Data & Analytics
AI & Machine Learning
Models that hold up in production. Not notebooks that win a bake-off.
12+
capabilities shipped into production systems
We build predictive, classification and computer-vision systems on your own data. Every model ships with a measured baseline and an honest read on where it breaks, because a model that scores well on a test set has not yet been shown to beat the process you run today.
AI & Machine Learning Services
We build models that earn their keep. Then we prove they do.
From the first conversation about which decision is being made on a gut feel, to a validated model with its limitations written down: four services, one senior team from the first data audit to independent review.
ML Strategy & Feasibility
We start with the decision, not the algorithm. Which call is being made on instinct, how often, and what does being wrong cost? We audit whether your data can actually support the model before anyone budgets for one, including when the honest answer is that it can't yet.
Output
- Data readiness audit
- Use-case ranking
- ROI model
Custom Model Development
Forecasting, classification, ranking, anomaly detection and computer vision, built on your data and validated against the process you run today. We start with the simplest model that could work and only add complexity when it buys measurable accuracy.
Built on
- scikit-learn
- XGBoost
- PyTorch
- TensorFlow
- Azure ML
Computer Vision & NLP Solutions
Applied deep learning where the input is an image, a document or free text: defect detection, object counting, OCR, entity extraction and ticket routing. Evaluated on escapes and false alarms, not on a public benchmark.
Built on
- PyTorch
- YOLO
- OpenCV
- Hugging Face
- spaCy
Model Validation & Assurance
Independent review of models you already run, or a second pair of eyes on ours. Backtesting on held-out periods, leakage checks, bias and fairness testing, and the documentation your risk and audit functions will actually ask for.
Output
- Backtest report
- Bias & fairness testing
- Model card
How we build
Two disciplines. Twelve capabilities production actually needs.
Classical ML earns its budget on structured data. Deep learning earns its on images, audio and language. Choosing wrongly is the most expensive mistake in this field: a gradient-boosted tree that ships in six weeks usually beats a neural network that never quite does.
Forecasting & time series
Demand, capacity, revenue and load forecasting with seasonality, promotions and external drivers modelled explicitly, and intervals, not just point estimates.
- XGBoost
- Prophet
- ARIMA
- LightGBM
Classification & propensity
Churn, conversion, credit and risk scoring, calibrated so a 0.7 genuinely means seven in ten, because downstream teams act on the number, not the ranking.
- scikit-learn
- CatBoost
- Calibration
Anomaly detection
Fraud, fault and outlier detection on streaming or batch data, tuned against the false-positive rate your team can realistically work through in a day.
- Isolation Forest
- Autoencoders
- Streaming
Segmentation & clustering
Customer, product and behavioural segmentation that produces groups an operator can name and act on, not twelve clusters nobody can describe.
- K-means
- HDBSCAN
- UMAP
Feature engineering & selection
Deriving the signal that actually predicts the target, then cutting everything that does not; fewer, better features beat a wide table almost every time.
- Feature selection
- Leakage checks
- pandas
Explainability & fairness
Feature attribution, bias testing across protected groups, and model cards written for the risk committee rather than for the data science team.
- SHAP
- Fairlearn
- Model cards
Computer vision
Defect detection, object counting, OCR and quality inspection on the line, evaluated on escapes and false alarms, not on a benchmark dataset.
- PyTorch
- YOLO
- OpenCV
- Segmentation
NLP & text classification
Intent detection, entity extraction, routing and sentiment over tickets, notes and correspondence, with the taxonomy your business already uses.
- Transformers
- spaCy
- Hugging Face
Neural network architecture
Custom architectures where the problem genuinely needs one: sequence models, embeddings and multi-input networks over mixed data types.
- PyTorch
- TensorFlow
- Keras
Transfer learning & fine-tuning
Pre-trained backbones adapted to your domain on a fraction of the labelled data: the difference between a viable project and an impossible labelling budget.
- Fine-tuning
- LoRA
- Augmentation
Edge & real-time inference
Quantised, distilled models running on device or at the line where latency and connectivity rule out a round trip to the cloud.
- ONNX
- TensorRT
- Azure IoT Edge
Synthetic data & augmentation
Where the defect is rare or the labelling budget is small, we generate and augment training examples so the class you actually care about becomes learnable.
- Augmentation
- Synthetic minority
- Albumentations
We reach for the simplest model that clears the bar. If logistic regression solves it, that is what you get; deep learning is a cost as well as a capability.
How we engage
Find the stage that matches where you are
Four ways to work with us, from a first look at whether your data can support a model to an independent review of one you already run.
Stage: Exploration
What you’re asking: “Can our data actually support a model, and which one first?”
Our service: ML Strategy & Feasibility
Timeline: 2–4 weeks
Stage: Prototype
What you’re asking: “Build a model on our real data and beat our current process.”
Our service: Custom Model Development
Timeline: 6–12 weeks
Stage: Production
What you’re asking: “Apply it to our images, documents or free text.”
Our service: Computer Vision & NLP Solutions
Timeline: 8–14 weeks
Stage: Scale
What you’re asking: “Review a model we already run before we trust it further.”
