI that actually works in production.
We close that gap. From selecting the right model architecture to deployment, monitoring, and iteration we build AI systems that run inside your operations and get smarter over time. No hype. No hallucinating demos. Just inference that converts.
What We Integrate
07 Specialisations01
Core
LLM Integration
Embed large language models into your product chatbots, document summarisation, classification, Q&A systems. GPT-4, Claude, Gemini or open-source.
02
Prediction
Predictive Analytics
Build models that forecast demand, churn, revenue, and risk trained on your historical data and deployed as live decision engines inside your stack.
Model output next 7 periods
03
NLP
NLP Pipelines
Named entity extraction, sentiment analysis, intent classification structured intelligence from unstructured text at scale.
04
Vision
Computer Vision
Object detection, image classification, OCR visual intelligence embedded directly into your product or internal ops.
05
Ops
Anomaly Detection
Catch fraud, system failure, inventory drift, and outliers in real-time before they escalate into business damage.
06
Documents
Document AI
Turn invoices, contracts, reports, and forms into structured data automatically. Extract, classify, validate zero manual touch.
PDF Parsing
Table Extraction
Signature Detection
Entity Linking
07
Agents
Autonomous Agents
Build AI agents that research, decide, and act inside your workflow handling multi-step tasks end to end without human intervention.
Tool Use
Memory Systems
Orchestration
Human-in-loop
How We Work
From data to
From data to
deployed model
No ambiguity, no "let's see how it performs" surprises. A structured pipeline from problem definition to production AI that your team can trust.
01
Problem Definition & Data Audit
We identify the exact decision AI should automate, then audit your existing data for volume, quality, and labelling readiness before touching a model.
02
Model Selection & Architecture
We choose the right model class transformer, gradient-boosted tree, CNN, or LLM based on your task, latency requirements, and cost ceiling.
03
Train, Fine-tune & Validate
We train or fine-tune on your data, establish accuracy benchmarks, and validate against real edge cases not curated test sets.
04
Integration & API Wiring
We wire the model into your existing system via API, SDK, or direct embedding with zero disruption to live operations.
05
Monitor, Retrain & Iterate
We set up drift detection, performance dashboards, and retraining triggers so the model improves as your data grows.
Model Pipeline
data_ingestion.py
Raw data → cleaned feature matrix
Done
feature_engineering.py
Transform, encode, normalise
Done
model_training.py
XGBoost + cross-validation (5-fold)
Running
evaluation.py
Precision, recall, F1, ROC-AUC
Queued
deploy_api.py
FastAPI endpoint + monitoring
Queued
VALIDATION ACCURACY
Use Cases
Real Problems
Real Problems
We Solve
Across industries, the pattern is the same: manual decisions that slow you down, replaced by models that run faster and cheaper than any human process.
UC 01
Customer Churn Prediction
Identify customers likely to leave 30–60 days before they do and trigger automated retention workflows based on risk score.
UC 02
Invoice & Document Processing
Replace manual data entry with AI that reads invoices, extracts line items, validates totals, and pushes to your accounting system automatically.
UC 03
Demand Forecasting
Predict inventory requirements at SKU level by learning from seasonality, promotions, and market signals reducing overstock and stockouts simultaneously.
UC 04
Intelligent Support Triage
Automatically classify, prioritise, and route support tickets with NLP cutting first-response time and escalating critical issues before they become fires.
UC 05
Fraud & Anomaly Detection
Flag unusual transactions, access patterns, and system events in real-time stopping fraud before financial damage occurs.
UC 06
Internal AI Assistants
Build LLM-powered assistants that answer questions about your business data sales figures, policy docs, product specs without hallucinating.
97%+
Accuracy Floor
Minimum production benchmark
<100ms
Inference Latency
P95 response time target
Any Data
Input Compatibility
Text, image, tabular, time series
Auto
Retraining Triggers
Drift detection built-in
Tech Stack
Models &
Models &
Frameworks
We work model-agnostic picking the right tool for the task, not the trendy one. Everything chosen for production reliability.
Large Language Models
GPT-4, Claude, Gemini, Llama, Mistral
Classical ML
XGBoost, LightGBM, scikit-learn, CatBoost
Deep Learning
PyTorch, TensorFlow, HuggingFace Transformers
Orchestration
LangChain, LlamaIndex, Airflow, Prefect
MLOps & Serving
MLflow, BentoML, FastAPI, Docker, AWS SageMaker
Vector Databases
Pinecone, Qdrant, Weaviate, pgvector
$
python train.py --model xgboost --data
customer.csv
Training fold 1/5... accuracy: 0.961
Training fold 2/5... accuracy: 0.958
Training fold 3/5... accuracy: 0.964
✓ Mean CV accuracy:
0.962
✓ Model saved →
./models/churn_v3.pkl
$
python deploy.py --endpoint /api/predict --port
8080
Loading model churn_v3.pkl...
✓ API live at
http://0.0.0.0:8080
✓ Drift monitor active
threshold 0.05
$
curl -X POST /api/predict -d '{"customer_id":
1042}'
{"churn_risk": 0.87,
"action": "escalate"}
$
