Ship AI software that actually runs in production

Most machine learning projects stall between prototype and deployment. Vertex builds the bridge — production-grade pipelines, real-time inference, and monitoring that keeps models honest after launch day.

Run your first model free

Why most projects fail

The gap between a notebook demo and a live system

Research teams build brilliant prototypes. Then reality arrives: scaling, latency budgets, data drift, compliance. Eighty-seven percent of ML projects never reach production. We exist to close that gap with purpose-built AI software infrastructure.

What goes wrong

  • Models trained on static snapshots degrade within weeks of deployment
  • Engineering teams rebuild pipelines from scratch for every new experiment
  • Monitoring is bolted on as an afterthought, leaving silent failures undetected
  • Compliance and audit trails are missing, blocking regulated-industry adoption
  • Infrastructure costs balloon because serving architecture was never optimised

How Vertex fixes it

  • Continuous retraining loops with automated drift detection keep models accurate
  • Modular pipeline templates cut experiment-to-production time by up to 70 percent
  • Observability is native — every prediction is logged, scored, and alertable
  • Immutable lineage tracking satisfies FCA, ICO, and ISO 27001 auditors
  • Adaptive autoscaling reduces inference costs while meeting latency SLAs

What we build

Six capabilities, one integrated platform

Predictive analytics engines

We design forecasting systems that ingest structured and unstructured data — sales figures, sensor telemetry, free-text customer feedback — and output actionable predictions with calibrated confidence intervals. Unlike dashboard-only tools, our engines feed directly into downstream decision systems such as pricing optimisers and inventory planners, closing the loop between insight and action. Every model ships with an explanation layer so stakeholders can interrogate why a prediction was made.

Real-time inference APIs

Sub-50ms response times backed by GPU-accelerated serving, batched request queuing, and graceful fallback to lighter models under load spikes.

Data pipeline orchestration

Declarative DAGs that handle ingestion, transformation, validation, and feature-store updates — scheduled or event-driven, with built-in retry logic.

Drift and anomaly monitoring

Statistical tests run on every batch of live predictions, flagging concept drift before it erodes business metrics. Alerts route to Slack, PagerDuty, or custom webhooks.

Custom model development

From NLP classifiers to computer-vision inspection systems, our research engineers build bespoke architectures tuned to your domain data and performance targets.

Vertex AI Software production server infrastructure with violet ambient lighting

From brief to production

Four phases, no hand-waving

Every engagement follows a structured path so you always know what happens next, what it costs, and when it ships. We do not hide behind agile-flavoured ambiguity.

Discovery audit

We map your data estate, identify high-value prediction targets, and quantify expected ROI before writing a single line of code. This phase typically takes five business days and includes a feasibility report you own regardless of next steps.

Rapid prototyping

A working model on real data within three weeks. We validate performance against your success criteria and iterate until the metrics pass. No prototype leaves this phase without a documented evaluation benchmark.

Production hardening

The model is wrapped in fault-tolerant serving infrastructure, integrated with your existing systems via API or event stream, and load-tested to contractual SLA thresholds.

Ongoing stewardship

Continuous monitoring, retraining triggers, and quarterly model reviews ensure performance stays above baseline. You get a live dashboard and a dedicated technical account manager.

99.97%
Platform uptime (trailing 12 months)
142
Models in active production
3.8 bn
Predictions served last quarter
<38 ms
Median inference latency

Common questions

Straight answers, no jargon

Our clients range from Series-B startups to FTSE-250 enterprises. The common thread is not company size but data maturity — if you have structured data and a clear business question, we can likely help. We have delivered projects for teams as small as three engineers and as large as multi-hundred-person platform organisations.

No. We operate as a fully embedded squad — research engineers, ML ops specialists, and a project lead. If you do have an internal team, we integrate with their workflow and hand over ownership at the end of the engagement, including full documentation and training sessions.

Discovery audits are fixed-fee. Build phases are priced on a time-and-materials basis with a capped ceiling agreed upfront. Ongoing stewardship is a monthly retainer scaled to inference volume and retraining frequency. We never charge per prediction — that model creates perverse incentives to limit usage.

By default, all workloads run in UK-region cloud infrastructure (AWS eu-west-2 or GCP europe-west2). For clients with stricter requirements, we support on-premise deployment and air-gapped environments. Data never leaves the jurisdiction you specify, and we hold ISO 27001 certification.

Yes. We have production connectors for Snowflake, BigQuery, Databricks, Kafka, RabbitMQ, and most REST or gRPC-based internal services. During discovery we map your integration surface and flag any compatibility concerns before build begins.

Start a conversation

Tell us what you are trying to predict

Whether you have a clearly scoped project or just a hunch that your data could work harder, we are happy to talk. The first call is always free and comes with zero obligation.

Visit us
778 Water Lane, Zulauf-on-VonRueden-Hilpert, England, GO50 0TL, United Kingdom

Ring us
+44 337 343 5075

Email
[email protected]

Vertex AI Software engineers collaborating on a data project