Our service: Model Validation & Assurance
Timeline: 3–5 weeks
Where it applies
Industries we build AI and machine learning for
The modelling discipline stays the same across every deployment. What changes is the data realities, the regulation, and how much a wrong prediction costs.
- 01
Healthcare & Life Sciences
Risk stratification, readmission prediction and imaging support, with explainability and human sign-off on anything that reaches a clinical decision.
Explore the industry - 02
Financial Services & Insurance
Credit scoring, fraud detection and claims triage, built to survive a model-risk review and evaluated against the false-positive rate your team already tracks.
Explore the industry - 03
Logistics & Transportation
ETA prediction, route and load optimisation, and demand models that reason over telematics feeds and historical movement data.
Explore the industry - 04
Manufacturing & Supply Chain
Predictive maintenance, visual quality control and demand forecasting pulling from ERPs, SCADA and sensor telemetry.
Explore the industry - 05
Real Estate & Data Centers
Capacity and power forecasting, anomaly detection on infrastructure telemetry, and occupancy models across the estate.
Explore the industry - 06
Retail & CPG
Demand forecasting, price elasticity, recommendation and inventory models connected to storefronts and planning systems.
Explore the industry
Platform focus
Native to the Azure data estate you already run.
Most of the training data we need is already in your lakehouse. We build on Azure Machine Learning and Microsoft Fabric so models train where the data lives, inherit the governance your security team already approved, and draw on the same governed tables your reporting already runs on.
- Azure Machine Learning
- Microsoft Fabric
- Databricks
- MLflow
- Power BI
- Azure AI Foundry
Train where the data lives
No copying the lakehouse into a separate ML platform, and no second copy of your data to secure.
Governed by default
Entra identity, managed endpoints and row-level security carried through from the source, not bolted on.
Beyond Azure
The same discipline extends to AWS SageMaker, GCP Vertex and on-premise GPU estates where that is where you run.
Why Praval
Why teams bring us in
We measure the baseline first
If we cannot state how your current process performs, we are not ready to build, and neither is the model. Accuracy without a comparison is a number, not a result.
We will talk you out of it
Plenty of problems are better solved with a rule, a report or a fixed process. We would rather say so in week two than bill you for a model that never ships.
We look for the leak
A result that looks too good usually is. We treat a suspiciously strong score as a bug until we have proven it is not, because leakage is cheaper to find now than after rollout.
Senior team, start to finish
The people who scope the model are the people who build and run it. No handoff to a junior bench once the statement of work is signed.
Start with the decision
Tell us which decision you’re making on a gut feel. We’ll tell you honestly whether your data can support a model.
No slide deck of use cases. A working point of view on your problem, from a senior team.
Related
Related work
3 of 3 shown
ML Ops & Governance
The layer between a model that works and a model that keeps working: deployment pipelines, feature parity, drift monitoring, retraining gates and rollback.
ServiceGenerative & Agentic AI
Generative and agentic AI systems: reasoning, tool-use and multi-agent orchestration wired into the platforms you already run, each with a baseline, guardrails and an evaluation harness.
PlatformMicrosoft & Azure
Azure consulting, migration, managed services and governance, plus the Microsoft data and low-code estate: Fabric, Power BI and Power Platform.
Questions
Common questions
- How much data do we actually need?
- It depends far more on how strong the signal is than on raw row count. A clean tabular problem can work on a few thousand well-labelled examples; a vision model with subtle defects may need tens of thousands of images. The data audit in week two answers this specifically rather than in general.
- What's the difference between this and the generative AI work?
- Machine learning predicts a value or a class from your historical data: how much will sell, which account will churn, whether this part is defective. Generative AI produces new content from a foundation model. Different tools for different problems, and plenty of systems use both.
- How do you know the model actually worked?
- We measure your current process before we build. The model has to beat that baseline on a business metric (forecast error, hours saved, escapes caught), not just post a good AUC on a test set. If it ties, we say so.
- How do you avoid overfitting and data leakage?
- Leakage is the most common reason a model looks brilliant in development and useless in the real world. We backtest on held-out time periods rather than a random split, audit every feature for information that would not have existed at prediction time, and treat a suspiciously good result as a bug until proven otherwise.
- Can you work with models we already have?
- Yes, and it is a common starting point. We review what you are running, backtest it against a fresh baseline, and either productionise it properly or tell you where it is falling short and what it would take to fix.
Why Praval
How we work with you.
Industry expertise
Seasoned professionals with deep industry knowledge and hands-on experience driving digital acceleration across sectors.
Client-centric approach
We prioritise understanding your challenges, goals and culture, and deliver solutions tailored to them rather than to a template.
Proven methodologies
Industry-leading frameworks and best practice, giving a structured and repeatable route to the outcome you asked for.
Collaborative partnership
We work as an extension of your organisation: transparency and agility during the engagement, and a handoff that holds after it.
- 01
Initial consultation
We evaluate your current systems and identify where the value is.
- 02
Customized plan
We design a solution scoped to your business, not to a template.
- 03
Design & development
We build and transition with minimal disruption to live operations.
- 04
Monitoring & support
Continuous oversight and support keep the estate healthy afterwards.