Services
Data Analytics & AI
Data pipelines, machine learning and generative AI, taken from proof of concept into production.
Your systems
- ERP
- CRM
- Web and app events
- Sensors and IoT
- Documents
- Dashboards
- Forecasts
- AI assistants
- APIs
Common challenges
Challenges we solve
Data you can't trust
Reports disagree because data is copied between systems without validation, and nobody owns data quality.
Reporting that's always behind
Analysts spend their time exporting and cleaning spreadsheets instead of analysing, so reports arrive too late to act on.
AI pilots that never reach production
Proofs of concept work in notebooks but lack the pipelines, monitoring and governance to run in production.
Generative AI without controls
Teams want to use large language models on company data but need access controls, answers grounded in approved sources and a way to measure quality.
How to get started
Three steps to a plan for Data Analytics & AI
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
Where are you today?
From spreadsheets
to AI in production
Choose the stage closest to where you are today to see the next step and what we deliver for it.
Stage 1 · Foundation
Can we trust our numbers?
- Reports are built by hand from system exports
- Teams quote different figures for the same metric
- Nobody owns data quality
The next step
Bring data from source systems into one warehouse with automated pipelines and data-quality checks.
What we deliver
- Data engineering
Find your starting point
Which capability do I need?
Choose what you want to do to see the capability that delivers it and what usually comes first.
Table 1. What you want to do, and the capability that does it. Choose a row.
| Goal | Capability |
|---|---|
| Answer questions from our documents and policies | Generative AI |
| Pull fields out of invoices, forms or contracts | Generative AI |
| Forecast demand, sales or workload | Predictive analytics |
| Flag fraud, churn or risky cases | Machine learning |
| Recommend products or content | Machine learning |
| Get one trusted set of numbers across teams | Data engineering |
| Dashboards that people actually use | Visualization & analytics |
| Inspect products or count items from cameras | Computer vision |
Answer questions from our documents and policies
A knowledge assistant searches your content and answers with citations (retrieval-augmented generation).
Needed first
- Current, approved documents with an owner
- Clear access permissions on that content
Overview
How we approach
Data Analytics & AI
Today's interconnected world generates vast amounts of data every second. Processed with AI and machine learning, that data reveals insights and trends that would be impossible to spot manually, and acting on them is vital to keep pace.
We build the pipelines and infrastructure to store, analyze and process large datasets efficiently, with an end-to-end approach covering data modeling, preparation, training, tuning and migration.
What we deliver
Choose a capability to see how it works, what you receive and where it applies.
- Generative AIAssistants, search and automation built on large language models, integrated safely with your data.Explore Generative AI
- Machine learningModels built, trained, deployed and monitored with proven tooling such as TensorFlow, PyTorch and scikit-learn.Explore Machine learning
- Predictive analyticsForecasts and risk scores that turn historical data into forward-looking decisions.Explore Predictive analytics
- Data engineeringAutomated pipelines and storage that give your teams up-to-the-minute, trustworthy data.Explore Data engineering
- Visualization & analyticsDashboards and reports that make complex data easy to explore and act on in real time.Explore Visualization & analytics
- Computer visionImage analysis, object detection and video analytics powered by machine learning.Explore Computer vision
Generative AI
Answers grounded
in your own data
We build assistants that answer from your approved content, found at the moment of each question (retrieval-augmented generation), with the source shown for every answer.
3 relevant passages found
- Returns policy1
- Shipping FAQ
- Warranty terms2
- Store hours
- Claims procedure3
- Holiday schedule
Damaged items can be returned within the refund window set in the returns policy1. Warranty terms apply once that window has passed2. Claims need photos of the damage3.
- Your sourcesPolicies, manuals, knowledge bases, tickets and other approved documents.
- IndexDocuments are split into passages and indexed for search, keeping who may see each one.
- RetrieveEach question finds the most relevant passages the person asking is allowed to see.
- GenerateA large language model answers from those passages only, and cites them.
- EvaluateTest sets, user feedback and review measure answer quality before and after launch.
Controls
Controls built into every assistant
What we build into every assistant: who can see what, where answers come from, and who signs off.
- Permission-aware accessAnswers only draw on content the person asking is already allowed to see.
