The carmaker had customers, subscription products and two outside agencies to make the calls. It also had an awkward result: expensive outreach, weak response and customers who resisted being contacted. In Info Services’ account of the project, the trouble began before anyone picked up the telephone. The sales pitch was arriving ahead of the evidence that someone wanted to hear it.
Info Services changed the sequence. Customer records were brought together in Salesforce Data Cloud. Marketing Cloud supported email, push notifications and text messages. Engagement helped identify who should receive an outbound call. The company reports a 10% increase in subscription trial conversion rates in the fourth quarter of 2024. The useful detail is the order of operations: learn something about the customer, then ask for their attention.
- Info Services connects enterprise software, data and the people operating them.
- Its customers include large automotive, healthcare and media organizations.
- It sells engineering capacity, scoped delivery and ongoing operations.
- Its AI products tackle hiring, customer service and platform reliability.
The call came too early
There is a small business lesson hiding in that carmaker’s problem. When a process disappoints, the tempting response is to increase its volume. More calls, more tickets, more people. Here, the existing approach already involved two agencies. Adding effort would leave the sequence intact. The intervention connected customer information to the decision about when to make contact.
The manufacturer remains unnamed in this case. Its reported conversion improvement is a company-published result, not a promise to the next buyer. Still, the mechanism is clear enough to copy: consolidate records, segment customers, connect outreach channels and make follow-up depend on observable engagement. A business with unreliable customer identities or no useful engagement signals would need to repair those foundations first.
A release queue with 120 people in it
Another customer’s bottleneck sat inside the company. A healthcare organization had more than 120 developers working on a Salesforce environment supporting over 10,000 users. Quote-to-Cash and configure-price-quote requirements made the application complicated. Extensive customization, sandbox management and manual code promotions made delivering changes complicated too.
Info Services describes a workflow dependent on support tickets, with merge conflicts and technical debt obstructing releases. Its response included continuous integration and delivery automation, Copado and AutoRABIT proofs of concept, automated regression testing and self-service code promotions. Salesforce Industries, formerly Vlocity, was adopted to reduce customization.
The healthcare DevOps case also reports merge conflicts below 5%. These are project-specific outcomes.
The lesson is less glamorous than the tools: writing code and moving code safely into production are different jobs. A team can become faster at the first while waiting indefinitely on the second. Give developers a tested route across environments, and the release queue becomes a tractable engineering problem. Automation earns its keep at the point where work used to stop.
Keep the old system alive long enough to leave it
For a media and entertainment multinational, the expensive friction was in data processing. Info Services describes pipelines spread across AWS EMR and Snowflake, with costly storage, difficult queries and limited control over computing resources. The proposed destination was a unified Databricks platform. The delicate part was ensuring that applications could continue trusting their data during the move.
The company redirected the Bronze and Silver processing layers to external Delta tables on Amazon S3. For the Gold layer, which served downstream applications, it wrote data to both environments during the transition. Users could validate the new outputs before switching their applications. Snowflake was decommissioned after migration. Info Services reports costs falling by up to 40%, alongside simpler pipelines.
The old system stayed useful while the new one earned trust.What the dual-write migration method makes possible
That approach offers a practical pattern: move in stages, maintain continuity and require validation before retirement. Running two environments carries its own cost and reconciliation work. It is most useful when the damage from an abrupt change would outweigh that temporary burden. The case supports a particular workload decision; it does not establish that one data platform is always cheaper than another.
The bill has two clocks
Cloud projects also demonstrate how easily different benefits get bundled into one sales story. In an automotive migration from on-premises and Pivotal Cloud Foundry applications to Google Cloud, Info Services reports a 15-20% performance improvement and initial cost neutrality. Automated tests checked behavior before and after migration. Weekly tracking sessions supported risk management across a global operating footprint.
Cost neutrality is an unusually useful phrase. A faster system can have value even when the opening bill has not shrunk. Buyers should decide whether they are paying for performance, resilience, easier provisioning or lower expenditure, then measure that particular result. Otherwise, a migration can satisfy its engineering objectives and disappoint the finance team at the same time.
For AI, the company’s August 2026 AWS implementation guide gives a broad production-ready deployment estimate of $25,000 to $250,000 or more, depending on complexity. It also estimates recurring AWS spend of $2,000-$8,000 a month for its managed Bedrock tier. Those are published planning ranges, not the disclosed prices of the projects above or a binding offer.
The guide assigns substantial effort to data preparation and separates demonstrations from hardened production systems. Its lesson for a buyer is to budget twice: once for building and integrating the application, then for running, reviewing and maintaining it. A model that produces an impressive answer still needs usable data, access controls and a defined standard for acceptable output.
Three products, one recurring headache
Info Services also packages repeatable work into named products. ClairAI addresses distributed platform reliability: monitoring pipelines, detecting failures and helping investigate causes through a conversational interface. The appeal is easy to understand. An alert says something is wrong; an operator still has to discover which dependency caused it and what to do next.
HiraCloud.ai applies a similar integration argument to recruiting. Its advertised workflow joins resume intake, video interviews, coding assessments, integrity checks and evaluation reports. It targets global capability centers, offshore delivery centers and IT services businesses. The product’s own site features Info Services as a case study, reporting 600 interviews scheduled in 60 days and more than 200 hours of senior architect time reclaimed.
InfoX moves the argument into customer operations. It offers AI agents across voice, chat, WhatsApp, email and SMS, with enterprise integrations and escalation to people. The attraction is continuity: a conversation ought to survive a change of channel. Its governance and monitoring features matter because resolving a request requires more than generating a plausible sentence.
These products make the company more than a supplier of project labor, but they do not erase the integration work. Hiring evaluations still need human standards. Reliability tools need signals from the systems they inspect. Customer-service agents need accurate knowledge and permission to take actions. The surrounding operating discipline determines whether the automation helps.
Buy the missing handoff
Info Services dates its consulting origins to 2004. Its company timeline describes hands-on engineering expanding in 2017, a cloud, data and automation phase in 2020, and an emphasis on agentic AI in 2024. Madhava Kota, identified as founder in Crunchbase, is listed on the company’s leadership page as President and CEO.
The commercial model leaves room for different kinds of buyer. Staff augmentation supplies people to an existing team. Scope-based engagements buy delivery against a defined problem. Managed services address the continuing burden of operating the result. Salesforce, SAP and ServiceNow work sits alongside cloud engineering, application modernization, security and data services.
Its public self-description emphasizes empathy, design thinking and an agile approach. Its stated mission is to help enterprises modernize, automate and scale. Those ideas become useful when translated into an engagement: identify the stalled step, assign responsibility and agree on a measurable result. A contract needs that specificity more than it needs a handsome statement of intent.
In the market, Info Services sits among enterprise integrators and specialist consultancies. Its combination of platform expertise, delivery people and proprietary accelerators gives buyers several ways to engage. That is a positioning choice, not proof that it will outperform a larger integrator or an internal team. The most instructive evidence remains the project method: change the outreach sequence, automate the release path, validate the replacement data.
For a prospective customer, the useful opening question is concrete: where does work wait? At the customer record, the deployment ticket, the interview calendar or the failed pipeline? Info Services’ cases suggest that the answer often lives between systems already purchased. Finding that interval is less theatrical than announcing a transformation. It is also where the next improvement can begin.