AI & Machine Learning
Bring AI into production safely. We help enterprises build data pipelines, integrate generative AI and large language models, operationalize machine learning (MLOps), and apply AI to automation, analytics, and threat detection — across AWS, Azure, OCI, and Oracle AI.
What we deliver
- Generative AI and LLM integration (RAG, copilots, chat assistants)
- MLOps — model training, deployment, and monitoring pipelines
- Data engineering and feature pipelines for AI workloads
- AI on the cloud — AWS Bedrock/SageMaker, Azure OpenAI, OCI AI Services
- Oracle AI, Autonomous Database Select AI, and vector search
- AI-driven automation, analytics, and anomaly detection
Data readiness comes first
Most enterprise AI projects that stall do so for reasons that have nothing to do with models. The data is scattered across systems that disagree with each other, key fields are inconsistently populated, nobody can say authoritatively which source is correct, and access controls were never designed for a system that reads everything at once. No model choice compensates for that.
So we start there. We assess what data you actually hold, what condition it is in, and what would need to be true for a given use case to work — then build the pipelines, quality checks, and lineage that make the answer trustworthy. This is unglamorous engineering, and it is the difference between a pilot that survives contact with the business and one that quietly gets switched off. Much of it draws on the same database and integration work covered under ERP development & automation.
LLM integration into systems you already run
The useful enterprise applications of large language models are rarely standalone chatbots. They are retrieval over your own documentation, drafting inside an existing workflow, classification and routing of inbound work, or summarisation of records a person would otherwise read one at a time. The value comes from the connection to your systems, not from the model itself.
We build that connection carefully. Retrieval-augmented generation is only as good as its index and its chunking, so we tune both against real questions rather than samples. Permissions are enforced at retrieval time so a model never surfaces material the requesting user should not see. Outputs that drive a downstream action are validated rather than trusted. And we scope deliberately: an assistant that does one thing reliably is worth more than one that does ten things you have to check.
MLOps and keeping models honest in production
A model that performed well in evaluation is making a claim about a world that no longer exists the moment it goes live. Inputs shift, business processes change, upstream data pipelines are modified by someone who did not know a model depended on them. Without monitoring, degradation is invisible until someone notices the predictions have been wrong for months.
We put the operational layer in place: versioned models and datasets so any prediction can be traced back to what produced it, reproducible training pipelines, automated evaluation against held-out data before promotion, and monitoring for drift in both inputs and outputs. Rollback is a first-class capability. We also make sure a human path exists for the cases the system should not decide alone — a design question that matters more than any accuracy metric.
Common questions
Where should we start if we have no AI work in production yet?
Pick one narrow, measurable use case where you already have decent data and a clear definition of a good outcome. Document search, ticket classification, and report summarisation are common first projects because success is easy to judge. Starting narrow gets you a real assessment of your data and infrastructure at low cost, which informs everything after it.
Can we use AI without sending our data to a public model provider?
Yes. Options range from cloud services that run within your tenancy and contractual boundary — AWS Bedrock, Azure OpenAI, OCI AI Services — through to open-weight models hosted on infrastructure you control. The right choice depends on your data sensitivity, latency requirements, and budget, and we work through that trade-off before selecting anything.
How do you handle accuracy and hallucination in production systems?
By constraining what the system is allowed to do. Ground answers in retrieved source material and cite it so users can verify. Validate structured outputs against a schema before they reach another system. Route low-confidence cases to a person. Then measure quality continuously against real usage rather than assuming a launch-day evaluation still holds.