Engineering the
Intelligence Revolution
Modern AI engineering + deep domain expertise
We are not just developers; we are industrial technologists. Akshaya.io helps teams move beyond demos to production by designing AI architecture, building secure RAG and agentic solutions, deploying machine learning at scale, and operationalizing data pipelines and model lifecycle management. We work end-to-end—from strategy and prototyping to product development, deployment, and enablement.
Our AI consulting services span Retrieval-Augmented Generation (RAG) architecture implementation, Model Context Protocol (MCP) integrations, NLP for unstructured data, Vision AI and custom computer vision solutions (OpenCV), vector database integration, and large-scale machine learning. We combine big data engineering, open-source acceleration, and cloud-native delivery across AWS, Azure, and Google Cloud to create reliable, secure, and measurable outcomes.
Core Technical Competencies
The stack that powers enterprise AI delivery
Generative AI & RAG (The Brain)
We build context-aware GenAI systems grounded in enterprise knowledge using RAG, semantic search, evaluation harnesses, and guardrails-reducing hallucinations and improving trust.
Agentic Workflows + MCP Integrations
Design agents that safely use tools and enterprise systems (APIs, databases, ticketing, ERP/CRM). Use MCP patterns where applicable to standardize tool access, orchestration, and governance.
NLP for Unstructured Data
Automate extraction and reasoning across contracts, claims, clinical documents, engineering reports, and PDFs using classification, entity extraction, summarization, and retrieval pipelines.
Vision AI & OpenCV (The Eyes)
Turn pixels into operational signals with custom computer vision-inspection, anomaly detection, video analytics, and document imaging-integrated into edge or cloud workflows.
Vector Databases & Retrieval Engineering
Select and implement VectorDB stacks and retrieval strategies (hybrid search, reranking, chunking, embeddings, evaluation) for high-precision search and copilots.
Machine Learning & Advanced Analytics
Forecasting, optimization, anomaly detection, recommendations, and risk scoring with robust ML practices and interpretable model strategies.
Big Data Engineering (The Foundation)
Build scalable data foundations: streaming/batch ingestion, lakehouse patterns, governance-ready pipelines, and reliable feature generation for ML/AI.
MLOps / LLMOps
Operationalize AI with CI/CD, monitoring, drift detection, evaluation, prompt/version management, and incident workflows.
Multi-Cloud & DevSecOps (AWS / Azure / GCP)
Cloud-agnostic reference architectures with secure networking, IAM, encryption, key management, and compliance-aligned patterns across major clouds.
Open-Source First Delivery
Leverage best-fit open-source frameworks and toolchains to reduce time-to-value while maintaining enterprise supportability and security controls.
Core Consulting Services
What we deliver for enterprise clients
AI Strategy & Roadmapping
Use-case prioritization, ROI modeling, risk assessment, and data readiness evaluation to build a practical AI roadmap.
Typical Deliverables
Prioritized use-case backlog, data readiness assessment, architecture direction, business case
AI Product Development
MVP to production development including UX design, backend integrations, and observability setup.
Typical Deliverables
Working product, API integrations, monitoring dashboards, deployment runbooks
Enterprise Knowledge Systems
RAG architectures, semantic search, content pipelines, and governance controls for enterprise knowledge.
Typical Deliverables
Knowledge retrieval system, content ingestion pipelines, evaluation framework
Document Automation
Intelligent document processing, extraction, classification, and workflow automation.
Typical Deliverables
IDP pipelines, extraction models, workflow integrations, accuracy metrics
Computer Vision Solutions
Inspection systems, monitoring applications, and operational analytics using custom vision models.
Typical Deliverables
Vision models, edge deployment, integration APIs, performance dashboards
Data Platforms for AI
Lakehouse/warehouse architecture, streaming pipelines, feature stores, and data governance.
Typical Deliverables
Data platform, ingestion pipelines, feature engineering, data quality monitoring
Security & Governance by Design
Security controls, auditability, RBAC, and SDLC integration for AI systems.
