AI solutions
AI that does real work for your business
Tanzo Technologies designs, builds and runs AI solutions, from customer-facing assistants to models embedded in your operations. We start with the business problem, not the technology.
Because we are cloud and DevOps engineers too, every solution is secure, tested and monitored, so it reaches production and keeps working.
Build
AI agents, chatbots and generative AI features connected to your systems and data.
Run
MLOps, monitoring and cost control so models stay accurate, fast and affordable.
Advise
Strategy, readiness and governance so you invest in the right things, safely.
Our AI services
From the first idea to a monitored production service.
AI agents & chatbots
Assistants that answer customers, support staff and complete routine tasks, connected securely to your own systems and knowledge.
- Customer service and internal helpdesk assistants
- Agents that take actions in your CRM, ticketing or ERP
- Grounded in your documents with retrieval (RAG)
- Human hand-off, guardrails and audit logs
Generative AI & LLM integration
Add large language models to your products and workflows to summarise, draft, classify and extract, safely and at a predictable cost.
- Azure OpenAI, AWS Bedrock and Google Vertex AI
- Document processing and data extraction
- Prompt design, evaluation and testing
- Cost, latency and quality monitoring
MLOps & model deployment
Take models from notebooks to reliable production services, with the same automation we use for software.
- Model training and deployment pipelines
- Model registry, versioning and rollbacks
- Drift, accuracy and performance monitoring
- SageMaker, Azure ML, Vertex AI or open source
AI strategy & readiness
Decide where AI will create real value, check that your data and systems are ready, and plan what to do first.
- Use-case workshops and prioritisation
- Data, infrastructure and skills readiness review
- Build, buy or partner recommendations
- A costed 6–12 month roadmap
Data engineering & platforms
Reliable data foundations: pipelines, warehouses and lakehouses that analytics and AI can trust.
- Snowflake, Databricks, BigQuery or Microsoft Fabric
- Batch and streaming pipelines with dbt and Airflow
- Data quality checks, lineage and cataloguing
- Dashboards in Power BI, Looker or Tableau
AI governance & responsible AI
Use AI safely and in line with regulation, with clear policies your teams can follow.
- AI usage policy and risk framework
- UK GDPR and data-protection alignment
- Guidance on emerging regulation such as the EU AI Act
- Bias, transparency and model risk reviews
Where AI helps most
Common starting points for growing businesses.
Customer service
An assistant that answers common questions on your website and hands complex cases to your team.
Internal knowledge
Staff ask questions in plain English and get answers from your policies, manuals and past tickets.
Document processing
Invoices, forms and contracts read, checked and entered into your systems automatically.
Sales & operations
Forecasts, lead scoring and reports generated from your own data.
From idea to production in weeks
A typical first AI project.
Week 1: Discover
Workshops to agree the problem, the users and how success will be measured.
Week 2: Design
Data, architecture, security and cost reviewed, and a fixed-price plan agreed.
Weeks 3–5: Build
A working solution on your own data, demoed every week.
Week 6: Launch
Released to real users with monitoring, guardrails and handover.
Built on your cloud, under your control
We deploy AI inside your own AWS, Azure or Google Cloud account, so your data stays where you can govern it.
- Your data is never used to train public models
- Access controls, audit logs and content guardrails
- Usage and cost dashboards from day one
- Deployed and updated through tested pipelines
Frequently asked questions
Can't find your answer? Ask us directly.
We don't have much data. Can we still use AI?
Often, yes. Many valuable use cases work with modest, well-organised data or with pre-trained models. A readiness assessment shows what's realistic now and what to build towards.
How do DevOps and AI fit together?
AI projects fail most often at the point of getting into production. We apply the same automation, testing and monitoring practices to data and AI work as to software, so ideas reach real users and keep working.
How much does an AI proof of concept cost?
We scope each proof of concept to a fixed price agreed up front, typically covering a 2–4 week sprint. You'll know the cost before any work starts.
Is our data safe?
We work inside your own cloud accounts under a data-processing agreement, follow UK GDPR, and never use your data to train third-party models.
Have an AI idea in mind?
Book a free 30-minute call. We'll talk through the idea, check it's feasible, and suggest the quickest way to prove it.