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Our Capabilities

How we help you win

From strategy to deployment, we deliver end-to-end AI and data solutions. Every engagement is grounded in operational reality—we build things that work, not just things that demo well.

AI & ML Strategy

Before writing a single line of code, we help you understand where AI can create real value—and where it can't. Our strategies are grounded in operational reality, not hype.

What We Deliver

  • AI Opportunity Assessment — Identify high-impact use cases based on your data and goals
  • Technology Roadmap — Phased implementation plan with milestones and resources
  • Vendor & Tool Evaluation — Unbiased build vs. buy assessment
  • ROI & Business Case — Quantified impact analysis for stakeholder buy-in
  • Data Readiness Audit — Assess infrastructure's AI readiness

Our Process

1

Discovery & Stakeholder Interviews

Talk to leadership, operators, and data teams to understand pain points.

2

Data & Infrastructure Assessment

Audit current data assets, systems, and technical capabilities.

3

Use Case Prioritization

Score opportunities by impact, feasibility, and strategic alignment.

4

Roadmap & Business Case Delivery

Clear plan with timelines, costs, risks, and expected returns.

Example Engagement

AI Readiness Assessment for Medical Device Startup

Helped a 15-person medical device company evaluate where AI could improve their quality inspection process. Delivered a prioritized roadmap with 3 quick-win opportunities.

3 use cases prioritized 2 weeks to delivery $18K engagement

ML Engineering

From prototype to production, we build machine learning systems that actually work in the real world. Designed for reliability, maintainability, and measurable impact.

What We Deliver

  • Custom Model Development — Purpose-built ML models for your use case
  • Feature Engineering — Transform raw data into predictive signals
  • Model Training & Optimization — Hyperparameter tuning and architecture search
  • Production Deployment — Containerized, scalable model serving with APIs
  • Model Explainability — Interpretable outputs for trust and compliance

Our Process

1

Problem Definition & Data Exploration

Clarify success metrics and validate ML is the right approach.

2

Rapid Prototyping

Build a working proof-of-concept to validate feasibility.

3

Iterative Development

Refine features and optimize through systematic experimentation.

4

Production Hardening & Deployment

Package with proper testing, monitoring, and documentation.

Technologies We Use

Python PyTorch TensorFlow scikit-learn XGBoost Hugging Face MLflow Docker Kubernetes AWS SageMaker
Example Engagement

Demand Forecasting Model for Regional Distributor

Built a demand forecasting model for a regional building materials distributor to optimize inventory levels across 3 warehouse locations.

23% reduction in overstock 6 weeks to production $22K engagement

Analytics & BI

Turn your data into decisions with dashboards, reports, and insights your team will actually use. We build analytics solutions that drive action, not just pretty charts.

What We Deliver

  • Executive Dashboards — Real-time visibility into metrics that matter
  • Operational Reporting — Automated reports without manual effort
  • Self-Service Analytics — Empower teams to answer their own questions
  • KPI Framework — Define metrics that drive business outcomes
  • Data Visualization — Clear visuals that communicate insights

Our Process

1

Requirements & KPI Workshop

Define what decisions need data support and what metrics matter.

2

Data Source Mapping

Identify where data lives and plan integration approach.

3

Dashboard Design & Development

Build iteratively with user feedback to ensure usefulness.

4

Training & Adoption Support

Ensure your team knows how to use the tools effectively.

Technologies We Use

Tableau Power BI Looker Metabase Apache Superset dbt SQL Python
Example Engagement

Sales Dashboard for Manufacturing Company

Created an executive dashboard for a 50-person manufacturing company, consolidating data from their CRM, ERP, and spreadsheets into one real-time view.

8 hrs/week saved on reporting 3 data sources integrated $15K engagement

Data Engineering

Build the foundation that makes everything else possible. We design and implement data infrastructure that's reliable, scalable, and ready for AI/ML workloads.

What We Deliver

  • Data Pipeline Development — Automated, reliable data movement
  • Data Warehouse Design — Modern architecture for analytics and ML
  • Data Lake Implementation — Scalable storage for all data types
  • Data Quality & Governance — Ensure accuracy and compliance
  • Real-time Streaming — Process data as it arrives

Our Process

1

Architecture Assessment

Evaluate current state and design target architecture.

2

Data Modeling

Design schemas optimized for your needs.

3

Pipeline Development

Build, test, and deploy with proper error handling.

4

Operationalization

Hand off with documentation and training.

Technologies We Use

Snowflake Databricks BigQuery Redshift Apache Spark Apache Kafka Airflow dbt
Example Engagement

Data Pipeline for E-commerce Analytics

Built automated data pipelines for an e-commerce company to sync Shopify, Google Analytics, and ad platform data into a central warehouse for unified reporting.

4 sources connected Daily automated syncs $12K engagement

MLOps & Infrastructure

Keep your models running, monitored, and improving. We build the infrastructure that turns ML experiments into reliable production systems.

What We Deliver

  • CI/CD for ML — Automated training, testing, and deployment
  • Model Monitoring — Track performance and detect drift
  • Feature Stores — Centralized feature management
  • Experiment Tracking — Systematic version management
  • Model Registry — Governance and lifecycle management

Our Process

1

MLOps Maturity Assessment

Evaluate current workflow and identify automation opportunities.

2

Platform Design

Architect tooling to support your ML lifecycle.

3

Implementation & Integration

Deploy and integrate with existing systems.

4

Team Enablement

Train your team and establish operational runbooks.

Technologies We Use

MLflow Kubeflow Weights & Biases Feast Seldon BentoML Evidently AI Terraform
Example Engagement

ML Deployment Setup for Fintech Startup

Set up a lightweight MLOps workflow for a fintech startup, enabling their data scientist to deploy and monitor models without DevOps bottlenecks.

Days → Hours deployment time 3 models deployed $20K engagement

Computer Vision

Extract insights from images and video. From document processing to real-time detection systems, we build vision AI that sees what matters.

What We Deliver

  • Object Detection & Tracking — Identify and track in images/video
  • Image Classification — Categorize images for automation
  • OCR & Document AI — Extract data from documents and forms
  • Video Analytics — Process feeds for security and QC
  • Medical Imaging — AI-assisted diagnostic analysis

Our Process

1

Use Case Definition & Data Collection

Define what to detect and gather training data.

2

Annotation & Dataset Preparation

Label data with precision and build robust datasets.

3

Model Development & Training

Train and fine-tune leveraging pre-trained models.

4

Edge/Cloud Deployment

Deploy optimized models for your infrastructure.

Technologies We Use

PyTorch TensorFlow OpenCV YOLO Detectron2 Hugging Face Vision AWS Rekognition NVIDIA Triton
Example Engagement

Invoice Processing for Accounting Firm

Built an OCR solution for a small accounting firm to automatically extract data from vendor invoices and receipts, reducing manual data entry by 70%.

70% less manual entry 95% extraction accuracy $16K engagement

Ready to get started?

Whether you know exactly what you need or want help figuring it out, we'd love to hear about your challenges. Every conversation starts with understanding your mission.

Schedule a Discovery Call