AI and data platform
development
you can explain.
Data pipelines, ML and agentic features built on governed, auditable foundations, evaluated before launch and monitored after.
Trusted on systems that cannot fail
Certified, accredited and independently reviewed
Real AI outcomes start with the right foundation.
You know what AI and data are supposed to deliver for your business. The harder question is what it takes to realise that value: the strategy that prioritises the right investments, the infrastructure that can scale them, the data foundation that makes them trustworthy, and the governance that keeps them safe.
Get the foundation right, and your AI programme compounds. Get it wrong, and you spend years rebuilding.
Data engineering and AI services,
from architecture to monitoring.
One team, accountable for the whole lifecycle, not a slice of it.
Data architecture
Warehouse, lakehouse and streaming designs sized to the questions you need answered.
Pipelines and ELT
Reliable, observable ingestion and transformation, tested like production code.
ML and model engineering
Models built, evaluated and versioned against clear, measurable acceptance criteria.
RAG and agentic features
Retrieval and agent workflows wired into your data, with guardrails and evaluations.
MLOps and monitoring
Deployment, drift detection and monitoring so models stay trustworthy in production.
Governance and lineage
Lineage, access control and audit trails built in from the first pipeline.
How AI development transforms your business.
Where AI earns its place: anticipating trends, identifying opportunities, solving complex problems and mitigating risk on data you already hold.
Better decisions
Forecasts and scenarios built on data you already hold, so planning stops relying on last quarter's spreadsheet.
Lower operating cost
Repetitive judgement work automated where the evidence supports it, with the cost per decision tracked.
Fewer human errors
Checks that never get tired, applied consistently to every record, image and transaction.
Risk anticipated earlier
Drift, anomalies and failure patterns surfaced while they are still inexpensive to act on.
Workforce allocated better
Specialists spend their time on the exceptions rather than on the routine cases.
New revenue from existing data
Products and services built from data already in your systems, with lineage you can show a buyer.
Spec-driven, AI-assisted
delivery, governed end to end.
AI agents build. Senior engineers hold the gates. The framework that turns AI speed into governed, predictable delivery, from the first call through to support after launch.
Assess
We map the system, the data and the constraints. Honest, fast, no obligation.
What this phase covers
- Data inventory - what exists, who owns it, and what quality it is actually in.
- Use-case shortlist - the cases where AI earns its place, and the ones where it does not.
- Foundation gap - the smallest platform the first use case genuinely needs.
- Risk register - residency, consent, sector obligations and model risk, named up front.
Spec
Requirements become behavior-first specifications before any code. For a model, that means accuracy, latency, cost and failure behavior written down and agreed.
What this phase covers
- Behavior-first specification set, reviewed and signed off by your team.
- Evaluation criteria per model: accuracy, latency, cost and failure behavior.
- Data contracts and lineage boundaries between source systems and the platform.
- Iteration plan, including what has to be ready on your side and when.
Build
AI agents build from the specs; senior engineers hold every gate. Pipelines are tested like production code, not run once by hand.
What this phase covers
- Ingestion and transformation pipelines with tests running in CI.
- Models and retrieval built against the agreed acceptance criteria.
- Infrastructure as code for each environment, versioned with the application.
- Weekly status against the plan: coverage, risks and changes since last week.
Govern
Tests, hooks and reviews enforce the standard continuously in CI. The evaluation harness runs on every change, and the gate is explicit rather than a judgement call at the end.
What this phase covers
- Evaluation harness running in CI, with the pass threshold agreed in the spec.
- Lineage from a result back to the inputs that produced it.
- Access control and audit trails on every dataset and model.
- Models, prompts and evaluation sets versioned like code.
Ship and scale
Release, observe and harden. Drift detection and monitoring keep models trustworthy once real data starts arriving.
What this phase covers
- Drift detection and monitoring on live models and pipelines.
- Runbooks for retraining, rollback and incident response.
- Architecture and operations documentation that matches what shipped.
- Knowledge transfer with your engineers, recorded.
What we build it with.
Chosen for what your team can operate and hire for, not for what is new.
Data and pipelines
- Python
- Spark
- dbt
- Airflow
- Kafka
- Debezium
Storage and serving
- PostgreSQL
- BigQuery
- Snowflake
- Databricks
- pgvector
- Redis
ML and model engineering
- PyTorch
- TensorFlow
- scikit-learn
- ONNX
- TensorRT
- MLflow
LLM, agents and evaluation
- Claude
- LangChain
- LlamaIndex
- Weaviate
- Ragas
- OpenTelemetry
Edge-GPU vision,
validated pre-launch
A US manufacturing company needed high-throughput industrial sorting running on edge GPUs.
Edge-GPU vision, evaluated against production tolerances before it went live.
A US manufacturing company needed high-throughput industrial sorting running on edge GPUs. We built the pipeline, the model and the evaluation harness together, so accuracy and latency were proven against production tolerances rather than a held-out test set.
- Detection accuracy
- 98-99%
- Lower latency
- 40-60%
"Their computer vision team understood high-throughput industrial requirements from day one."
How we work together
Every engagement runs on the Agentic SDLC, with senior engineers at every gate.
Data platform build
The foundation has to exist first.
AI feature delivery
A defined capability, evaluated before launch.
Embedded data pod
Continuous pipeline and model work.
Every engagement starts with an assessment. It is fixed price, it ends in a written plan, and the plan is yours whether or not you continue with us.
Certified, accredited and independently reviewed
- Built to SOC 2 Type II criteria
- GDPR-compliant processing, DPA available
- NDA and IP assignment as standard
Explore other services
All servicesFrequently asked questions.
Something not covered here? Ask an engineer directly, no SDRs, no funnel.
Do we need a data platform before we do AI?
Usually some of one. We scope the smallest foundation the first use case actually needs, rather than a two-year warehouse programme before anything ships.
How do you evaluate a model before launch?
Acceptance criteria are written into the spec: accuracy, latency, cost and failure behavior. The evaluation harness runs in CI and the gate is explicit.
Can our data stay in our own tenancy?
Yes. We build in your cloud accounts under your access controls. Nothing leaves your tenancy without a decision you made.
What about model and prompt versioning?
Models, prompts and evaluation sets are versioned like code, with lineage from a result back to the inputs that produced it.
Who owns retraining after handover?
Your team, with runbooks and monitoring we set up. We can stay on a support retainer if you would rather phase it.
Let's Work Together
Tell us what you're building. Our engineers will respond within 1 business day with a concrete next step - no sales script, no obligation.
- Email[email protected]
- Phone(+84) 246.276.3566
- Response TimeWithin 1 business day