Typical Deliverables
Security architecture, access controls, audit logging, compliance documentation
Enablement & Training
Team training, operating model design, and handoff playbooks for sustained success.
Typical Deliverables
Training materials, runbooks, operating procedures, knowledge transfer sessions
Industry Expertise
Deep domain consulting across critical sectors
Oil & Gas
Subsurface & Operations Intelligence
Use Cases
- Operational copilots for engineering and maintenance knowledge (RAG over manuals, work orders, SOPs)
- Predictive maintenance and anomaly detection for rotating equipment and field assets
- Seismic and subsurface analytics support (interpretation acceleration, report synthesis, knowledge retrieval)
Typical Data
SCADA/IIoT telemetry, CMMS/EAM work orders, historian data, inspection reports, PDFs, manuals, seismic metadata
Outcomes
Reduced downtime, faster troubleshooting, improved safety reporting
Healthcare
Clinical Precision & Privacy
Use Cases
- Clinical document intelligence: intake automation, summarization, coding assistance (with governance controls)
- Prior authorization / utilization management support workflows
- Member/provider copilots grounded in policy and benefit documents
Typical Data
EHR exports (where allowed), claims/encounters, clinical notes, policy docs, call transcripts
Outcomes
Faster cycle times, reduced administrative burden, improved consistency
Insurance
Automated Risk & Claims
Use Cases
- Claims automation: triage, fraud signals, subrogation insights
- Underwriting intelligence: extraction from submissions, risk summaries
- Customer operations copilots (policy Q&A, guided workflows)
Typical Data
Claims systems, policy documents, adjuster notes, images (where applicable), call center logs
Outcomes
Reduced leakage, improved throughput, better customer experience
Defense
Mission-Critical AI
Use Cases
- Secure, on-prem/air-gapped RAG for mission and program documentation
- Computer vision for inspection, monitoring, or evidence review (as permitted)
- Knowledge management and decision support with audit trails
Typical Data
Controlled documents, maintenance logs, sensors, reports
Outcomes
Faster information access, stronger traceability, improved readiness
Real Estate
Asset & Operations Intelligence
Use Cases
- AI copilots for asset management, leasing, maintenance, and tenant operations
- Document and invoice intelligence: extraction, categorization, variance detection
- Predictive analytics for occupancy, renewals, rent strategy, and maintenance prioritization
Typical Data
PMS/ERP exports, vendor invoices, lease docs, work orders, marketing data
Outcomes
Lower OPEX, improved NOI drivers, faster operations
E-commerce
Personalization & Search
Use Cases
- Personalization and recommendations
- Search relevance improvements (hybrid lexical + vector search, reranking)
- Customer support automation and returns intelligence
Typical Data
Clickstream, catalog, reviews, support tickets, order/return history
Outcomes
Higher conversion, lower support cost, improved retention
Energy & Utilities
Grid & Sustainability Intelligence
Use Cases
- Forecasting and optimization for DERs, storage, and EV charging operations
- Grid operations decision support and anomaly detection
- Sustainability reporting enablement (where data is available)
Typical Data
AMI, DERMS/EMS telemetry, charger networks, market/price feeds, weather
Outcomes
Improved reliability, reduced cost, more resilient operations
Learn more about AI for SustainabilityWhy Akshaya.io
Open-Source Advantage
Avoid lock-in with best-fit open-source frameworks. Enterprise supportability without proprietary constraints.
Enterprise-Grade Security
Auditability, RBAC, encryption, and SDLC integration baked in from the start. Security by design, not afterthought.
Business-First Delivery
Outcomes over outputs. We focus on adoption, operating model, and measurable business impact.
Offshore Capabilities & 24/7 Coverage
Leverage our global delivery model for continuous development, support, and cost-effective scaling. Our offshore teams provide round-the-clock coverage to accelerate your AI initiatives.
24/7 Development Cycles
Follow-the-sun model enables continuous progress. Your project advances while you sleep.
Global Talent Pool
Access skilled AI engineers, data scientists, and ML specialists across time zones.
