Data pipelines and integrations that hold under real conditions.
AI products need reliable data. We build the pipelines, vector stores, and integrations your system depends on: designed to survive schema changes and upstream failures.
Bad data is why most AI projects fail. Not bad models.
The pattern repeats across industries: a team builds an AI feature that performs well in evaluation, hands it to the data team to "just connect it to production," and watches adoption stall. The model was fine. The data feeding it was not — inconsistent schemas, stale batch cycles, missing integration coverage, no quality controls.
Data engineering is the part of AI product development that is systematically underscoped and understaffed. Teams that get it right treat the data infrastructure as a first-class engineering problem — not a prerequisite someone else will handle.
Six patterns we see across every industry.
These are not hypothetical. They come from engagements with AI product teams, regulated platforms, and data-heavy SaaS companies across fintech, healthcare, and industrial.
AI models trained on data that doesn't exist in production
Pilots use curated snapshots. Production data is schema-drifted, arrives late, and inconsistently formatted. Models degrade within weeks of deployment — and the data team is blamed for the model team's assumptions.
Batch pipelines making real-time decisions
Fraud scored on hour-old transactions. Risk decisions running on yesterday's data. Recommendation engines serving stale signals. The AI feature ships; the pipeline it depends on was never designed for its latency requirements.
Five upstream sources. Five schemas. No single view.
Every new data source adds integration debt. Transformations break when an upstream provider changes their API. There is no canonical model the whole team trusts — just a growing list of one-off fixes.
Pipelines that fail silently
Schema changes from third parties pass validation but corrupt downstream aggregates. Late-arriving events look like missing data. You find out something is wrong when someone notices the numbers are off — not from a monitor.
Data quality issues that reach the model
Features computed from data that is technically present but operationally wrong. Training pipelines mixing production records with data that was never meant to leave staging. Problems invisible until the model behaves strangely.
No observability — failures surface from users, not monitors
SLAs defined on pipeline run success, not data freshness. No alerting on distribution drift, schema anomalies, or row-count drops. The first signal is a Slack message from someone whose dashboard looks wrong.
Four categories of data work.
Most engagements involve more than one. The scoping call is the fastest way to identify what the real problem is and what category of work will fix it.
Data pipeline engineering
Ingestion, transformation, and delivery pipelines built to handle schema drift, late-arriving data, and upstream failures without manual intervention. Designed with observability and data quality checks as first-class requirements.
AI data infrastructure
Feature stores, vector databases, embedding pipelines, and training data infrastructure. The data foundations that make LLM and ML applications behave predictably in production — not just in notebooks.
Third-party integrations
Bi-directional syncs, webhook architectures, and API integrations with CRMs, ERPs, payment processors, and healthcare data systems. Built to survive upstream API changes and designed for auditability.
Analytics engineering
Data warehouse modelling, dbt transformation layers, and BI-layer design. Analytics your business teams can trust and your engineers can maintain — with lineage, testing, and documentation built in.
Four stages. No big-bang migrations.
Data audit
We map your current sources, pipeline architecture, and reliability problems. You leave with a clear picture of what is causing the issues — and which to fix first.
Architecture design
We define the target architecture: streaming vs batch tradeoffs, transformation layers, data quality controls, and integration boundaries. Scoped to what you actually need.
Build incrementally
We build and migrate in layers — no big-bang migrations. Each layer is observable and stable before the next is added. Your product keeps running throughout.
Handoff with documentation
Runbooks, data dictionaries, lineage diagrams, and alerting configuration your team can maintain without us. You own it from day one.
ActiDash's clients were making business decisions on data that was 4–24 hours old. Sales, marketing, and consumer behavior data arrived on batch schedules — by the time it surfaced in dashboards, the moment to act had passed. We rebuilt the ingestion and processing layer on a fault-tolerant Kafka streaming architecture with 100+ servers. Analytics dashboards now refresh on sub-minute cycles. Delivered ahead of schedule, no scope reduction, full integration with existing on-premises infrastructure.
More data engineering cases.
Real-Time E-Commerce Intelligence & Data Streaming Platform
Billions of events processed via Kafka-based streaming — near real-time dashboards replacing 4-hour batch cycles for ActiDash clients.
Read case study EnergyAdvanced Analytics Platform for Energy Data Processing
2× faster historical data processing, 60% faster long-term reports — legacy analytics system replaced without downtime or data loss.
Read case studyThings people ask before booking.
Is Data & Integrations a standalone engagement or part of a Build?
Both. It works as a standalone engagement when the specific problem is pipelines, integrations, or AI data readiness — scoped and delivered independently. It also frequently runs as a concurrent workstream within a Build engagement when the product build has data dependencies that need senior attention in parallel.
What if we only need one integration, not a full pipeline redesign?
Single-integration scopes are a good fit. We assess what already exists, build the integration to a standard that will not create future maintenance debt, and hand it off. The scoping call is the fastest way to understand what the real scope is — sometimes what looks like one integration has architectural implications that are worth understanding before building.
What data tools and frameworks do you work with?
The stack depends on your requirements. We work with Kafka and Flink for streaming, Apache Airflow for orchestration, dbt for transformations, and Snowflake / BigQuery / Redshift for warehousing. For AI infrastructure: Pinecone, Weaviate, LanceDB for vector storage, MLflow for experiment tracking, Feast for feature stores. We are tool-agnostic — we recommend based on your latency requirements, budget, and team, not on what we prefer to work with.
How do you handle GDPR, HIPAA, or other compliance requirements in data pipelines?
Compliance requirements are scoped in from the start — not retrofitted after. PHI boundary enforcement, data retention controls, audit logs for data movement, and de-identification pipelines are treated as architectural requirements, not afterthoughts. We have built HIPAA-aware data infrastructure for healthcare clients and PCI-DSS-compliant pipelines for fintech. Final compliance sign-off is with your legal team; we build the architecture that makes it possible.
Can you work with our existing Snowflake / BigQuery / Redshift setup?
Yes. We routinely work within existing warehouse infrastructure. The engagement typically involves assessing what already exists, identifying where the reliability or quality problems are, and building or rebuilding the parts that are causing issues — without requiring you to replace infrastructure that is working.
What is the minimum engagement size?
Data engagements typically start from $25K for a well-scoped single-workstream problem — an integration, a pipeline rebuild, or an AI data readiness audit with implementation. Larger platform-level data work runs $75K–$200K. The scoping call gives us enough to outline an honest range before you commit to anything.
How do you migrate from batch to streaming without downtime?
Incrementally. We run the new streaming pipeline alongside the existing batch system, validate output parity, then shift consumers one by one. No big-bang cutover. The existing system stays live until the new one has demonstrated reliability at production load. We have done this on platforms with millions of daily events where any interruption had commercial consequences.
How quickly can a data engagement start?
Typically within two to three weeks of signing. We begin with a half-day technical discovery session, deliver an architecture and scope proposal within five business days, and start build work once the proposal is agreed. Urgent timelines are sometimes possible — mention it on the call.
Ready to fix the data layer?
Book a 30-minute technical call. Bring your pipeline problem, your integration backlog, or your AI feature that stalled on data quality. We will tell you what we think in the first 20 minutes.
Book a 30-min technical callA senior engineer replies within one business day, often faster.