About us

Digital Transformation is fueling the next generation of business capabilities. You need a creative IT plan.

To be known as the company of possibilities that challenges earlier thinking, solves problems & transforms business through the power of technology, value-innovation & ‘disruptive’ thinking.

SERVICES

Idea of our business is also 4 letters D.A.T.A

Startegy & Advisory | Data Ecosystem | Modeling | Insights  & Outcomes | Adoption

Consulting & Advisory

Today, data is the new oil. Our team of experts takes a business-first approach to solving your IT challenges. Industry 4.0 is here. RPA, IoT, cutting-edge ML, and a slew of innovations have begun.

Be it a Green/Brownfield implementation of your ERP, or your cloud-led AI/ML predictive solution, data is the life blood.

CBC provides a knowledgeable project team of Data Scientists, Data Engineers, ML Specialists, and App Developers who can deliver high-end consultation for your Data Science & Machine Learning applications. Your business transformation programs can derisk by adopting the best-known methods in the industry.

Data Strategy

Database Management systems and processes have begun moving to the Cloud.

Be it your Cloud migration or homing in on your on-prem systems to build new capabilities, an in-depth understanding of the data models and integrations (including the various vendor technologies and platform-as-a-service environment), creating a roadmap including data lakes and lake houses are critical to getting it right the first time over.

CBC leaders come from deeply-entrenched experience in managing enterprise data for many decades. You can trust our hands-on knowledge every time. Be it building a flexible data model or designing your lake house.

AI at Scale, is easier said than done. With 4 out of 5 cognitive solutions struggling to adapt out of their old ways, business mindset is of paramount importance to engaging with an AI/ML partner who has a pedigree of managing complex change and explainable AI (at the very least). Domain-led, feature engineering is the most critical founding block of your AI/ML cognitive initiatives. Lynchpin so to say.

Business context, Data ingestion, Data prep, Data exploration, Feature engineering, Model creation & its training, Tuning, Deployment, Monitoring and curation, and finally Maintaining AI systems – are the critical success factors for building intelligent applications including UI/UX using tools and technology.

CBC leads with a domain and industry focus instead of IT.

Insights & Impacts

Advanced analytics capabilities, such as agent-based modeling, discrete-event modeling, Monte Carlo simulation or cognitive design support for generative adversarial networks and self-supervised learning – all have created opportunities for ways to monetize your data assets as never before.

From pure Cloud environment solutions to hybrid and on-prem solutions, CBC specializes in the Software Development Life Cycle (SDLC). Conducting discovery to future-proof your investments while delivering a surefire, augmented ROI from business insights – are the two-pronged value realization methodologies that we adopt.

Change Adoption

Cloud-first or On-premise, Code-first, Domain-specific, Low-code, Metadata & Flexible Data Models, Migration of on-prem to Cloud, using open-source libraries along with libraries provided by hyper-scalers such as AWS, Azure, and Google (GCP) – all present us with opportunities for digital transformation at the same time creating the need for a well-thought-off strategy that considers immediate and future challenges with protecting investments that can sustain, augment ROI at the same time.

CBC provides business users, data citizens, data scientists and engineers, testing and integration teams with the required know-how to ensure change is adaptive, progressive. We offer high-touch customer service and experience to our client projects, so your IT journey with us as your partner is remembered for the right reasons.

A Technology Company needs to understand Business foremost.

You’ll notice that at each step of the way, during your interactions with us, there will be 3 SMEs (subject-matter experts) present. An Industry Principal, a Domain Consultant, and a Technology Practitioner. We provide solutions to 17 industry verticals, across 25 domains and a host of Traditional and Startup technologies.
Industry
Domain
Technology
Our business transformation programs are led by 17 SMEs (subject-matter experts) today. We continue to develop industry solutions that deliver early ROI for your investments including time-to-market value. Typically, our POC’s (Proof of concepts) are completed in days and production MVPs (minimum viable product) in few weeks.

Our domain experts from a COE(center of excellence) are aligned to each vertical industry we work in. Our capabilities include Advisory & Process Consulting in over 25 domains as on date. Our practices co-develop innovative solutions for industry by partnering with our Customers and Startup Technology partners (3-in-a-box).

Our Specialists from practices under each Technology. For example, we have an SAP, Oracle, IMB, Salesforce, practice alongside Microsoft, Opensource, Java, Python, Data Science, Hyper-Scalers (AWS, Azure, Google).

