Ingestion, warehousing, pipelines, model serving and drift monitoring — built to stay reliable after launch. Delivered by named senior engineers, fixed scope, staging from week one.
Every source is characterised for replay and idempotency before a line moves, so restarts are safe, duplicates are impossible, and row counts reconcile at each hop.
Stars modelled from real reporting needs rather than copied schemas, with tested transformations, documented lineage and slow-changing dimensions handled deliberately instead of by accident.
Serving ships with feature and prediction drift tracked against baselines, so decay is caught by dashboards and alerts long before anyone notices in a business report.
Schema, volume and freshness checks run as pipeline stages, so bad data stops at the boundary and the offending batch is quarantined with a reconciled replay.
Spatial models, indexes and query plans tuned so map layers and catchment reports answer in seconds, with the same reconciliation discipline as any other pipeline.
Extraction, classification and routing at scale, with each field carrying its source page and confidence, so review effort concentrates where the model was least certain.
Window: 3-9 weeks
Team: 1 data eng + 1 ML eng
Complexity: M-L
Indicative only. A fixed price follows a free 30-minute scope review.
Get a fixed quoteWe profile each source for schema stability, replay behaviour and idempotency, and agree the reconciliation counts that will prove correctness before code is written.
Ingestion, transformation and load stages ship with idempotent offsets and row-level checks, and a wave is not signed off until counts reconcile end to end.
Dimensional models and serving endpoints go live behind the same gates, with query performance checked against the reporting workloads they were built for.
Feature and prediction drift, freshness and cost are monitored together, so decay and spend are caught while they are still cheap to fix.
Lineage, dashboards and reconciliation scripts transfer with a shadowing window, then thirty days of accompaniment while your team takes the controls.
Idempotent offsets and reconciliation counts kept every feed correct through months of continuous operation, with exchange-grade monitoring proving counts hop by hop.
Confidence scores and provenance per field cut review cycles from months to days, because reviewers saw exactly which extractions needed a human check.
Spatial models and tuned indexes turned catchment and coverage reports that once ran overnight into queries answered in seconds against reconciled data.
More evidence in the delivery history and the complete 734-engagement register.
Idempotent offsets and deterministic replay mean a restart or retry cannot duplicate a record, and reconciliation counts prove row parity at every hop.
Feature and prediction distributions are tracked against baselines, with alerts on divergence, so retraining is triggered by evidence rather than by a complaint.
Yes. We design spatial schemas, tune indexes and query plans, and apply the same reconciliation and quality gates used across the rest of the platform.
Extraction and classification pipelines that attach a confidence score and provenance to every field, so human review concentrates only where the model was least certain.
No account managers. Your message lands with the people who would deliver it, and you get a straight answer within one business day.
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