Round-the-Clock Support
Production support and incident response available 24/7 for mission-critical systems.
Cost-Effective Scaling
Scale your AI team up or down with flexible offshore engagement models.
Offshore Delivery Services
AI Development
- RAG & GenAI implementation
- ML model development & training
- Computer vision pipelines
- NLP & document processing
- Agentic workflow development
Data Engineering
- Data pipeline development
- ETL/ELT implementation
- Data quality & governance
- Feature engineering
- Vector database management
Operations & Support
- MLOps/LLMOps management
- Model monitoring & drift detection
- 24/7 production support
- Incident response & escalation
- Performance optimization
How We Work
Our delivery model for enterprise AI success
Discovery & Use-Case Prioritization
Assess value, feasibility, data readiness, and ROI to identify highest-impact opportunities.
Architecture & Rapid Prototype
Design secure-by-design architecture and build measurable prototypes with clear success criteria.
Build & Productionize
Develop production pipelines, evaluation frameworks, monitoring, and governance controls.
Operate & Improve
Manage drift detection, cost optimization, incident response, and continuous enablement.
Technology & Platforms
Cloud
AWS, Microsoft Azure, Google Cloud
Data
Lakehouse/warehouse patterns; streaming + batch pipelines
AI/ML
Modern ML frameworks; LLMOps/MLOps patterns; evaluation harnesses
Vector Search
Vector databases + hybrid search + reranking
CV
OpenCV + vision models
APIs/Integration
Secure API gateways, event-driven architectures, enterprise systems
Leadership
Akshaya.io is led by Ganesh Raju, an entrepreneur and digital transformation leader focused on applied AI, enterprise architecture, and building production systems.
View Ganesh Raju on LinkedInFrequently Asked Questions
What is RAG and when should we use it?
RAG (Retrieval-Augmented Generation) grounds LLM responses in your enterprise knowledge by retrieving relevant documents before generating answers. Use RAG when you need accurate, contextual responses from internal documentation, policies, manuals, or knowledge bases-reducing hallucinations and improving trust. It's essential for enterprise copilots, customer support automation, and any GenAI use case requiring factual accuracy.
What is MCP and how does it help agentic AI integrations?
MCP (Model Context Protocol) is a standardized way for AI agents to interact with external tools, APIs, and enterprise systems. It provides a governance-ready pattern for tool access, allowing agents to safely query databases, call APIs, create tickets, or interact with ERP/CRM systems. MCP helps organizations build agentic workflows with proper controls, audit trails, and human-in-the-loop approvals where needed.
How do you secure GenAI systems and prevent data leakage?
We implement defense-in-depth: input validation and prompt hardening to prevent injection attacks, output filtering to block sensitive data disclosure, RBAC-controlled retrieval ensuring users only access authorized content, audit logging for all interactions, and network isolation where required. We also implement guardrails for content safety and establish clear data handling policies for model providers.
Do you support on-prem or air-gapped deployments?
Yes. We design architectures that can run entirely on-premises or in air-gapped environments using open-source models and self-hosted infrastructure. This is particularly relevant for defense, healthcare, and financial services clients with strict data residency or security requirements. We help select appropriate models and build deployment patterns that meet compliance needs.
How do you choose a vector database and retrieval strategy?
Selection depends on scale, latency requirements, existing infrastructure, and retrieval complexity. We evaluate options like Pinecone, Weaviate, Milvus, pgvector, and others based on your needs. Beyond database selection, we design the full retrieval strategy: chunking approach, embedding model selection, hybrid search (combining vector and keyword), reranking, and evaluation frameworks to measure retrieval quality.
What does a typical AI engagement timeline look like?
A focused use case typically follows: Discovery and prioritization (2-4 weeks), Architecture and rapid prototype (4-6 weeks), Build and productionize (8-16 weeks depending on complexity), then ongoing operations support. We emphasize measurable milestones at each phase and adjust scope based on learnings. Complex enterprise programs may run multiple workstreams in parallel.