TalentPros Hub helps leaders make confident decisions before funding complex cloud, data, AI, integration, and cybersecurity programs. We turn technical uncertainty into an executive roadmap, secure architecture, and phased delivery plan.
Focused services for clients who need executive clarity, technical depth, and security discipline across cloud, infrastructure, integration, data, AI, and operations.
Assess and modernize applications, databases, infrastructure, and operating models across AWS, Azure, and GCP.
Design the network, segmentation, hybrid connectivity, and data center integration foundation needed for secure cloud adoption.
Connect internal platforms, external partners, files, APIs, events, and legacy systems using secure synchronous and asynchronous patterns.
Map fragmented sources, reports, KPIs, ownership, and MDM gaps into a practical platform and engineering roadmap.
Security architecture, control validation, privacy, GRC, and risk mitigation built into cloud, data, and AI decisions.
Dedicated delivery and operations PODs with named outcomes, weekly reporting, and senior oversight.
Equip your teams to own what we build — cloud, data, AI, and security fluency that lasts.
Use clean, policy-governed data and documents to support analytics, ML, RAG, GenAI, and controlled agentic workflows.
A practical blueprint for turning fragmented enterprise data into governed analytics, quality data products, feature pipelines, ML, and secured LLM decision support with RAG, LangChain-style orchestration, citations, guardrails, and auditable actions.
We start with source, system, report, KPI, owner, and steward discovery, then define the roadmap for resolving fragmented analytics, duplicate metrics, inconsistent reporting, and MDM gaps.
ETL/ELT pipelines land data into object storage, warehouse, and lakehouse layers where transformations, models, tests, data quality checks, and access controls create usable, governed business data.
Once the data is clean and governed, AI can support decision-making over enterprise policies, process documents, and business data through secured LLMs, RAG, MLflow, feature stores, and agentic workflows.
Discovery & assessment, fit gap analysis and design, and phased delivery are tied together through workshops, documented scope, MVP milestones, backlog governance, and measurable decision points.
Workshops establish business objectives, cloud and data scope, application and workload inventory, source-system discovery, report and KPI review, analytics discrepancy analysis, MDM ownership, network and integration baselines, risk posture, and migration readiness.
Current-to-target fit gap translates assessment findings into cloud migration strategy, 7R disposition, landing-zone design, network design, integration design, data platform blueprint, AI value blueprint, governance model, and phased roadmap.
Delivery runs in agile phases with agreed MVP scope, milestone demos, Scrum ceremonies, Jira backlog transparency, Confluence decisions, burn-up/burn-down visibility, and executive checkpoints before the final release.
Continuous improvement turns delivery metrics into operating confidence across cost, reliability, security posture, service quality, AI/data quality, and roadmap iteration.
For a new startup, trust has to be earned early. Our approach gives leaders confidence through senior-led workshops, visible delivery controls, clear artifacts, and practical advice before a client commits to a large program.
Short advisory workshops help clients validate cloud, data, AI, security, GTM, sales, delivery, or operating-model direction. This works when leaders need a second pair of experienced eyes before funding a larger program.
Startups can use our experience to think like an enterprise earlier: secure architecture, delivery governance, consultative selling, sell-to and sell-through motions, partner models, and hybrid delivery planning.
Enterprises can move faster without ignoring risk: define an MVP, test assumptions, fail quickly in controlled increments, learn from feedback, and mature the solution through secure agile releases.
Scrum ceremonies, Jira backlog visibility, Confluence decisions, milestone demos, risk logs, dependency tracking, and burn-up/burn-down views show progress before the final handoff.
These anonymized examples are based on our migration, AI-ready data, and insurance fraud RAG solution materials. They are written for executives first, with enough technical detail to show delivery depth.
A legacy enterprise environment needed lower operating cost, faster provisioning, stronger governance, and a future-ready architecture. The approach used business-led discovery, dependency mapping, 7R classification, landing-zone governance, wave execution, CDC database migration, validation, rollback readiness, and hypercare.
A business with scattered operational databases, SaaS data, streaming events, files, logs, and inconsistent reporting needed a trusted data foundation. The design used batch and streaming ingestion, cloud object storage, lakehouse and warehouse patterns, Bronze/Silver/Gold data products, governance, lineage, RBAC/ABAC, and BI/API enablement.
An insurance fraud solution needed to move beyond ad-hoc LLM prompts and basic ML scoring. The refined architecture used feature store signals, document intelligence, hybrid retrieval, LangGraph-style agent orchestration, MCP tool governance, validation agents, structured fraud verdicts, guardrails, audit events, observability, and human-in-the-loop escalation.
Start with a 30-minute fit discussion. If there is a match, we can produce a short advisory memo, reference architecture, or roadmap workshop proposal.
We focus where modernization is hardest — where downtime, compliance, and data sensitivity raise the bar on every decision.
Regulated workloads, real-time data, and zero-tolerance security postures.
HIPAA-aligned cloud, clinical data, and AI for research & care delivery.
Secure modernization, FedRAMP-aware architectures, and citizen-data protection.
Unified data, demand intelligence, and resilient global supply-chain platforms.
Subcontracted cloud, AI, data, and security delivery — extending your bench worldwide.
Opinionated starting points — battle-tested patterns, IaC modules, and playbooks that get you to outcomes faster without locking you in.
