One partner for the entire data, analytics and AI lifecycle.
Six integrated practices, one delivery model. From a two-week discovery sprint to a managed enterprise data & AI platform — Data Nectar owns the outcome, not just the sprint.
AI, ML & advanced analytics.
From descriptive dashboards to predictive and prescriptive intelligence — we pick the right technique per use case and ship models that operate reliably in production, not just in notebooks.
Demand, revenue, risk, churn — time-series at scale.
Pricing, routing, allocation, scheduling.
Document AI, classification, OCR, defect detection.
CI/CD for models, drift detection, automated retraining.
- Use-case discovery & ROI sizing
- Production-grade models with SLA
- Feature store & model registry
- Monitoring & retraining loops
Generative AI engineering.
Enterprise copilots, retrieval pipelines and autonomous agents built on a governed AI fabric — with guardrails, evaluation harnesses and human-in-the-loop where it matters.
Hybrid retrieval over docs, tickets, code, drawings.
Tool-using agents wired into your operational systems.
Domain copilots embedded in BI, CRM and field apps.
Eval, redaction, policy, audit — G-Fence pattern.
- LLM gateway & model routing
- Vector index & retrieval pipeline
- Eval harness with golden sets
- Cost, latency & safety dashboards
Data engineering & the lakehouse.
Reliable, scalable, governed data platforms — open formats, declarative pipelines and clear contracts between producers and consumers so the platform compounds rather than rots.
Delta / Iceberg on Databricks, Snowflake, BigQuery.
Kafka, Kinesis, Spark Structured Streaming.
dbt, dimensional & data-vault patterns.
Tests, SLAs, lineage, contracts, alerting.
- Reference architecture & ADRs
- Ingestion & transformation pipelines
- Catalog, lineage and access model
- Cost & performance baseline
BI & data visualisation.
Self-service analytics, executive dashboards and embedded analytics built on a single semantic layer — so every team trusts the same number and acts on the same definition.
One model, one metric, every tool.
Executive, operational and embedded surfaces.
Curated marts, governed exploration.
Reverse-ETL, alerts, data products.
- Metric catalog & semantic model
- Persona-driven dashboard suite
- Embedded analytics SDK
- Training & enablement runbook
Data strategy & consulting.
Roadmaps, governance frameworks and target operating models that align data investments to outcomes — and make the next 18 months executable, not aspirational.
Maturity, gaps, quick wins, anchor bets.
Org design, RACI, capability map.
Catalog, ownership, MDM, policy.
Sequenced delivery with measurable KPIs.
- Current-state assessment
- Target architecture & TOM
- 18-month executable roadmap
- Governance & policy charter
Digital transformation.
End-to-end modernisation across application, data and AI layers — delivered by senior multidisciplinary teams who own outcomes, not just sprints.
Decompose, replatform, replace.
Landing zones, FinOps, security by design.
Golden paths, IDP, self-service.
Adoption, training, ops handover.
- Domain decomposition & strangler plan
- Cloud landing zone & guardrails
- Internal developer platform
- Adoption & enablement programme
How we work.
The same four-phase rhythm across every engagement — small enough to start in two weeks, structured enough to scale across a multi-year programme.
Outcomes, constraints, data, risks. Define success in numbers.
Reference design, ADRs, contracts. Pick the right tools.
Sprints with working software fortnightly. Reviews & demos.
Observability, enablement, handover. We design for day 90.
Start small. Scale with confidence.
Working diagnosis, target architecture and a costed 6–12 month plan.
A senior, multidisciplinary pod that ships measurable outcomes every fortnight.
We run your data & AI platform with SLOs, observability and continuous tuning.
Tell us the outcome. We'll bring the team.
A response within one business day with a tailored proposal, indicative roadmap and the senior people who will actually deliver the work.