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Most AI projects fail because the data platform was never engineered for production. BNS builds the foundations first — governed data, golden pipelines, evaluation harnesses — then ships AI you can put in front of customers.
Trusted data platforms and production AI that moves the P&L — not the lab.
We design data platforms, MLOps foundations and applied AI products that deliver measurable business outcomes, with governance and explainability built-in.
BNS builds the data platforms, MLOps foundations and applied AI products that move the P&L — not the lab. The fastest AI teams we see are not the ones with the best models; they are the ones whose platforms made the models boring. Governed retrieval, lineage-aware pipelines, and evaluation harnesses are delivered before product teams plug in — so AI lands inside model-risk, not around it, and integrates with the cloud platform your engineering organisation already runs on.
Our seniors have been through model-risk committees, regulator audits and drift incidents. Every model ships with an evaluation product, a drift budget and a retraining economics model — signed off with the client's risk panel. Human-in-the-loop by default; autonomous only where the telemetry earns it.
Data inventory, candidate use-cases, ROI model and a platform reference architecture.
Lakehouse, ingestion, golden pipelines, a semantic layer and eval harness.
Production model or AI product behind feature flags with measurable business metrics.
Model risk committee integration, drift monitoring and retraining economics.
Lakehouse and warehouse builds on Snowflake, Databricks and BigQuery with lineage and access control.
Semantic layers, dbt models and self-serve BI that your business partners will actually trust.
Feature platforms, training pipelines, eval harnesses, drift detection and model governance.
RAG, agentic workflows and fine-tuning on your proprietary data with guardrails and red-teaming.
Forecasting, optimisation and simulation embedded into operational workflows.
Model risk, policy-as-code, PII controls, and EU AI Act readiness.
BNS is technology-neutral by policy — our engineers work across the open-source and commercial stacks that enterprise platforms actually run on.
Every platform ships with lineage, access control and a documented semantic layer before product teams get access. Regulators and model-risk committees see a single source of truth. Clients pass clinical and financial audits on the first pass, with 100% of models under drift monitoring.
Acceptance criteria, evaluation harnesses and audit trails are co-designed with your model-risk panel before training starts. We engineer the sign-off surface, not just the model. Production deployment typically lands 6 weeks after data audit — without a second committee cycle.
Eval harnesses are owned by a team, run on every merge, and carry SLAs that match your release cadence. Regression in model quality is caught before a product manager sees it. Evaluation is a capability your platform develops, not a milestone on a slide.
Retrieval corpora are versioned, access-controlled and lineage-tracked — with PII handling, residency controls and document-level audit. Agentic workflows get the context they need without inheriting your legacy data risks. RAG becomes a platform capability, not a prompt-engineering exercise.
Every production model ships with drift detection, retraining triggers and a unit economics model. Operational cost drops by 31% on average after co-pilot rollout — because the telemetry catches regressions before they compound. Models that earn their keep; models that don't are retired early.
Our AI practice leaders have shipped production AI inside banks, insurers, clinical systems and regulated infrastructure. They have been through MRM, clinical safety panels and regulator audits — so the architecture is engineered for the sign-off, not just the demo.
OpenAI, Anthropic, Llama, Vertex AI, Azure AI Foundry — we work across all major providers and open-source stacks. The choice is driven by residency, economics and evaluation results, not partner pressure. Fine-tuning, retrieval and bespoke models are options we pick for a reason.
Live AI workloads across finance, healthcare, government, energy and logistics — delivered from four regional hubs with sovereign cloud, data clean rooms and regional model endpoints. EU AI Act, HIPAA and FINRA-aligned engagement postures are part of the standard template.
Model risk, PII controls, evaluation gates and fairness monitoring live in the pipeline, not in a document. Every prompt, response and decision is logged. Audit readiness is an engineering property — not a quarterly fire drill before the regulator arrives.
Measurable outcomes from the platforms our engineers shipped this year.
Most enterprise AI projects stall because the platform was never built to run models in production. A pragmatic blueprint for the foundation layer.
The hardest part of shipping AI in healthcare or financial services is not the model. It is proving that you did the uninteresting things right.
Boards over-index on burn-rate and under-index on delivery signal. The five charts that change the room.
Where it makes sense. For most enterprise use-cases, well-engineered retrieval, fine-tuning and evaluation beat bespoke models on cost and speed.
We design for it up front — including sovereign clouds, data clean rooms, and regional model endpoints.
Landing zones, platform teams and SRE practices that make cloud a business advantage.
Custom business applications engineered for the load, risk and scale of your organisation.
Engineering-grade security: embedded into the platform, not bolted on top.
Share your brief and we’ll assemble two senior data & ai engineering practitioners for a 90-minute working session — no deck, no sales.