Rapid AI Development done incrementally, not all at once.
1. Full-Stack AI Engineering
Design, build, and deploy scalable AI Applications - from prototypes to enterprise-grade solutions.
2. On Demand AI Labs and Experimentation
We help you answer critical AI questions through structured research and experimentation.
3. AI Data Engineering Services
From legacy systems to modern platforms, we accelerate data programs using deep domain expertise and Generative AI accelerators without sacrificing quality or control.
4. Custom Private AI & Edge Solutions
We help you build compact, efficient AI that runs anywhere even offline.
Build AI applications that run on the edge - from smart devices to real-time industrial systems - optimized for speed and privacy.
Why Work With Us
- Proven track record in building custom AI solutions that scale
- Deep understanding of modern AI tools, frameworks, and platforms
- Flexible team models and cost-effective solutions tailored to your goals
GenAI Protos
Most teams do not have a website access problem. They have a usable data problem.
Web content is unstructured, page formats vary, JavaScript-rendered pages complicate extraction, and manual cleaning becomes difficult to maintain across repeatable workflows.
SiteScriber turns that into an API-driven flow using Firecrawl for extraction, optional schema-based structuring, LangChain/OpenAI refinement, and JSON responses that can feed analytics, automation, databases, and other downstream systems.
The architecture question is whether website content can become a reusable interface rather than another one-off scraping script.
SiteScriber documents the website-to-API implementation:
www.genaiprotos.com/solutions/sitescriber?utm_sour…
For broader full-site crawling with LLM-ready Markdown, HTML, and structured JSON output, AI Crawler shows the adjacent implementation:
www.genaiprotos.com/solutions/ai-crawler-instantly…
#GenAIProtos #EnterpriseAI #AIEngineering #DataEngineering #EnterpriseArchitecture #CTO #AILeader
17 hours ago | [YT] | 1
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GenAI Protos
Multi-step enterprise workflows often require more than one model interaction or an isolated AI assistant.
The harder problem is coordinating specialised tasks, adapting to changing inputs, handling failure paths, and keeping the workflow observable enough to trust in production.
GenAI Protos designs these systems around research, planning and orchestration, monitoring and validation, and fact-checking agents, with parallel execution, dynamic orchestration, fault tolerance, and transparency built into the architecture.
A concrete implementation is the #NVIDIA Powered Research Agent. GenAI Protos built a Planner-Executor-Reporter workflow that combines document retrieval, web intelligence, and live progress updates into a structured research process:
www.genaiprotos.com/project/nvidia-powered-researc…
#GenAIProtos #AgenticAI #EnterpriseAI #AIEngineering #AIObservability #EnterpriseArchitecture #AILeader
4 days ago | [YT] | 2
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GenAI Protos
The model looked accurate.
The workflow was not ready.
That is where many healthcare AI pilots get stuck.
Clinical AI cannot depend only on model performance. It has to work inside real care environments where fragmented patient data, clinical validation, human review, privacy, and workflow accountability all matter.
GenAI Protos approaches healthcare AI from that production layer.
The focus is not just building an AI model. It is designing the surrounding system: local validation, clinician-in-the-loop review, workflow integration, secure architecture, and audit-ready outputs that can support regulated healthcare operations.
In radiology, this means connecting imaging data, patient history, clinical notes, lab results, and structured reporting into one workflow while keeping radiologists in control of final interpretation.
That is the difference between AI that performs in a demo and AI that can support real clinical work.
Explore GenAI Protos healthcare AI:
www.genaiprotos.com/industry/healthcare?utm_source…
Related case study:
www.genaiprotos.com/case-studies/ai-powered-radiol…
#GenAIProtos #HealthcareAI #ClinicalAI #EnterpriseAI #RadiologyAI #AIEngineering
1 week ago | [YT] | 2
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GenAI Protos
The incident review was meant to identify what failed.
The harder question was why the system did not act sooner.
For technology and operations leaders, siloed telemetry and alert dashboards do not solve the operational problem when support teams are still reacting after disruption, L1 queues keep growing and known issues require repeated manual intervention.
In this GenAI Protos AIOps deployment, telemetry, logs, metrics and event streams were connected to anomaly detection, predictive maintenance, intelligent ticketing, approved runbooks and L1 support automation.
Resolution outcomes also fed back into the operating loop to improve future detection and reduce false positives.
The result was a production system with measurable impact: 20% lower IT support operating cost, 15% improved system uptime, 40% faster ticket resolution, more than 90% predictive accuracy and over 60% of L1 tickets resolved autonomously.
Review the full AIOps case study: www.genaiprotos.com/case-studies/ai-powered-it-ope…
Explore GenAI Protos’ software engineering and modernisation capabilities:
www.genaiprotos.com/industry/software-engineering?…
#GenAIProtos #AIOps #ITOperations #SoftwareEngineering #AIEngineering #Automation #MLOps
1 week ago | [YT] | 3
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GenAI Protos
Data modernization slows down when repetitive engineering work consumes the same capacity needed for architecture, validation, and migration decisions.
