Government Agency Saved 30% in Recurring Costs

Our client is a government security organization in India tasked with maintaining public safety, identifying suspects, and tracking criminal records across the country.

Their system includes:

• A national database of face images and personal records

• Manual processes to verify identities of suspects and citizens

• Security protocols that rely on biometric authentication, including facial recognition, thumbprints, and digital footprints

But the existing process wasn’t keeping up.

Manual verification was slow, expensive, and error-prone, and the organization lacked the in-house technical talent to build a secure AI-powered alternative.

The mission was clear:

Ensure faster, more reliable identity verification using AI-powered facial recognition — while reducing operational overhead.

But they faced serious limitations:

The agency lacked the specialized engineers to build and deploy a secure, real-time system. Internal teams weren’t equipped to automate the process or manage the model lifecycle at scale.

Despite strong infrastructure and access to data, the organization faced four key challenges:

Verifications were done by hand, creating delays in identifying suspects and issuing clearances.

Lighting, occlusions, or poor-quality images often led to misidentification during manual reviews.

There was no internal capacity to build, train, or deploy machine learning models.

As a government body, hiring was bureaucratic, slow, and resource-intensive — making it difficult to attract niche AI talent.

Muoro provided a remote team of AI and DevOps engineers with expertise in facial recognition, cloud deployment, and real-time data processing.

We collaborated with the agency to map out:

Their current manual verification workflow

Existing datasets (face images, personal records, past verifications)

Requirements for real-time inference and monthly reporting

Within days, Muoro assembled a specialized team including:

Python Engineers to script and manage training pipelines

Computer Vision Experts to build deep learning models using TensorFlow

Cloud Architects for secure deployment and data handling on AWS

DevOps Engineers to automate training and reporting workflows

We provided a managed offshore team with:

Transparent communication and sprint reporting

Full IP and data protection under government compliance

Optional ramp-up for additional modules (like thumbprint and gait recognition)

The solution required a custom stack optimized for AI model accuracy, cloud scalability, and real-time performance.

1. AWS SageMaker – For training and managing deep learning models

2. TensorFlow – For building face recognition models and continuous tuning

3. Python – Core scripting language for data ingestion, model training, and inference

1. AWS EC2 – Hosted the live inference API and model deployment environment

2. AWS S3 – Stored image datasets, logs, and model versions securely

We never fail to deliver as promised to our clients.

The system now verifies identity in seconds — no human involvement needed.

The model is trained to handle poor lighting and occlusions, improving accuracy across edge cases.

Admin dashboards now display verification stats, outliers, and trends without manual updates.

Compared to local hiring and manual processes, the client saved both time and ~30% in recurring costs.

All data is processed and stored securely on AWS under strict compliance protocols.

The client gained:

A real-time facial recognition system

• End-to-end automation of identity verification and reporting

• A fully managed AI team with cloud and security expertise

• Flexibility to expand the system into new biometric modalities

If you're working on secure, mission-critical applications and need fast access to engineers in AI, data, or DevOps, Muoro can help.

We provide fully managed offshore teams trained to deliver in high-stakes environments with the speed, scale, and skill your project demands.

Let’s talk.