Data & Analytics
Data Science
AI, ML and deep learning that predicts future shifts or prescribes the exact strategy to outpace the competition.
The engineering around the model (deployment, monitoring, retraining) is where most of the effort goes, and most programmes underestimate it.
What we deliver?
What Data Science covers?
Four capabilities that run in sequence, from the first use case conversation to the models we operate for you in production.
1. AI Strategy & Use-Case Engineering
Start from the decision, not the model. We identify which problems are worth solving with AI, and which of them are actually ready to be.
- AI/ML Readiness Assessment: We score your data, tooling and skills, then rank the gaps worth closing first.
- Use-Case Discovery & Prioritization: A scored portfolio balancing business value, technical feasibility and realistic time to production.
- Feasibility & PoC Design: We agree what gets proven, on which data, against a measurable success threshold.
- Build, Buy or Fine-Tune: Model and platform selection judged on cost per outcome rather than licence price.
2. AI & Machine Learning
Models that reach the workflow rather than the notebook, built to survive production traffic and second-line review.
- Predictive & Prescriptive Modelling: Forecasting, risk scoring and propensity models built directly into operational decision workflows.
- Computer Vision & Deep Learning: Image, signal and geospatial models detecting patterns that engineered features cannot capture.
- Document Intelligence & NLP: Extraction, classification and validation across invoices, claims, contracts and clinical documents.
- Decision Science & Optimization: Scenario simulation and constrained optimization for pricing, inventory, routing and capacity planning.
3. Generative & Agentic AI
Retrieval and reasoning grounded on your own data, with provenance your teams can check before they act on an answer.
- Enterprise RAG & GraphRAG: Retrieval pipelines, chunking strategy and vector or graph indexes over your content.
- Domain Agents & Multi-Agent Systems: Agents with scoped tool access, memory and MCP integration into your existing systems.
- AI Copilots & Conversational Analytics: Natural language access to governed data, answering questions with traceable source links.
- Model Adaptation & Evaluation: Prompt engineering, fine-tuning and evaluation harnesses that measure quality before anything ships.
4. ML Ops & Governance
We run what we build, and we can show exactly how every model in production is behaving today.
- MLOps Engineering & CI/CD: Model registries, automated pipelines, controlled deployment and rollback across every environment.
- Monitoring, Drift & Maintenance: Continuous performance tracking, drift detection and retraining cadence against agreed service levels.
- Responsible AI & Model Governance: Explainability, bias testing and validation evidence that satisfies your second-line reviewers.
- LLMOps & Cost Control: Guardrails, evaluation pipelines and token spend tracked per workload and per use case.
No governed data underneath this yet?
Lakehouse engineering, pipelines, semantic models and platform operations live with our Data & Analytics practice.
Where to start
Fixed-scope entry points
Start where the work is blocked. Each engagement is scoped up front and ends with a plan you can act on, whether or not we do the build.
- 01
AI Use-Case Discovery Sprint
A two-week engagement that inventories candidate use cases across your business, scores each one on value, data readiness and feasibility, and returns a prioritized roadmap you can take straight into a budget conversation.
Scope this with us: AI Use-Case Discovery Sprint - 02
GenAI or Agent Proof of Concept
A six-week fixed-scope build against one named bottleneck, with the baseline measured before we start and an evaluation harness delivered alongside the application, so the result is judged on quality rather than on a demo.
Scope this with us: GenAI or Agent Proof of Concept - 03
Model Risk & Governance Review
An independent review of the models already running in your business, covering documentation, explainability, bias testing and monitoring, ending with a gap list mapped to the evidence your risk and audit teams will ask for.
Scope this with us: Model Risk & Governance Review - 04
MLOps Maturity Assessment
A diagnostic of how models move from experiment to production today, covering registries, deployment, monitoring and retraining, with a sequenced plan to shorten release cycles and remove the manual steps that break under load.
Scope this with us: MLOps Maturity Assessment
How we engage
Engagement models
- Data science as a service
- Advisory services
- Fixed-scope project
Related
Related work
AI & Machine Learning
Predictive, classification and computer-vision models built on your own data, each with a measured baseline and an honest read on where it breaks.
ServiceML 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.
Questions
Common questions
- How quickly can you show something real?
- Our frameworks are tested and ready, so the first milestone is a working proof of value against your own data rather than a slide deck. How long that takes depends on the state of the data we start from, which the assessment establishes first.
- Do you build models or maintain them?
- Both, and the second matters more than most programmes assume. Models decay; without a retraining and drift-monitoring cadence, a good model quietly becomes a bad one.
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.