CEOs face a critical juncture: early AI investments struggle to show returns, yet waiting carries significant risk as agentic systems reshape how organizations generate value.
Offerings
A progression of engagement models that align with your organization's AI maturity and risk tolerance.
"Pathfinder"
POC that scouts value fast
Rapid validation of AI opportunities that align strategic investment with tangible business value.
Starting at $24,000
2-4 weeks
"Pathfinder" engagements help organizations move past uncertainty by creating product-focused prototypes that demonstrate what AI-powered offerings could deliver. These are not technical experiments—they're strategic tools designed to build stakeholder confidence and secure investment for next-stage development.
We work with your leadership team to identify high-potential use cases, architect scalable technical approaches, and build working prototypes that showcase meaningful business impact. The deliverable is a functioning demonstration that excites internal stakeholders while providing clear guidance on implementation requirements, timeline, and expected returns.
Organizations beginning their AI journey, or those seeking to validate specific use cases before committing to full development.
"Endeavour"
MVP that meets real users
External-facing minimum viable products that validate market demand and gather customer intelligence.
Starting at $72,000+
2-4 months
"Endeavour" projects take MVPs into the market to ascertain viability. We build production-quality MVPs designed to engage real customers, generate actionable feedback, and prove commercial viability. A dedicated product manager leads customer discovery, ensuring that what we build reflects actual user needs rather than internal assumptions.
Our methodology follows a structured progression: discovery workshops establish product-market fit hypotheses, design sprints create user experiences, implementation delivers working software, evaluation captures quantitative and qualitative feedback, and deployment ensures stable operations. The result is not just a functioning product, but validated learning that informs scaling decisions.
Organizations ready to test market demand for AI-enabled offerings, or those seeking to build competitive advantage through customer-facing innovation.
"Voyager"
Production pilot we operate & harden
Fully operational AI systems that we build, deploy, host, and iterate on your behalf.
Starting at $120,000+
3-6 months
"Voyager" projects bridge the gap between MVP and enterprise deployment. We take responsibility for running production-grade AI agents in our cloud environment, handling 3-4 months of real-world operation while continuously improving performance, reliability, and scalability.
This hands-on operational phase surfaces challenges that only emerge under production conditions: edge cases in agent behavior, integration complexities, performance optimization needs, and organizational change requirements. We document operational playbooks, establish monitoring and governance frameworks, and build the foundation for eventual transfer to your teams.
Organizations needing production validation before committing internal resources, or those requiring rapid deployment without immediate infrastructure investment.
"Enterprise"
Managed fleet in your cloud
Long-term managed services for portfolio-scale AI deployments within your infrastructure.
Custom pricing
6+ months
"Enterprise" engagements address the complexity of running multiple AI systems at scale. We transfer operational responsibility to your cloud environment while providing ongoing management services for an evolving fleet of AI applications—both internal and customer-facing.
Our managed service model handles platform operations, agent performance monitoring, continuous optimization, security and compliance, and scaling as usage grows. We work as an extension of your team, ensuring that agent deployments remain aligned with business strategy while adapting to technological advances and changing requirements.
Organizations operating multiple AI systems that require sustained technical excellence and strategic evolution, without building large internal AI operations teams.
Selected Work
Knowledge workers needed a way to automate complex, multi-step document analysis and content generation workflows that required more than simple chat interactions. Existing AI tools lacked the ability to capture repeatable business logic, connect to external data sources, and execute sophisticated workflows reliably at scale with enterprise-grade security.
Founded AI Hero (Delaware C Corp) and built a notebook-style workflow automation platform enabling users to create, customize, and execute AI-powered workflows. Architected a fully scalable Kubernetes infrastructure where each request spawned isolated pods for secure, parallel processing. Implemented comprehensive authentication, workflow orchestration, and chat capabilities with a focus on enterprise security requirements.
Achieved SOC 2 Type 2 compliance, providing enterprise customers with the security assurance needed for AI adoption. The scalable pod-based architecture enabled reliable execution of complex workflows involving PDF analysis, web scraping, and multi-step reasoning—transforming AI Hero into an enterprise-ready workflow automation platform.
Note: I'm a founder of A.I. Hero, Inc.
Galley, a culinary resource planning platform, struggled with recipe data onboarding—a critical bottleneck that slowed sales cycles and required extensive manual data entry from diverse, unstructured sources.
Designed and implemented an AI-powered Recipe Importer using Large Language Models to intelligently parse recipes from PDFs, Excel files, and other formats—transforming unstructured data into normalized, structured entries with automated deduplication.
