You already have the data, the systems and the ideas. What you need is AI that works inside them. We connect models and agents to the applications and workflows you run today, then evaluate, secure and monitor every one before and after launch.
Trusted by teams whose systems can't afford to fail
Results from recent AI work
You've seen the demo work. Then real data, real permissions and real users arrive, and the pilot stalls. That's because the model is only one part of the system. Production also depends on usable data, reliable pipelines, access rules that match your organisation and a plan for what happens when something fails.
So we start small and prove it. One bounded use case, connected to your real systems and measured against criteria you agree to up front. It returns answers with a traceable source, respects your access controls and runs at a known cost. Then we expand.
You shouldn't have to coordinate five vendors to ship one AI feature. One team owns the data and model layers, the application interfaces, the evaluation harness, deployment and monitoring.
Connect models and agents to the systems you already run, from ERP, CRM and SCADA to document libraries and partner platforms, through governed APIs, events and permission-aware interfaces.
Warehouses, lakehouses and streaming platforms designed around the questions your business needs answered, with data-quality checks, lineage and observability in every pipeline.
Give large language models access to your approved sources only. Every answer carries citations, respects access rules and is tested against evaluation sets.
Predictive and vision models trained, evaluated and versioned before they go live, with accuracy, latency, throughput and failure behaviour defined for your environment.
Agents that act with limited tool access and clear human approval points. Retry limits, fallback paths and audit logs decide what happens when an action fails or touches an external system.
Models, prompts and evaluation sets versioned like code, then watched in production for output quality, retrieval performance, drift, latency, cost and failures.
Where AI earns its place: anticipating trends, identifying opportunities, solving complex problems and mitigating risk on data you already hold.
Forecasts and scenarios built on data you already hold, so planning stops relying on last quarter's spreadsheet.
Repetitive judgement work automated where the evidence supports it, with the cost per decision tracked.
Checks that never get tired, applied consistently to every record, image and transaction.
Drift, anomalies and failure patterns surfaced while they are still inexpensive to act on.
Specialists spend their time on the exceptions rather than on the routine cases.
Products and services built from data already in your systems, with lineage you can show a buyer.
Our Agent-Centric Development Cycle uses AI throughout delivery. Senior engineers remain responsible for the architecture. Nothing passes a security gate or reaches production without their approval.
We study how the work moves today and where it breaks down. The result is a focused plan for the smallest use case worth proving.
We define measurable success before development begins. The specification makes the expected performance clear and sets firm boundaries around cost and risk.
We build around your existing systems instead of treating the AI model as a standalone feature. Access is controlled from day one. The workflow also includes human review and a clear recovery path.
We test the system against real scenarios, including the cases most likely to fail. Every change is versioned so teams can review it and trace its history.
We release through controlled gates and watch how the system performs in production. Real evidence determines what we improve next.
We pick tools for your systems, compliance needs, workload and the skills your team already has. A tool earns its place by fitting your environment, not by being new.
Python 
Spark 
dbt 
Airflow 
Kafka 
PostgreSQL 
BigQuery 
Snowflake 
Databricks 
Redis 
PyTorch 
Hugging Face 
scikit-learn 
ONNX 
TensorRT 
MLflow 
Claude 
OpenAI 
LangChain 
LlamaIndex 
Ragas 
OpenTelemetry Amatrium's technical teams needed to search thousands of specifications, compare documents side by side and translate files without breaking the original layout. We built AmatriumGPT on Azure with cited RAG search, multi-file investigation, format-preserving translation and production monitoring.
Every engagement starts with a fixed-price assessment. It ends with a written plan, and that plan is yours whether or not you keep working with us.
Where are you starting from?
Best for: Teams with a defined AI capability in mind that has to fit an existing product or workflow and pass evaluation before launch.
What you get
Best for: Organisations whose data isn't ready for analytics or AI yet, and need a governed foundation, pipelines and serving layer first.
What you get
Best for: Teams with continuous data-platform, model, integration and production-operations work, not a single project.
What you get
Certified, accredited and independently reviewed
Something not covered here? Ask an engineer directly, no SDRs, no funnel.
They connect AI models and agents to the data, applications and workflows your business already runs. That covers system interfaces and data pipelines, permissions and evaluation before release, and governance and monitoring after deployment.
Usually not. We define the smallest governed data foundation your first use case needs, prove it with representative data and only expand it when the next workload calls for more.
We write quality and latency targets into the specification, then add cost and failure behaviour for your environment. The evaluation harness runs representative cases and known failure modes in CI, and the release gate records whether the system passed.
Yes. We work inside your cloud accounts and access controls. Your data only leaves your tenancy if your architecture and security teams explicitly approve it.
We version models, prompts, retrieval settings and evaluation sets like code, so every production result can be traced back to the inputs and configuration that produced it.
You do. Your team gets the monitoring, the runbooks and full ownership of the system. If you would rather hand over gradually, we can stay on through an embedded pod or a support engagement.
An engineer, not a salesperson, will review it and get back to you within one business day with a concrete technical next step. No scripts, no obligation.