- Cited sourcesEvery answer links to the passage it came from, so it can be checked.
- Data protectionPersonal and sensitive data handled according to your policies.
- Human reviewPeople approve high-stakes outputs before they're acted on.
- Audit loggingQuestions, sources and answers logged for review, in your own cloud account.
Deliverables
What you receive
- Data audit and prioritized use cases
- Automated data pipelines with data-quality checks
- Data models in a warehouse or lakehouse
- Dashboards and self-service reporting
- Trained models with evaluation against a baseline
- Deployment, monitoring and retraining (MLOps)
How the work runs
From first workshop to handover
Assess
- Data audit and access review
- Use cases and success metrics
- Feasibility and baseline
Prepare
- Pipelines and data modeling
- Cleaning and labelling
- Data-quality checks
Build
- Model training and tuning
- Evaluation against the baseline
- Dashboards and interfaces
Operate
- Deployment
- Monitoring for drift and quality
- Retraining and migration
Machine learning operations
Models kept accurate
after launch
We monitor every model we put into production for changes in the data it sees and for falling accuracy, and retrain it when either moves, so a model that worked at launch keeps working.
How we deliver
Six phases, from question to production
Every data and AI project we run moves through six phases, and each ends with something you can use. Choose a phase.
Phase 1 of 6
Discovery
What we do
- 1.1 Agree the business goal and the decision it supports
- 1.2 Review the current process and data
- 1.3 Define the success measures
- 1.4 Plan the project
You receive
A project plan with success measures
AI and data consulting
Your use cases, ranked
We score your use cases for value and feasibility, so you know what to build first and which foundations unlock the rest. Choose a point in this example.
Use case
Invoice data extraction
Value
0.62
Feasibility
0.85
High volume and a clear measure (fields extracted correctly); documents are already digital.
Generative AIMLOps
Where your machine learning operations stand
Answer four questions to see your level and where our MLOps work would start.
Assessment
Your MLOps check
Answer each question for the models you run today
- Is model training code in version control, with automated builds?
- Is training automated and are experiments tracked in one place?
- Are models released through a CI/CD pipeline with tests?
- Do production metrics, such as drift, trigger retraining automatically?
Your level
Level 1: DevOps but no MLOps
Builds and application tests are automated, but models are still handed over by hand and hard to reproduce.
Where we'd start
We'd automate training and track experiments in one place, such as MLflow.
Machine learningGovernance and security
Controls built into every AI project
What we build into every AI system, so it stays accurate, secure and under your control. Choose a concern.
Table 3. What clients ask us to guard against. Choose one to see the control we build in.
| Concern | What it looks like |
|---|---|
| Wrong answers | Confident answers that aren't supported by your sources. |
| Data leaks | Personal or confidential data reaching the wrong people or a provider. |
| Misuse and attacks | Prompt injection, attempts to get around the rules, and attacks on connected tools. |
| Unfair results | Models or assistants that perform worse for some groups of customers. |
| Over-reliance | People acting on outputs without checking them. |
| Harmful content | Offensive or unsafe output reaching customers. |
| Rising cost | Usage and model spend growing faster than expected. |
| Model changes | A provider's model update changing behaviour without warning. |
What we build in
Wrong answers
Answers grounded in your content with citations, and scored on an evaluation test set before every release.
Handover
Delivered with every AI project
- An evaluation test set and report
- A model card or system documentation
- Access control and audit logging
- Monitoring dashboards and alerts
- Runbooks, code and documentation in your accounts
How to start
From first call to production
Start where you are. Each step ends with a decision, so you commit to the next one only when it makes sense.
Protocol
How an engagement runs
Each step ends with a decision on whether to continue
Free technical consultation
One or two sessions
Talk through the goal, the data you have and the systems involved.
Outputs
- (a) A shortlist of use cases, ranked by value and feasibility
- (b) A recommended next step
- (c) A plan covering stack, architecture, timeline and budget
By industry
Where it applies
What this service builds in each industry we serve.
RetailPersonalization and product recommendationsRecommendations, segments and search ranking driven by browsing and purchase data.Demand forecasting and retail analyticsSell-through, margin and replenishment dashboards, and forecasting models that inform buying and allocation.
