AI
AI & Data Science
Practical AI and the data foundations it needs, from first use case to production.
Start with an AI use-case workshopWhat we solve
Sound familiar?
Lots of AI ideas, but no clear view of which one is worth doing first.
Pilots look promising but never reach production.
Data sits in silos and is not ready for AI.
Reports are slow, manual or not trusted.
Concerns about accuracy, security and governance hold projects back.
What we deliver
AI & Data Science, end to end.
AI strategy & use cases
Map where AI can help and agree which use case to start with.
Prototype & proof of value
A working prototype on your own data before a full build.
AI agents & automation
Agents that take on defined tasks, with guardrails and human review.
Agentic workflows
Agents and people connected across a whole process, with an audit trail.
AI platforms & operations
Deployment, monitoring, evaluation and governance on a shared platform.
Data foundations
Pipelines, a modern data platform and data governance.
Analytics & BI
Dashboards and models that answer real business questions.
MLOps
Versioning, testing and monitoring for models in production.
Our AI team
The people who build it.
AI Architects
Design the solution, the data flow and the guardrails before anything is built.
Forward Deployed Engineers
Embed with your team and take AI from prototype into your real systems and workflows.
AI Generalists
Build, evaluate and improve models, prompts and agents day to day.
Engaged as an AI pod that works alongside your team, with specialist AI talent available through our staffing team.
Where AI helps
Practical use cases.
Customer support agents that answer routine questions and hand complex cases to your people.
Document processing that reads, extracts and checks data from forms, contracts and invoices.
Knowledge assistants that answer staff questions from your own policies and documents.
Forecasting and analytics that help teams plan demand, staffing and spend.
Workflow and back-office agents that take on repetitive steps in a process.
Developer productivity: AI assistance for code, tests and documentation.
Built in-house
We build AI products, too.
In development
Our team is building Aivaros, a complete AI-powered workforce management platform. Building our own product keeps our engineers close to the real challenges of shipping AI: data, reliability, security and cost.
Responsible AI
Safe to put in front of your business.
Data privacy
Your data stays in your environment where required.
Evaluation & testing
Every model and agent is tested against your own examples before release.
Guardrails
Clear limits on what AI can see, say and do.
Human review
People stay in the loop where decisions matter.
Access control
AI only reaches the systems and data it is allowed to.
Cost monitoring
Usage and spend tracked, so costs stay predictable.
How we deliver
Four phases. Clear deliverables at each.
Phase 1: Assess
Use-case workshops and a review of your data.
Deliverables
Prioritized use cases and a data readiness view
Phase 2: Design
Solution design, data flows and evaluation criteria.
Deliverables
Architecture and success measures
Phase 3: Build
Prototype first, then production build with testing.
Deliverables
A working prototype, then a production release
Phase 4: Run & hand over
Monitoring, evaluation and governance in production.
Deliverables
Monitoring, runbooks and a trained team
Technology
Technology we work with.
- Python
- Azure OpenAI
- Amazon Bedrock
- Google Vertex AI
- LangChain
- Databricks
- Snowflake
- dbt
- Apache Spark
- MLflow
- Power BI
Ways to engage
Start where it makes sense.
Assessment
A focused, fixed-scope starting point that ends in a clear plan.
Project delivery
We own the outcome from design to go-live.
Dedicated team
A TMT pod working inside your team, on your priorities.
Specialist talent
Individual specialists through our staffing team.
Explore talent
FAQ
Questions clients ask.
Where do we start?
With a use-case workshop or a pilot on your own data, so you see real results before committing to a full build.
Can you work alongside our team?
Yes. Most engagements mix our engineers with yours, and we hand over fully at the end, with documentation and knowledge transfer.
How is delivery staffed?
A US-based lead with a delivery team in India, so work continues across time zones.
Who owns what we build?
You do: code, infrastructure definitions and documentation.
Is our data safe?
Yes. We design for your security and privacy requirements from the start, keep data within your approved environments where required and add governance before anything reaches production.
Which models do you use?
We work across major model providers and choose per use case, including options that keep data in your cloud.
How do you measure quality?
We agree success measures with you at the design stage, test against your own examples before release and keep evaluating in production, with human review where accuracy matters most.
Let’s connect
Have a challenge worth solving?
Let’s explore how TMT can help you turn possibility into progress.