Expert data engineering builds the data pipelines that collect, clean, and unify your data. Precise annotations turn it into labeled, model-ready AI training data.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
Your CRM, ERP, product logs, and spreadsheets each tell a different version of the truth.
Unmonitored data pipelines fail silently, and errors surface weeks later as wrong dashboards or drifting models.
Inconsistent annotation teaches your model the wrong lessons, and retraining costs pile up.
One vendor moves the data, another labels it, and nobody owns quality end to end.
Better AI models or algorithms rarely rescue a project built on scattered, inconsistent, or loosely labeled data. If any of these sound familiar, the fix should start upstream.
Building the Foundation
We design and run the data infrastructure that moves, cleans, and organizes your data. Your analytics, BI, and AI teams work from one reliable source.
Teaching AI the model
Human-in-the-loop data labeling services that turn images, video, text, audio, and 3D data into accurate, consistent AI training data.
Share a sample and your goal. We can point out the gaps in sourcing, quality, and labeling, and tell
you what it takes to close them. No cost, no commitment.
When you choose our data services for AI, one dedicated team engineers and labels your data; nothing gets lost between handoffs. We build all annotation guidelines on clean, well-modeled inputs, and the labeled output flows straight back into your stack.
We audit sources, volumes, quality, and the outcome your model or report needs.
Pipelines ingest, clean, deduplicate, and model data into a governed store.
Multi-layer QA checks both data integrity and label accuracy, with per-batch reports.
Trained annotators label to your taxonomy, starting with a free pilot batch.
Data lands in your format and platform, with pipelines monitored as new data arrives.
Most teams split data and AI work across in-house engineers, a labeling vendor, and a self-serve tool. Here is how that compares with a single data services provider.
| Feature | TechnoScore’s Data Services for AI | Labeling-only vendor | Self-serve tools |
|---|---|---|---|
| Pipelines + labeling together | One team, one SLA | Labeling only | You do the work |
| Time to start | Pilot in a few weeks | Fast, after data is prepped | Fast setup, slow output |
| Ownership of quality | End-to-end, measured per batch | Labels only, not inputs | Entirely yours |
| Ownership of Code, Data & Models | You own all IP, pipelines, and model artifacts | Vendor lock-in; data lives on their platform | Vendor lock-in; data lives on their platform |
| Scaling up or down | Flexible team size | Flexible for labeling | Limited by your team |
| Works with your stack | Your cloud, tools and formats | Often their platform | Their platform |
Schema, freshness, and volume checks catch broken data before it reaches anyone downstream.
Annotator, reviewer, and QA layers, with gold-standard items seeded in every batch.
IoU, inter-annotator agreement, and data quality scores reported against your SLA.
Automation speeds up profiling and pre-labeling; experts make the final call.
Your Data Stays Yours
Unified product and customer data pipelines, plus attribute tagging for search and recommendations.
Clinical data integration and medical image labeling under strict privacy controls.
Sensor data pipelines and camera and LiDAR annotation for ADAS models.
Data modernization for legacy core systems, plus document AI training data for forms and invoices.
IoT and ERP integration, with defect and object labeling for vision systems.
Product event pipelines and intent and sentiment data for chatbots and LLMs.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
Work with one trusted data services company for all your AI and data needs—one
point of contact and one accountable quality standard.
TechnoScore’s data services for AI cover two main areas: data engineering (data pipelines, integration, data modernization) and data annotation (image, video, text, audio, 3D, and LLM data).
Yes. Our AI data services prepare and label training data for vision, NLP, speech, and LLM models. When real examples are scarce, we can also build a synthetic dataset to cover the gaps.
AI systems are only as good as the data beneath them. Working with one data services company removes handoffs, and a single SLA covers quality from raw source to final training set.
Look for proven work in both data engineering and annotation, measurable quality metrics, and certified security. A reliable data services provider will also offer a pilot, so you can judge quality first. TechnoScore offers a no-cost pilot on your data to show you what we can deliver. Contact us at info@technoscore.com.
Yes. We build and extend AI data platforms on AWS, Azure, and Google Cloud, using Snowflake, Databricks, BigQuery, Airflow, and dbt, so nothing you rely on today gets replaced.
Checks run at every layer. We test all structured and unstructured data pipelines for schema and completeness, and every label passes annotator, reviewer, and QA review, so your AI training data meets the accuracy agreed in your SLA.
We build data security and governance into every engagement: NDAs, role-based access, encrypted transfers, and audit trails. We can also mask PII or work entirely inside your own cloud.
We price data engineering per project or as ongoing managed data services, and annotation per unit or per hour. For the latter, vetted resources start at 5-8 USD/hour. Every engagement begins with a free readiness review; we share a clear, itemized quote. Contact us at info@technoscore.com.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
Share your project requirements.
One team, one SLA
