News & Press

Groq® and Carahsoft Partner to Provide Rapid AI Inference Speed to the Public Sector

MOUNTAIN VIEW, Calif., and RESTON, Va. – May 7, 2024 – Groq®, the leader in real-time AI inference, and Carahsoft Technology Corp., The Trusted Government IT Solutions Provider®, today announced a partnership to deliver fast and cost- and energy-efficient AI inference speed to Government agencies and Federal systems integrators throughout the United States. Under the distribution agreement, Carahsoft will serve as Public Sector distributor for Groq, making its innovative AI inference solutions available to the Public Sector through Carahsoft’s reseller partners and NASA Solutions for Enterprise-Wide Procurement (SEWP) V contracts.

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Cohere Partners with MongoDB on New Program to Build Advanced Enterprise AI Applications

Today, Cohere, the leading enterprise AI platform, will join MongoDB’s new AI Applications Program (MAAP) as part of its first cohort of partners. The MAAP program is designed to help organizations rapidly build and deploy modern generative AI applications at enterprise scale. Enterprises will be able to utilize MAAP to more easily and quickly leverage Cohere’s industry-leading AI technology, such as its highly performant and scalable Command R series of generative models, into their businesses.

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Cohere’s Command R Model Family is Now Available In Amazon Bedrock

Today, we’re excited to announce that our latest Command R model family is now available in Amazon Bedrock. We look forward to continuing our partnership with AWS to provide enterprise customers best-in-class AI solutions for their business needs to unlock powerful productivity and efficiency gains.

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Supercharging our in house CRM with AI Automation

Supercharging our in house CRM with AI Automation

At Blotout, which runs multiple divisions and embedded partnerships, our internal CRM system on how we reach to our B2B customers serves as a central repository for all customer interactions for partner introductions, and cross selling. As our business scaled, we identified critical inefficiencies in our data management processes that necessitated an automated solution. This case study outlines our methodology, implementation, and measurable outcomes from deploying an automated data validation, cleanup, and enrichment system across our CRM segments.

Business Context and Challenges

Our CRM architecture comprises three distinct data segments:

1. 1P Data (Blotout Native Customers)

  • Core customer base
  • Primary focus for retention and expansion

2. 2P Data (Embedded Partner Customers)

  • Acquired through strategic partnerships
  • Significant cross-sell potential

3. 3P Data (Prospects)

  • Targeted accounts for customer acquisition
  • Requires continuous validation and enrichment

Identified Pain Points

  • Data Completeness: 62% of 1P/2P records lacked firmographic data (revenue, headcount, funding)
  • Lead Quality: 28% of 3P contacts contained invalid or outdated information
  • Operational Efficiency: Sales teams spent 15-20 hours weekly on manual data cleanup

Need to Hyper Segmented Outreach

As we scaled our different product lines, we needed to hyper personalize companies that were a fit with our direct customer base, or partner base, or outbound but with a different core ICP within the customer organization. We felt going inhouse would save us a ton of agency cost and learn how we can scale partners in our ecosystem and our software in partner ecosystems while we grow the overall pool. We are also sharing our CRM automation approaches with partners, unlike tools like CrossBeam or AISDR that are expensive and only solve parts of the problem.

Solution Architecture

1P/2P Data Enrichment Framework

Data Sources:

CRM Automation

Technical Implementation:

  • n8n workflows for API integrations
  • Custom validation rules for data consistency
  • Automated CRM field updates

3P Data Validation System
Process Flow:

  1. Initial contact import
  2. Multi-point validation (email format, phone syntax, domain verification)
  3. Automated enrichment (job titles, seniority levels)
  4. Deduplication and merge processing
  5. Final scoring and routing

Business Impact and ROI

Quantitative Outcomes

1P/2P Segment:

  • Increase in identified expansion opportunities
  • Improvement in cross-sell conversion rates
  • Reduction in account research time

