Enterprise 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. Where trained models meet production, this works hand in hand with our infrastructure automation & DevOps practice.

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 & Generative AI Integration

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.

The choice between RAG and fine-tuning comes up early, and for most knowledge use cases the answer is RAG: it keeps facts in a store you can update instantly and cite, where fine-tuning teaches tone and format but goes stale as your content changes. Getting retrieval quality, grounding, and honest abstention right is where these projects are won or lost — we cover that ground in detail in our guide to RAG in production.

MLOps 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.

How much of this a given model needs depends on its stakes: a search-ranking model and a credit-decision model do not warrant the same governance. We scale the discipline to the blast radius rather than imposing it uniformly, an approach we set out in getting models to production and keeping them honest.

AI on Oracle — Select AI, 23ai Vector Search & Autonomous Database

For enterprises whose crown-jewel data already lives in Oracle, the most pragmatic place to run AI is often the database itself. Oracle Database 23ai adds a native vector data type and similarity search, so retrieval for RAG can run against embeddings stored next to the source rows — inheriting the same row-level access controls, backups, and Data Guard standby you already operate, and removing the second data store that a bolt-on vector database would otherwise require. On Autonomous Database, Select AI adds natural-language querying: the data stays put and the generated SQL executes under the calling user's own privileges.

None of it is automatic — retrieval relevance still depends on chunking and content quality, and Select AI is only as good as your schema naming and comments. But keeping AI close to governed data removes the single biggest objection to production AI, which is moving that data somewhere less controlled. We work through the trade-offs alongside our Oracle Database & DBA support practice, and go deeper in AI on Oracle.

AI-Driven Operations (AIOps)

The same modelling techniques that power our AI products can be turned inward, onto the job of running infrastructure. Applied honestly, AIOps is not autonomous self-healing — it is noise reduction and correlation that collapse forty alerts into one incident, dynamic anomaly detection that learns a metric's seasonal baseline instead of relying on a static threshold, and log clustering that surfaces a never-before-seen error pattern as latency climbs. Each shortens the time between something going wrong and an engineer understanding why, which is where most outage duration actually hides.

We keep a human in the loop for anything consequential and insist that correlations and anomaly scores come with an explanation a responder can sanity-check. This work sits at the join between AI and our infrastructure automation & DevOps practice; our writing on using AI to run infrastructure covers what genuinely works and what to be sceptical of.

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.

Can you integrate an LLM with our Oracle database?

Yes, and if your data already lives in Oracle it is often the cleanest option. Oracle Database 23ai includes native vector search, so retrieval can run against embeddings stored next to the source rows and inherit the same row-level access controls, backups, and standby you already run. On Autonomous Database, Select AI adds natural-language querying where the data stays in the database and the generated SQL executes under the calling user's privileges. We work through which approach fits your estate rather than defaulting to a separate AI stack.

RAG or fine-tuning — which do we need?

For most enterprise knowledge use cases, retrieval-augmented generation. RAG keeps facts in a store you can update instantly and cite, which is what you want when answers must be current and verifiable. Fine-tuning is better at teaching a consistent tone or output format than at injecting facts, and it goes stale when your content changes. The two are complementary, and starting with RAG keeps your options open while costing far less.

What is MLOps, and do we need it?

MLOps is the operational discipline that keeps a model trustworthy after launch: versioning of code, data, and weights so any prediction is traceable; reproducible training pipelines; evaluation gates before promotion; and monitoring for drift in inputs and outputs. A model can be completely wrong while every server is healthy and every dashboard is green, so yes — any model making real decisions needs at least the minimum: know what version is running, know whether it still works, and be able to roll back. We scale the process to the stakes rather than imposing it uniformly.