Production AI that knows your business.
From intelligent chatbots that answer customer queries to credit scoring models that assess loan risk in milliseconds — we build production AI that runs in the real world, not just in Jupyter notebooks.
Observable AI agent platform
Knowledge · guardrails · evaluation · deployment
RAG
Grounded, cited answers
24/7
Evaluation & monitoring
Private
Secure knowledge layer
The full AI/ML stack — from data to production
RAG Chatbots & Conversational AI
Customer-facing and internal chatbots grounded in your knowledge base. RAG pipelines, multi-turn memory, tool-calling agents, CRM integration, and human escalation.
Fraud Detection Systems
Real-time streaming fraud detection with sub-100ms scoring. Anomaly detection, behavioural analysis, graph-based fraud networks, and SHAP explainability for compliance.
Credit Scoring & Risk Models
Banking-grade credit scoring with logistic regression, gradient boosting, and neural networks. Full explainability reports for regulators (SHAP/LIME). Scorecard development.
Property Price Prediction
Automated valuation models (AVM) using historical sales, location embeddings, amenity proximity, and macroeconomic features. REST API + dashboard for valuers.
Climate Change Prediction
Time-series forecasting models for temperature, precipitation, and extreme event probability. LSTM, Prophet, and ensemble methods on meteorological datasets.
Microservices Log Detection
Unsupervised anomaly detection across distributed logs. LSTM autoencoders, Isolation Forest, and real-time alerting for SRE teams.
Chatbots that actually know your business
Retrieval-Augmented Generation (RAG) is the architecture that lets an LLM answer questions about your specific knowledge base — your product docs, policies, case files, pricing sheets — without hallucinating. We ingest your documents, chunk and embed them into a vector store (Pinecone, Weaviate, or pgvector), and wire up a retrieval pipeline that pulls the right context for every query.
The result is a chatbot that gives your customers accurate, cited answers from your official documentation — not confident-sounding guesses. Confidence thresholds route low-certainty queries to a human agent with the full conversation context.
RAG architecture flow
Document ingestion
PDFs, Notion, Confluence, URLs, Google Docs
Chunking & embedding
Semantic splitting + OpenAI/E5 embedding model
Vector store
Pinecone, Weaviate, or pgvector (self-hosted)
Retrieval
Hybrid search — dense + BM25 keyword fallback
LLM generation
GPT-4o / Claude with retrieved context + system prompt
Output validation
Confidence scoring + hallucination detection
Monitoring
Latency, CSAT, intent classification, failure rate
Credit scoring, fraud detection & banking risk models
Credit Scoring & Risk Models
We build banking-grade credit scoring systems trained on your historical loan data. Models use a combination of traditional scorecard features and ML algorithms, with full SHAP explainability so your credit officers can understand every decision — essential for regulatory compliance.
Use cases we've built
- → Consumer loan scoring for microfinance lenders
- → SME credit decisioning for fintech lending platforms
- → BNPL eligibility scoring at checkout (< 200ms)
- → Student loan risk assessment for universities
Real-Time Fraud Detection
We build fraud detection pipelines that process transaction streams in under 100ms — using a combination of rule engines, ML models, and graph-based analysis to catch fraud patterns that rule-based systems miss. Every flag includes a SHAP explanation your analysts can act on.