Once upon a time, not long ago...

Our Top-Shelf Stories.
  • Retailers

    RETAIL

    Retail Situation

    Retailers have gotten themselves into a corner. This time with their own inventory policy and unidirectional supply chain design that’s now grown old to keep up to challenges including agility.

    Retailers need global visibility of their inventory in real-time. Not just that. They need to predict and develop cost-effective strategies to rebalance inventories across their stores, DCs, .Com fulfillment channels including e-marketplaces.

  • Modern-SOC-IR-Teams

    HEALTHCARE

    Healthcare Situation

    A data breach left a large health system that comprised 30 hospitals and 50,000 patients leaving their Personal Identifiable Information (PII) in the wrong hands.

    Modern SOC (Security Operations Center), IR (Incident Response) Teams, Digital Forensic Case Management – all are critical to the well-being, functioning, and securing the confidence of the public they serve.

  • Building-a-foundation-for-an-AI-led-Engineering

    MANUFACTURING

    Manufacturing Situation

    Adaptive Manufacturing is an essential part of Smart Factory. Today, the need to curate data from your field assets is critical for performance monitoring and meeting service levels (SLAs) that you’ve committed to your Customers.

    Building a foundation for AI-led Engineering is key to realizing the benefits of digitization. Structured, Unstructured and, Streaming data need to enable a “machine-first” approach for Operations.

  • While-a-plethora-of-technology-offerings

    MANUFACTURING

    Manufacturing Situation

    Industry 4.0 is creating the pressing need for manufacturers and their suppliers to stay ahead of their game. Customers need personal, relevant, and highly-customized services across their value chain.

    While a plethora of technology offerings have swamped the marketplace, the need for a business metrics-driven approach to solving complex problems including collaboration has been evasive.

  • Can-Retailers-accomplish-all-the-value-benefits

    RETAIL

    Retail Situation

    Discount and Price Optimization are critical for Retailers and Consumers alike. Buying decisions for both depend upon their ability to foresee “cost-to-deliver”. Further, markdowns, promotions, product-mix, omnichannel fulfillment, and anticipatory commerce – all need attention to detail.

    Can Retailers accomplish all the value benefits and fast-impact making programs fast enough?

  • Cybersecurity-Geo-political-sensitivity-privacy-and-confidentiality

    ENERGY

    Energy Situation

    Need to develop a strategy and business transformation across a global energy services company leading to unearthing realities. Why does the “herd” mentality not work anymore?

    Cybersecurity, geo-political sensitivity, privacy, and confidentiality on the lifecycle of the hydrocarbon products – all create a need to deliver new ideas while preserving the old ways.

  • How-can-the-most-profitable-manufacturers-realize

    AFTERMARKET SERVICES

    Aftermarket Services Situation

    Manufacturers and their OEMs are struggling to the truth – their end consumer markets are being disrupted by digital entrants in all directions.

    How can the most profitable (manufacturers realize double-digit operating margins in spare parts) business line escape the peril of losing customers to new entrants?

  • How-did-the-retailer-know-where-to-start

    RETAIL

    Retail Situation

    With the onset of the pandemic in March 2020, this lifestyle retailer was under pressure to conceptualize and implement a cost-effective strategy for their last-mile delivery.

    How did the retailer know where to start keeping their last mile in mind?

  • How-did-the-CPG-define-their-go-to-market-plans

    CPG

    CPG Situation

    Growing new revenues through the direct-to-customer (D2C) channel brought new challenges for both business and IT.

    How did the CPG define its go-to-market (GTM) plans? Channel conflicts, mid/last-mile delivery systems needed a complete rehaul and reimagination.

  • How-can-you-make-most-out-of-your-supply-chain-network-strategies

    LOGISTICS

    Logistics Situation

    Shippers are reeling out of the Covid aftermath and seeing extreme demand for their products (including on-the-shelves). How is the logistics capacity and availability impacting a shipper’s bottom-line?

    How can you make the most out of your supply chain network strategies? How can you sustain transportation costs for your shippers without leaving their loads on the load-boards?

  • How-did-our-Industrial-Manufacturer-solve-the-unsolvable-problem

    MANUFACTURING

    Manufacturing Situation

    Demand sensing through traditional means is no longer effective in predicting demand for products and services.

    How did our Industrial Manufacturer solve the unsolvable problem of forecasting? What challenges will manufacturers face while adapting to modern cognitive methods for a forecast?