Dramatically reduced sales cycles from 90 to 29 days while cutting recipe input time from 10 minutes to under 1 minute, enabling instant recipe conversion during sales demos and positioning Galley as a true data platform.
Note: Galley was a customer of Tribe AI.
Native Studios' Customer needed to democratize access to millions of analytical records, enabling non-technical users to query complex SQL databases using natural language and receive instant, chart-based insights—moving far beyond basic SQL generation to reliable, production-grade data visualization.
Architected and built a sophisticated natural language query system featuring a global schema interpretation model, semantic reasoning engine, and optimized snippet-based query generation. The system intelligently maps user intent to SQL, selects appropriate visualizations, and renders interactive charts—all from conversational queries over large-scale Snowflake data warehouses.
The MVP enabled users to explore millions of records through natural conversation, with transparent SQL generation and automated chart rendering. The Python-powered NLP engine with React/D3.js frontend transformed data accessibility across the organization.
LlamaIndex was running on ECS, facing unpredictable usage spikes that risked performance issues and rising costs. The company also needed infrastructure planning for their on-premises offering.
Led migration from ECS to a resilient Kubernetes-based auto-scaling architecture on AWS, provisioning compute resources dynamically based on real-time traffic while planning infrastructure patterns for their on-prem deployment strategy.
Achieved near-zero downtime during traffic surges, optimized operational costs, enabled seamless scaling to support rapid growth, and established the foundation for their on-premises offering.
Arcade.dev needed to enable enterprise customers to deploy AI authentication services in their own cloud environments (AWS, Azure, GCP) and on-premises infrastructure—a critical requirement for security-conscious organizations wanting AI agents to take authenticated actions on behalf of users.
Architected and implemented production-grade Helm charts and multi-cloud Terraform scripts enabling one-command deployment of Arcade's auth service across all three major cloud providers and on-prem environments. Built comprehensive infrastructure with horizontal pod autoscaling, TLS automation, observability stack, and cloud-specific ingress configurations.
Transformed Arcade's go-to-market strategy by enabling customer-controlled deployments, opening enterprise sales opportunities with security-conscious organizations. Delivered scalable infrastructure supporting 1-15 pod autoscaling with 50Gi stateful storage and full observability.
Note: Arcade AI was a customer of A.I. Hero, Inc. where I'm a founder.
Data Scientists and Machine Learning Engineers were having a hard time sharing A100 machines on Slurm and implementing fine tuning pipelines. Training and fine-tuning large-scale foundational models (2048 H100 GPUs) in different cloud environments presented significant operational complexity.
Developed a containerized, Kubernetes-driven training platform in Oracle and Azure clouds, enabling fine-tuning and pre-training workflows and optimized team-wide GPU resource allocation.
Accelerated fine-tuning cycles, reduced training costs substantially, and facilitated faster iteration, speeding new model deployments.
Note: Adept was a customer of Tribe AI.
Grindr aimed to modernize ML pipelines and seamlessly integrate a Generative AI ("Wingman") feature without disrupting user experience.
Architected a robust, scalable MLOps platform on AWS, automated via Terraform, with streamlined CI/CD pipelines to support rapid GenAI experimentation and implementation.
Accelerated AI feature releases, significantly reduced operational overhead, and enhanced user experiences through innovative, personalized AI capabilities.
Note: Grindr was a customer of Tribe AI.
Handling over one million daily OCR/NLP predictions demanded a high-throughput platform and seamless DevOps collaboration.
Deployed a resilient MLOps architecture optimized for high-performance inference, leading a cross-functional team to implement robust operational best practices.
Delivered sub-second response times, significantly expanded AI service capabilities, and enhanced reliability and customer satisfaction.
Note: I was a Director of Data Science at Appen
AI startups required rapid, consistent, and error-free infrastructure deployments to support diverse, fast-evolving tech stacks.
Developed comprehensive Infrastructure as Code (IaC) pipelines leveraging Terraform, Kubernetes, and Helm, ensuring uniform, repeatable deployments.
Eliminated configuration drift, minimized human errors, accelerated feature delivery, and provided stable foundations for rapid, scalable growth.
Note: The startups were customers of A.I. Hero, Inc. where I'm a founder.
Let's Talk
Free Consultation
Schedule a free 30 min consultation to discuss your needs and if we're a good fit
rahul@elevate.do
I look forward to chatting with you.