Fraud categories detected
- → Payment fraud — card-not-present, account takeover
- → Identity fraud — synthetic ID, document forgery
- → Merchant fraud — chargebacks, triangulation fraud
- → Insurance fraud — claim anomaly patterns
Prediction models for every domain
Property Price Prediction
Automated Valuation Model (AVM) for real estate
- Historical sales regression (XGBoost/LightGBM)
- Location embeddings (lat/lng, neighbourhood)
- Amenity proximity features (schools, transport)
- Macroeconomic indicators (interest rates, inflation)
- Computer vision for property condition scoring
- REST API for integration with valuation platforms
- Interactive heatmap dashboard
- Confidence intervals per prediction
Industries
Real estate platforms, banks, mortgage lenders, property portals
Climate Change Prediction
Time-series forecasting for environmental data
- LSTM & GRU deep learning for temperature forecasting
- Prophet for trend + seasonality decomposition
- Ensemble methods (ARIMA + ML hybrid)
- Precipitation & extreme event probability
- CO₂ concentration impact modelling
- Geospatial climate risk heatmaps
- NetCDF & ERA5 reanalysis data ingestion
- REST API + Streamlit research dashboard
Industries
Environmental agencies, agriculture firms, insurance, research
Microservices Log Detection
Unsupervised anomaly detection across distributed systems
- LSTM autoencoder for sequential log patterns
- Isolation Forest for point anomalies
- Log embedding with BERT/sentence transformers
- Multi-service correlation analysis
- Real-time streaming (Kafka + Flink)
- PagerDuty / Slack / OpsGenie alerting
- Root cause analysis suggestions
- Custom Grafana / Kibana dashboards
Industries
SRE teams, cloud-native startups, DevOps platforms
Student Performance Prediction
Early intervention models for education institutions
- At-risk student identification (≥ 85% accuracy)
- Feature engineering from attendance & grades
- Intervention recommendation engine
- LMS integration (Moodle, Canvas, Blackboard)
- Fairness & bias auditing across demographics
- Faculty dashboard with cohort analytics
- REST API for SIS (student info system)
- FERPA-compliant data handling
Industries
Universities, online learning platforms, ed-tech companies
Supply Chain Demand Forecasting
Inventory optimisation and demand prediction
- Multi-step demand forecasting (LSTM / Temporal Fusion)
- Promotion & seasonality effect modelling
- External signals (weather, events, trends)
- SKU-level and aggregate forecasting
- Safety stock & reorder point optimisation
- ERP integration (SAP, Oracle, Odoo)
- S&OP planning dashboard
- API for warehouse management systems
Industries
Retail, FMCG, manufacturing, 3PL logistics providers
Healthcare Outcome Prediction
Clinical risk scoring and patient outcome models
- Readmission risk scoring (30/90 day)
- ICU mortality prediction (APACHE-style)
- Chronic disease progression modelling
- Medical image classification (X-ray, pathology)
- EHR feature engineering (ICD codes, labs)
- HIPAA & GDPR data handling
- Explainability for clinical decision support
- Integration with EMR/EHR systems
Industries
Hospitals, insurance companies, healthtech startups
Replace manual processes with intelligent pipelines
Every business has workflows that eat hours of staff time — extracting data from PDFs, classifying support tickets, routing emails to the right team, generating weekly reports, or reviewing contracts. LLM-powered pipelines can handle these tasks at scale with accuracy that rivals trained human reviewers.
We map your existing process, identify the highest-ROI automation candidates, build the pipeline with confidence thresholds and human-review gates, then deploy to serverless or containerised infrastructure with full monitoring. Most clients see positive ROI within 90 days.
Document Processing
Extract structured data from invoices, contracts, medical forms, and ID documents — accuracy > 96%.
Email & Ticket Triage
Classify, route, and draft initial responses for support emails — reducing first-response time by 80%.
Contract Review
Flag non-standard clauses, extract key dates and parties, flag compliance risks in legal documents.
Report Generation
Automated weekly/monthly reports from your databases — formatted as PDF, Word, or email summaries.
AI Sales Qualification
Score inbound leads, extract buyer intent from forms and emails, and prioritise for your sales team.
Compliance Monitoring
Continuous monitoring of transactions, communications, or records against regulatory rules.
Your data, your model
Fine-tuning on your proprietary data creates a model that understands your product, your customers, and your brand voice — reducing prompt length, improving accuracy, and cutting per-query token costs. We use LoRA and QLoRA techniques that make fine-tuning accessible even on smaller datasets.
Fine-tuning process
- 1.Baseline evaluation on vanilla model
- 2.Dataset curation from your docs & logs
- 3.LoRA/QLoRA training run (GPU cluster)
- 4.Evaluation: perplexity, task accuracy, BLEU
- 5.Iterative refinement until target KPIs met
- 6.Deployment: weights + inference container
- 7.Ongoing monitoring & periodic retraining
Vision models for real-world problems
We build and deploy computer vision systems for inspection, verification, and tracking — from a defect detection camera on a factory line to an ID verification microservice that runs at checkout. Edge deployment to NVIDIA Jetson or Raspberry Pi for scenarios where cloud latency is unacceptable.