Financial ServicesFraud detection and credit risk modelsMachine learning for transaction monitoring, anomaly detection and credit scoring, with documented, explainable outputs.Regulatory reporting and data platformsPipelines and warehouses that consolidate transaction data for reporting, reconciliation and audit.
HealthcareClinical and operational dashboardsData pipelines and dashboards for capacity, patient throughput, referral leakage and quality measures.Predictive analyticsModels for no-show prediction, readmission risk and demand forecasting, using de-identified data where possible.
EducationLearning analytics and early alertsDashboards and models combining attendance, LMS activity and grades to flag students for advisor outreach.
Non-ProfitsImpact dashboardsReports that turn program and fundraising data into outcome metrics for funders, boards and annual reports.
MediaRecommendation enginesPersonalized rows and 'up next' recommendations based on viewing and reading behavior.Audience analyticsFirst-party data pipelines and dashboards for engagement, churn and advertising performance.
ManufacturingPredictive maintenanceModels on vibration, temperature and runtime data that flag equipment likely to fail, so maintenance can be scheduled.Demand and production planningForecasting and scheduling tools that use order history and capacity to plan production.
Real EstateMarket and portfolio analyticsDashboards and valuation models for pricing, occupancy, rent rolls and portfolio performance.
LogisticsRoute and load optimizationPlanning tools that optimize routes and loads against time windows, vehicle capacity and hours of service.Operations analyticsDashboards for on-time delivery, cost per shipment, dwell time and carrier performance.
Technologies and standards
Chosen for your project
Built on the cloud you already use. Choose yours to see the services involved; we recommend the full stack in the free technical consultation.
Table 2. Managed services for each layer, by cloud. The highlighted column is the one you chose.
| Layer | AWS | Azure | Google Cloud |
|---|---|---|---|
| Storage and warehouse | Amazon S3, Amazon Redshift | OneLake, Microsoft Fabric | Cloud Storage, BigQuery |
| Pipelines | AWS Glue, Amazon Kinesis | Azure Data Factory, Event Hubs | Dataflow, Pub/Sub |
| Machine learning | Amazon SageMaker AI | Azure Machine Learning | Gemini Enterprise Agent Platform |
| Generative AI | Amazon Bedrock | Microsoft Foundry (including Azure OpenAI), Azure AI Search | Gemini Enterprise Agent Platform |
| Dashboards | Amazon QuickSight | Power BI | Looker |
| Governance | AWS Lake Formation | Microsoft Purview | Dataplex |
Also runs on any of the three: Snowflake, Databricks, dbt, Fivetran, Tableau.
Machine learning and AI
- Python
- TensorFlow
- PyTorch
- scikit-learn
- Hugging Face
- LLM APIs
Data platforms
- Snowflake
- Databricks
- Google BigQuery
- dbt
- Apache Airflow
- Apache Kafka
Analytics
- Power BI
- Tableau
- Looker
MLOps and monitoring
- MLflow
- Evidently AI
- Langfuse
- Amazon SageMaker AI
- Azure Machine Learning
Questions
Common questions
about Data Analytics & AI
How do we know if our data is ready for AI?
We check it in the free technical consultation and confirm it with a data audit of volume, quality, labelling and access for your use case. Where the data isn't ready, we start with data engineering.
How do you stop a generative AI assistant from making things up?
We build it to answer from your approved documents, found at question time and cited (retrieval-augmented generation), showing each person only what they may see. Answer quality is measured on test sets before and after launch, and high-stakes outputs keep a person's review.
Will our data be used to train public AI models?
No. We use providers' business API terms, which exclude your data from model training, or host models in your own cloud account.
Should we start with dashboards or machine learning?
We usually start with data and dashboards: reliable pipelines and reporting give the clean data, and the baseline, that machine learning is later measured against.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
Explore more
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- Digital TransformationLean, agile, human-centred transformation that creates seamless experiences across every channel.
- Ecommerce SolutionsStrategy, design and development of commerce experiences that unify your customer journey.
Ready to talk about Data Analytics & AI?
Start with a free technical consultation: a detailed plan covering the right tech stack, architecture, timeline and budget for your project.
Start a project