A packaged accelerator to assess, prioritize, and plan cloud adoption across infrastructure, applications, operations, and data platforms — from maturity baseline to 7R migration roadmap and executive business case.
Secure reference architecture for enterprise GenAI — model gateway, prompt management, evaluation, and guardrails — on AWS Bedrock, Azure OpenAI, or GCP Vertex.
Opinionated Snowflake + Databricks medallion design with dbt models, Airflow orchestration, lineage, and data contracts — your single source of truth, ready for AI.
Automated control mapping for SOC 2, HIPAA, and PCI — with remediation playbooks tied to your cloud baseline.
Repeatable agile migration waves run by dedicated PODs — landing zones, IaC modules, runbooks, cutover playbooks, Jira tracking, and weekly KPI reporting.
Production-grade LangChain / LangGraph orchestration with approved-source retrieval, validator, policy gates, audit trails, and human-escalation paths.
Detect and prevent data-quality drift, suspicious access, policy violations, hallucinations, and unsafe tool actions — with continuous monitoring and exception workflows.
24/7 POD-based operations for cloud, data platforms, and AI workloads — predictable economics, defined SLAs, continuous optimization.
Curated videos, articles, webinars, playbooks, and training content for cloud, infrastructure, integration, data, AI, and security practitioners. The first frameworks are listed now, with weekly KnowledgeWave updates as new content is published.
How operational databases, SaaS, streams, files, and logs become governed Bronze, Silver, and Gold data products for analytics, APIs, ML, and RAG.
A practical walkthrough of access gates, approved-source retrieval, validator layers, policy checks, audit events, and human escalation paths.
Design choices for using Snowflake governed analytics, Databricks Spark/Delta processing, dbt models, and Airflow orchestration together.
Turning curated business data into retrieval-ready context with embeddings, metadata filters, vector stores, lineage, and confidentiality controls.
A structured readiness flow covering discovery and assessment, maturity baseline, workload prioritization, TCO/ROI, migration roadmap, and business-case handoff.
How classification tags, owners, lineage, PII/PHI flags, retention rules, and access policies travel from source systems to BI, APIs, and LLM retrieval.
Patterns for catching data quality drift, suspicious access, unsafe tool actions, policy exceptions, and hallucination risk before they become business incidents.
Hands-on enablement for secure IaC, CI/CD controls, cloud baselines, AI workload guardrails, and operational readiness across AWS, Azure, and GCP.
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TalentPros Hub is a new professional services company led by practitioners with long enterprise careers in cloud, infrastructure, data, AI, integration, cybersecurity, compliance validation, and risk mitigation. Together, our founders have managed $100M+ in cloud, data, AI, and infrastructure transformation programs for Fortune 500 multinational companies and regulated organizations.
We bring senior architecture and security leadership into the first conversation, then recommend the smallest useful next step: assessment, fit gap design, roadmap, delivery POD, or risk review.
Srini is a business-aligned technology transformation leader with 20+ years of experience guiding enterprises through cloud modernization, infrastructure programs, data platform builds, analytics modernization, and AI adoption. At TalentPros Hub he owns enterprise architecture, pre-sales strategy, and founder-led delivery — partnering with executive teams to turn modernization ambition into shipped, governed outcomes.
His prior enterprise leadership includes $52M+ in cloud order-booking experience during his Wipro tenure. That track record informs TalentPros Hub’s go-to-market discipline, executive solutioning, and delivery governance as a startup professional services firm.
His practice spans cloud modernization across AWS, Azure, and GCP; infrastructure and cloud network architecture; hybrid connectivity with AWS Transit Gateway and Azure ExpressRoute; data center modernization; data strategy and engineering roadmaps; MDM, owners and stewards; data platform architecture on object storage, Snowflake, Databricks, dbt and Airflow; and production AI with RAG, LangChain-style orchestration, feature stores, MLflow, vector pipelines, and guardrails — supported by DevSecOps and infrastructure-as-code practices.
Prasad is a strategic program leader with 20+ years driving digital transformation, enterprise IT initiatives, and secure product delivery across healthcare, oil & gas, energy, defense, and retail. At TalentPros Hub he leads the cybersecurity and risk services practice — owning security architecture, compliance validation, and risk mitigation across every engagement.
His remit covers AI-powered GRC and continuous compliance, enterprise security architecture and KPIs for multi-cloud ecosystems, privacy and data governance with ServiceNow, OneTrust, and BigID, cybersecurity capability assessments, HITRUST/NIST alignment, OT/ICS security programs, healthcare cloud migration and data protection, and incident response programs.
Enterprise modernization works when senior leaders own the outcome and security is engineered in, not bolted on. We bring both founders into every engagement and back them with disciplined global PODs.
TalentPros Hub is a startup, but the work is led by practitioners with two decades of enterprise delivery experience. The first discussion is designed to earn confidence, not force a sales cycle. We review your objective, current environment, constraints, risks, delivery model, and possible next steps, then recommend whether an advisory workshop, short assessment, fit gap design, roadmap, delivery POD, or no engagement is the right answer.
Tell us your business objective, current environment, risk concerns, and timeline. We will respond within one business day with a practical starting point: discovery and assessment, fit gap analysis and design, delivery, managed POD, or focused security review.