Industry applications
- →Manufacturing: defect detection on production lines
- →Retail: shelf stock monitoring & planogram compliance
- →Fintech: document OCR + ID verification at KYC
- →Agriculture: crop disease detection from drone imagery
- →Security: CCTV anomaly detection & crowd analysis
From idea to production in 8 steps
Problem Definition
We translate your business problem into an ML task — classification, regression, anomaly detection, generation — with success metrics your stakeholders agree on.
Data Audit & Pipeline
We assess your data quality, identify gaps, engineer features, and build a reproducible data pipeline. Clean data is the most important step.
Baseline Model
We establish a simple baseline (logistic regression, decision tree) before adding complexity. This sets a benchmark and validates that the problem is solvable.
Model Development
We train and evaluate candidate models using k-fold cross-validation, tune hyperparameters with Optuna or Ray Tune, and select the best performer.
Explainability
We generate SHAP or LIME explanations for every model decision — required for regulated industries and essential for your team to trust the model.
API Development
We wrap the model in a FastAPI or Laravel service with authentication, rate limiting, input validation, and OpenAPI documentation.
Monitoring & Drift
We deploy model performance monitoring that alerts when data distribution or prediction accuracy drifts — triggering the retraining pipeline.
Retraining Pipeline
We build automated retraining workflows triggered by drift alerts or a schedule — keeping your model accurate as your data evolves over time.
Everything included in every AI engagement
Multiple LLM support
GPT-4o, Claude, Gemini, Llama 3 — we pick the right model for your use case and budget.
Data sovereignty
On-premise, VPC, or dedicated cloud deployment. Your data never leaves your jurisdiction.
Model explainability
SHAP, LIME, and attention visualisation for every model. Required for regulated industries.
Retraining pipeline
Drift detection and automated retraining so your model stays accurate as data evolves.
Real-time alerts
PagerDuty, Slack, or email alerts on model drift, anomaly spikes, or pipeline failures.
Full documentation
OpenAPI specs, Jupyter notebooks, model cards, and data lineage documentation.
REST API delivery
Every model delivered as a production-ready FastAPI or Laravel service with auth.
Cloud-agnostic
AWS SageMaker, Azure ML, GCP Vertex, or self-hosted GPU infrastructure.
A/B testing
Champion-challenger framework to test new models against live production models safely.
Compliance ready
GDPR, HIPAA, SOC 2, Basel III — we design for your regulatory environment.
Human-in-the-loop
Review gates for low-confidence predictions. Your team stays in control.
MLOps tooling
MLflow, DVC, Weights & Biases for experiment tracking and model versioning.
Beyond the demo — AI that runs in production
Most AI demos look impressive. Most production AI systems disappoint. The gap is in everything that happens after the model is trained: the data pipeline that keeps it fed, the monitoring that catches when it drifts, the API that serves predictions at the right latency, and the retraining workflow that keeps accuracy up as the world changes. We build all of it.
We've built credit scoring models that process 50,000 loan applications per month, fraud detection pipelines that flag suspicious transactions in 80ms, RAG chatbots that handle 30% of a bank's customer support volume without human intervention, and property valuation models used by real estate platforms across three countries.
Explainability is not optional
In regulated industries — banking, insurance, healthcare, government — a model that makes accurate predictions but can't explain why is legally unusable. We build SHAP and LIME explainability into every model from day one, not as an afterthought. A credit officer needs to understand why a loan was declined. A fraud analyst needs to know which signals triggered the alert. A doctor needs to understand which clinical features drove a risk score.
Explainability also helps your team trust the model, catch bias, and iterate faster. We build interactive dashboards where your domain experts can explore model decisions and flag edge cases for the training pipeline.
Frequently asked questions
Related services
Software Development
Full-stack backends and frontends — Laravel, Go, and React — to power your AI-driven application.
- Laravel
- FastAPI
- Go
- React
API Development
Well-documented APIs that let your AI models integrate with every system in your stack.
- REST
- GraphQL
- OpenAPI
- FastAPI
Payment Integration
Combine AI fraud detection with real-time payment processing for maximum security.
- Stripe
- JazzCash
- Fraud Detection
- WebSocket