Real-Time Data Pipelines
Streaming ingestion and live human-in-the-loop labeling for always-on AI systems.
When your model serves live traffic, your data pipeline has to move at the same tempo. We design and operate streaming pipelines that ingest, validate, and label new events within minutes — so production AI keeps up with the world, not last quarter's training set.
Where real-time pipelines win
- Distribution drift surfaces in production logs, not in last quarter's training set
- Edge cases need labels in hours, not weeks
- Model errors must close the loop into the next training run automatically
- Manual batch handoffs add latency you can't afford
Our real-time offering
Streaming ingestion
Connect to your event bus, Kafka, Kinesis, Pub/Sub, or webhook source. We dedupe, validate, and route new data into the labeling queue in minutes.
Live human-in-the-loop labeling
Always-on reviewer pods triage, label, and adjudicate fresh events on a rolling cadence — hourly or per-event SLAs.
Active learning & error mining
We surface low-confidence predictions and production failures, prioritize them for review, and feed them back into your next training run.
Drift & quality monitoring
Live dashboards for label agreement, distribution shift, and per-segment model quality — with alerts when something moves.
How we deliver
- 01
Scoping & guideline co-design
We meet with your ML and product leads to map model objectives, target metrics, and the failure modes the next training run must address. Together we draft an annotation rubric and a calibration set.
- 02
Pilot & calibration
A small batch goes through our reviewers and yours in parallel. We measure agreement, surface ambiguous cases, and lock the guidelines before we scale.
- 03
Production labeling
Domain-expert annotators with model-assisted tooling work through the queue. Per-batch quality dashboards stream to your team.
- 04
Multi-pass QA & adjudication
Independent reviewers re-label a statistical sample and adjudicate disagreements. Golden-set F1 and per-class accuracy are reported every batch.
- 05
Delivery, evaluation & iteration
Data ships in your preferred schema. We run evaluation against your held-out set, capture model-lift signals, and roll learnings into the next sprint of guidelines.
What you get
Production-ready labeled dataset
Delivered in the schema and storage of your choice (S3, GCS, Azure, on-prem) with versioned manifests.
Annotation guidelines & calibration set
A living document plus a held-out calibration set you can re-use to onboard future vendors or in-house teams.
Per-batch quality reports
Inter-annotator agreement, golden-set F1, per-class accuracy, throughput, and reviewer-level performance.
Audit trail
Per-label reviewer, timestamp, and version history — ready for regulator and customer audits.
Handover & training
Documentation, tooling access, and a working session so your team can extend the pipeline internally.
Built for production AI, not pilots
GDPR & CCPA compliant
Lawful basis, data-subject rights workflows and documented retention policies on every engagement.
Senior delivery ownership
A named senior program lead owns every engagement End-to-End — no ticket queues, no vendor relay.
Human-in-the-loop QA
Multi-pass review, gold-set calibration and consensus scoring — quality reviewed by people, not just scripts.
NDA & secure handling
NDAs by default, role-based access, EU/US data-residency options and full chain-of-custody on project assets.
Why teams choose DOT Data Labs
Domain-expert workforce
Vetted reviewers with the credentials your task requires — clinicians, attorneys, CFAs, native linguists, or sensor-fusion specialists. Not a general-purpose crowd.
Measured quality, not promised quality
Every batch ships with agreement scores, golden-set F1, and per-class accuracy. If quality regresses, you see it before the data lands.
Security & compliance by default
SOC 2-aligned operations, signed NDAs per project, and customer-controlled deployments (VPC, on-prem, air-gapped) on request.
Senior program management
You get a named program lead who owns delivery End-to-End — not a ticket queue. Your ML team stops managing the vendor.
Built to integrate, not to lock you in
Guidelines, tools, and data are yours. We plug into your annotation tool or bring our own — whichever maximizes throughput and quality.
Real model-lift focus
Success is measured in downstream model metrics, not labels delivered. We track lift per data sprint and adjust strategy when the curve flattens.
Ready to scope your dataset?
Tell us about your model and target metrics — we'll come back with a data plan and timeline.
Frequently asked questions
For streaming use cases we ingest, validate, and queue new items within minutes; reviewed labels typically land in your training store within hours, depending on the SLA you choose.
We meet your stack where it lives. Common integrations include S3 / GCS / Azure Blob, Kafka, Kinesis, Pub/Sub, REST or GraphQL webhooks, Snowflake / BigQuery / Databricks, and direct calls to your model-serving layer for confidence-based sampling.
Yes. We can consume your model's predictions and confidence scores, mine errors and low-confidence regions, prioritize them for human review, and emit a labeled retraining set on your cadence.
Pipelines run on infrastructure aligned with SOC 2 controls, with options for VPC-isolated processing, customer-managed keys, and regional data residency (EU, US, APAC).
Most engagements progress from kickoff to the first labeled batch within one to two weeks, although exact timing depends on how specialized the workforce must be.
Our pricing is meticulously structured on a per-project basis, offering full transparency through a detailed breakdown of costs.
We support deployment configurations within your controlled Virtual Private Cloud (VPC), on-premise environments, or air-gapped systems when data sensitivity mandates strict isolation.
Upon project completion, full ownership of the data and all associated intellectual property rights transfer to your organization.
Production datasets often require ongoing maintenance to ensure model efficacy. Our approach involves establishing continuous data programs that incorporate scheduled refresh cycles.