GenAI Protos applies AI accelerators across that delivery layer: ETL and ELT development, legacy-code conversion, data modeling, metadata and lineage, testing, documentation, governance, and AI-ready data foundations. The service supports migrations from legacy platforms such as Teradata, Oracle, SQL Server, Netezza, DB2, SSIS, and Hadoop into modern platforms including Microsoft Fabric, Snowflake, Databricks, BigQuery, and Redshift. Automated reconciliation and quality checks remain part of the migration flow so acceleration does not remove validation.
The objective is to automate high-volume pattern work while keeping the resulting platform understandable, governable, and operable by the internal data team.
The AI Data Engineering Services page shows the full modernization scope, supported platforms, accelerator set, validation approach, and AI-ready data foundation work:
www.genaiprotos.com/blog/ai-governance-with-nist-a…
#GenAIProtos #DataEngineering #DataModernization #AIEngineering #DataGovernance
1 week ago | [YT] | 2
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GenAI Protos
Enterprise image editing gets difficult long before the model fails.
The real challenge is operational: handling multimodal inputs, long-running requests, image encoding, external model reliability, and session continuity in a workflow creative and product teams can actually use.
GenAI Protos built Image Edit AI around that operating layer: a React conversational interface, FastAPI orchestration, Gemini vision through OpenRouter, structured image handling, persistent sessions, and retry/error handling.
The architecture matters because image generation and editing have to remain usable across repeated interactions, not only produce a strong first output.
The Image Edit AI page documents the implementation from prompt and image submission through model execution, response formatting, and session persistence:
www.genaiprotos.com/solutions/image-edit-ai?utm_so…
#GenAIProtos #EnterpriseAI #EnterpriseArchitecture #CTO #AILeader
2 weeks ago | [YT] | 2
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GenAI Protos
The difficult part of compliance automation is not generating a fast answer. It is making the answer defensible. GenAI Protos built the compliance intelligence platform around live retrieval from authoritative sources, planned query decomposition, citation-bound generation, verification, and a reviewable execution trail.
That architecture is what allowed routine lookups to move from hours of manual cross-referencing to minutes without removing evidence from the workflow. For regulated finance use cases, speed and auditability have to be engineered together.
See the operating model and implementation details, then read the Tier-1 bank compliance story:
www.genaiprotos.com/solutions/bank-cut-compliance-…
www.genaiprotos.com/blog/how-a-tier-1-bank-cut-com…
#GenAIProtos #FinanceAI #ComplianceAI #AgenticAI #AIGovernance #AIEngineering #RegTech
2 weeks ago | [YT] | 4
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GenAI Protos
Private enterprise search becomes a full-stack architecture decision when documents, embeddings, retrieval, and model inference all need to remain inside the same environment.
Spark Vault is GenAI Protos' on-prem search implementation for sensitive medical and healthcare documents. Beyond local LLM inference, the architecture uses vision-language parsing and semantic chunking for complex files, Nomic embeddings, PostgreSQL with pgvector and Apache AGE, and a hybrid retrieval path that combines semantic similarity, fuzzy keyword matching, and graph relationships. The runtime is containerized on NVIDIA DGX Spark so ingestion, indexing, retrieval, and contextual answer generation stay local.
That makes privacy part of the search architecture itself rather than a control added after the knowledge layer is built.
The Spark Vault source page documents the complete local ingestion, document-understanding, hybrid retrieval, database, and DGX Spark deployment architecture:
www.genaiprotos.com/solutions/spark-vault-enterpri…
#GenAIProtos #PrivateAI #EnterpriseSearch #RAG #AIEngineering #OnPremAI #HealthcareAI
2 weeks ago | [YT] | 5
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GenAI Protos
Production LLM failures rarely come from the model alone.
They happen when enterprise AI systems lack controls around data access, prompt injection, outputs, tools, and runtime behaviour.
At GenAI Protos, we engineer these controls into the architecture helping teams move from AI pilots to secure, governed production systems.
Because enterprise AI security cannot be a patch added after deployment.
Explore More at: www.genaiprotos.com/?utm_source=youtube&utm_medium…
#GenAIProtos #EnterpriseAI #AISecurity #AIGovernance #LLMSecurity #AgenticAI #AIEngineering #AILeader
2 weeks ago (edited) | [YT] | 4
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GenAI Protos
A validated AI prototype is only the beginning of the enterprise engineering work.
Once a use case is validated, the work shifts from model experimentation to architecture, backend engineering, data pipelines, enterprise integrations, testing, deployment, and ongoing monitoring.
GenAI Protos covers that lifecycle across discovery, architecture design, production-aligned prototyping, full-stack development, deployment, and MLOps for custom AI applications, agents, copilots, and RAG systems.
For teams defining the path from prototype to monitored production, the Full-Stack AI Engineering page maps the six-stage delivery model and includes built applications such as ScheduleWise, Legal AI Assistant, and FactCheck:
www.genaiprotos.com/our-services/full-stack-ai-eng…
For a deeper architecture reference, the Full-Stack AI Engineering Playbook is here:
www.genaiprotos.com/resources/playbook?utm_source=…
#GenAIProtos #EnterpriseAI #AIEngineering #EnterpriseArchitecture #RAG #CTO #AILeader
3 weeks ago | [YT] | 5
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