End-to-end AI Engineering
Production-ready AI,
built on Databricks.
Anyone can build an AI demo in an afternoon. Only a few ships one that survives production.
Our Approach
AI built to become part of your business
Engineered for production from day one, not retrofitted for it after a successful demo. Everything we build sits natively inside the Databricks Lakehouse, which means your AI applications inherit the platform's governance, security, and data quality controls automatically.
That is the difference between an AI feature and an AI system your business can depend on.
Governance built in, not bolted on
Building natively on the Lakehouse means access control, lineage, and data quality are properties of the platform rather than a separate workstream. Unity Catalog governs the data the model sees the same way it governs the data the business sees. There is no second security model to maintain and no parallel stack to audit.
Evaluation designed in, not added after
Quality criteria are defined before the build starts, and structured evaluation runs against them throughout. Automated judges score outputs against criteria your domain experts set, so "is it good enough" becomes a measurable question with a documented answer instead of a matter of opinion in a review meeting.
Your team operates it when we leave
Structured knowledge transfer runs throughout delivery rather than as a handover phase at the end. The engagement is complete when your team can operate, extend, and govern the system independently, not when the code is committed.
Five build types. One architecture underneath them.
AI Assistants & Chatbots
RAG (Retrieval-Augmented Generation)
Agentic AI
End-to-end governance runs through Unity Catalog, with secure orchestration via Mosaic AI and AI Gateway. The architecture is modular enough to configure agents for specific departments, so finance, operations, or customer experience can each get a domain-specialised deployment in less than a week.
Internal Copilots
Workflow Automation
From use case to production, with quality built in.
What migrations deliver in practice
From snow guns to smart systems: TechnoAlpin’s data transformation with Datapao
Common questions before building production AI.
What types of AI applications can you build on Databricks?
We develop enterprise AI assistants, RAG systems, agentic AI solutions, internal copilots, and intelligent workflow automation. Whether you need a knowledge assistant grounded in company data, specialised agents handling complex tasks, or AI embedded into business processes, we design the solution around your actual requirements rather than forcing a particular technology.
Do we need agentic AI, or would a simpler AI solution work?
Not every problem needs a team of AI agents. Sometimes a well-designed RAG system or a straightforward automation delivers better results with less complexity and lower operating costs. We assess your use case, data, and business requirements before recommending an architecture. If agentic AI adds genuine value through multi-step reasoning, orchestration, or specialised tasks, we'll show you where and why.
Can your AI solutions integrate with our existing business applications?
Yes. We build AI systems that connect to your existing data sources, APIs, enterprise applications, and business workflows. Whether you're integrating with a CRM, internal knowledge base, operational system, or employee-facing application, we focus on making AI useful within the tools and processes your teams already rely on.
What happens after the AI application goes live?
We make sure your team can operate what we've built. Every engagement includes documentation, knowledge transfer, and operational handover. We can also provide continued engineering support as your requirements evolve, but our goal is to leave you with a system your organisation can confidently own and extend.
