ActiDash's clients were making business decisions on data 4–24 hours old. We rebuilt the ingestion and processing layer on a fault-tolerant Kafka streaming architecture — analytics dashboards now refresh on sub-minute cycles, delivered ahead of schedule with no scope reduction.
AI products fail on data before they fail on models.
Your pilot worked on a curated snapshot. Production data arrives schema-drifted, late, and inconsistently formatted. A third-party API changes without notice and an integration silently breaks. We build the pipelines, integrations, and data infrastructure that hold up once real traffic hits them — not just in the demo.
The data engineering problems that block AI and product roadmaps.
These come from engagements where an AI feature, an analytics rebuild, or a product launch stalled because the data layer underneath it was never treated as a first-class engineering problem.
Your pilot worked. Production data looks nothing like it.
Curated snapshots powered the demo. Live data arrives schema-drifted, late, and inconsistently formatted — and the feature degrades within weeks while the data team gets blamed for assumptions the model team made.
Every new integration adds a schema nobody owns.
Five upstream sources, five schemas, no canonical model the team trusts — just a growing list of one-off fixes whenever a provider changes their API without notice.
A pipeline breaks and you find out from a Slack message, not a monitor.
Schema changes that pass validation but corrupt downstream aggregates, late-arriving events that look like missing data — invisible until someone notices the numbers are off.
Your AI feature is only as good as the pipeline feeding it
Feature stores, vector databases, and embedding pipelines built without observability or data quality checks make LLM and ML applications behave unpredictably in production, not just in notebooks.
Batch pipelines built for daily summaries can't keep up with what the business needs now
Query latency that makes real-time dashboards impractical, decisions made on data that's already hours old — the pipeline was never designed for the latency the business actually needs.
Building new data infrastructure from the ground up
A new product or AI feature built with the right pipeline architecture, integration boundaries, and data quality controls from day one is faster to extend than three years of patches on a pipeline that was never designed for production scale.
What comparable engagements delivered.
A lightweight, cloud-agnostic IoT platform supporting high-throughput sensor data ingestion, multi-tenant device management, and secure OTA firmware updates — deployable across any cloud or on-premises environment, eliminating vendor lock-in.
A pharma supply chain forecasting model had been stuck in development for nine months. We rebuilt the pipeline with GxP-aligned validation, audit logging, and a kill-switch the QA team owns — deployed to production in 90 days with a validation package the regulatory team accepted without revision.
What we build for data-dependent teams.
Most engagements involve more than one of these. A scoping call is the fastest way to identify what the real problem is and which category of work will fix it.
Learn about Product Pilot →- Data pipeline engineering — ingestion, transformation, delivery
- AI data infrastructure — feature stores, vector databases, embedding pipelines
- Third-party integrations — APIs, webhooks, CRM/ERP/payment systems
- Analytics engineering — warehouse modelling, BI-layer design
- Predictive and generative model development
- ML model operations and governance
What clients say about working with us.
Need the data layer your AI or product roadmap depends on?
Book a 30-minute technical call. Bring your pipeline problem, your integration backlog, or the data quality issue nobody has fixed yet.
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