3P Segment:

  • Decrease in bounced emails
  • Improvement in lead-to-meeting conversion
  • Increase in sales productivity

Operational Efficiency:

  • Reduction in manual data entry
  • 60 hours/month saved in data cleanup

Strategic Advantages

  1. Customization: Proprietary algorithms for our specific business model
  2. Real-time Processing: Near-instantaneous data updates
  3. Scalability: Handles 300% volume growth without additional resources
  4. Cost Efficiency: 40% lower TCO compared to commercial solutions

Future Roadmap

1. Predictive Analytics Layer

  • Lead scoring models
  • Churn risk indicators

2. Enhanced Enrichment

  • Technographic data integration
  • Intent signal monitoring

3. Process Expansion

  • Automated campaign triggers
  • AI-assisted contact recommendations

Conclusion

Our CRM automation initiative has transformed raw data into a strategic asset, driving measurable improvements across sales productivity, marketing efficiency, and revenue growth. Our internal solution’s flexibility ensures continued value as our business evolves.

Bonus Upside

Our learnings eventually drove us to understand the complexities of CRM, that eventually enabled us to understand the basis for simplifying internal CRM systems in the AI era where AI can hyper personalize using tools like n8n but the core of the CRM needs to be hyper simplified and connected to traffic drivers. These learnings informed our separate product smblead.ai, which we’re moving from Alpha to Beta and the codebase between our own sales CRM and SMBLead.ai is shared.

In our next post, we’ll explore “The AI-Powered CRM: How We’re Scaling 1:1 Personalization for Every Customer”


Source: Supercharging our in house CRM with AI Automation

LMNT Electrolyte Review

LMNT electrolyte drink mixes are high in sodium and are marketed toward athletes and active individuals to restore health through hydration. After two weeks of daily use before and after high-intensity workouts, LMNT did increase my desire to hydrate and helped me recover faster than similar electrolyte mixes, but didn’t significantly change my physical performance.

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Dapi and Mastercard Partner to Provide A2A Payments in UAE

Dapi and Mastercard have partnered to provide account-to-account (A2A) payments in the United Arab Emirates (UAE).

With this strategic partnership, Dapi, a UAE-based FinTech company, will launch A2A payments on Mastercard Payment Gateway Services (MPGS), the companies said in a Thursday (June 1) press release.

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How Much Money Does SpaceX Make?

SpaceX is growing faster than just about anybody else. And unlike almost everybody else, it’s making money in space.

SpaceX, the pioneering space company founded by Elon Musk with money made from his PayPal payout more than 20 years ago, has grown to become arguably the most successful space company (or company, period) in the history of mankind. In 2023, SpaceX launched rockets to orbit nearly 100 times, exceeding the performance of every rival on Earth — up to and including the entire nation of China.

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AI Stock Soars On Earnings, But Is C3.ai Stock A Buy?

Artificial intelligence may well be the next big technological revolution after the internet. But investors looking to participate in this growth story after the AI rally have their work cut out trying to identify whether C3.ai (AI) stock is a potential leader. That begs the question, is AI stock a buy now?

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Is Affirm Stock Is A Buy Now? Fintech Leader Struggles To Surpass New Buy Point After A Big Swing On Earnings

When it comes to investing in fintech companies and the financing concept of BNPL — buy now, pay later — Affirm (AFRM) still comes to top of mind.

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Amazon Terminates iRobot Deal, Roomba Maker to Lay off 31% of Staff

KEY POINTS
  • Amazon and iRobot mutually agreed to call off their planned acquisition, writing that there was “no path” to regulatory approval and sending iRobot shares down sharply.
  • iRobot, which makes the Roomba, said it will lay off around 350 employees and that its founder and CEO Colin Angle would step down.
  • The Amazon-iRobot deal was originally valued at $1.7 billion, but a number of regulatory examinations drove the purchase price down before ultimately killing the deal.

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