Working Hours
Monday 9:00 am - 8.00 pm
Tuesday 9:00 am - 8.00 pm
Wednesday 9:00 am - 8.00 pm
Thursday 9:00 am - 8.00 pm
Friday 9:00 am - 8.00 pm
Saturday 9:00 am - 8.00 pm
Sunday Closed
Support for 1 - 8 hours per day in weekdays in the given time interval
Get practical RAG and retrieval pipeline support for development, debugging, search optimization, integrations, evaluation, deployment, and production issues.
Our RAG Pipeline Job Support service provides real-time, proxy, and milestone-based assistance for professionals building Retrieval-Augmented Generation (RAG) systems in enterprise and startup environments. We help developers design, debug, optimize, and deploy RAG architectures that reduce hallucinations, improve factual accuracy, and scale reliably in production.
This service is ideal for GenAI engineers, ML engineers, and consultants working on knowledge-driven chatbots, internal copilots, document intelligence systems, and enterprise search applications under strict delivery timelines.
Developer skills
Our RAG Pipeline Job Support covers the complete technical lifecycle of retrieval-augmented systems. We assist with document ingestion pipelines, data preprocessing, chunking strategies, metadata enrichment, and embedding generation for structured and unstructured data. Developers receive hands-on support in selecting and tuning vector databases, implementing hybrid search (semantic + keyword), and optimizing similarity search for accuracy and latency.
We also help integrate RAG workflows with modern GenAI frameworks such as LlamaIndex and LangChain, including retrievers, query engines, memory layers, and response synthesis. Additional support includes prompt–retrieval coordination, context window optimization, grounding techniques, and evaluation strategies to reduce hallucinations and improve answer faithfulness. Our experts assist with debugging retrieval failures, relevance drops, token overflows, and performance bottlenecks across cloud and on-premise deployments.
RAG Pipeline Job Support
- We support professionals working in roles that require building or maintaining RAG-based GenAI solutions, including GenAI Engineer, LLM Engineer, Machine Learning Engineer (NLP), AI Consultant, Applied AI Engineer, and Startup MVP Developer. Typical job responsibilities include designing RAG architectures, integrating large document repositories with LLMs, improving retrieval relevance, reducing hallucinations, and delivering stable, production-ready knowledge assistants for internal or customer-facing use cases.
How It Works
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We get the call or WhatsApp or email message from you requesting for Job support
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We will conference you with our Job Support experts and schedule a demo within 24 hours
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First session will be a demo session where you can explain our consultant about your project and what kind of support is required.
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Payment should be done for the support period requested before second session
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We are working on behalf of you and the work will be kept confidential
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We would also need your help in understanding your project so that we can assist you better
Terms & Conditions
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As soon as we receive job support request, our team member will go through your requirement and we will arrange a conference call with our experts/developers and she/he will go through your task requirement, Tools and Technologies if she/he is 100% confident with the job, then only we will agree to provide Job Support.
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If our experts is 100% confident and comfortable with your requirements, then only we will agree to provide service.
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Our experts/developers are available from Monday to Friday in the morning or evening. You have the possibility to choose the time slot that best suits your needs.
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Usually, we're not working on weekends. But if you have a deadline and project job to be completed? Don't worry. We're making exceptions and helping you on weekends, too.
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In the case of expert/developer absence, we can provide backup expert within 12 hours.
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Any Meeting, call, work update, and discussion related to work will be considered as working hours.
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Developer can do work which is supported by technology lets say if BLE is only used to send small data(few bytes)
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Developer will not be available in holiday period or any planned leave which will be shared earlier
TYPE OF JOB SUPPORT SERVICES WE PROVIDE
WHAT WE OFFER FOR YOU TO BOOST YOUR CAREER
MONTHLY PLAN
Support for 5 days a week (Monday to Friday) daily 1 hour to 4 hours of support would be provided based on requirement. You can connect using TeamViewer, Skype, go-to meeting etc. Payment would be on monthly basis.
TASK BASED
Support for your specific task (one or two days assignment). You can connect using TeamViewer, Skype, go-to meeting etc. Charges will be based on complexity of work and number of hours.
How our charges and billing works?
Weekly
$ 25 / ₹ 2000
per hour
If you are using 1 - 2 hour per day and total less than 15 hours in a week
Monthly
$ 20 / ₹ 1500
per hour
2 - 4 hours per day every months. so total 60 - 120 hours in a month
Enterprise
$ 40 / ₹ 3000
per hour
Full time employee for contract basis project. For more please discuss with us.
Our charges starts from $15+ per hour as opted plan which includes code walkthrough, developer working hours. You can pay daily, weekly or monthly whatever is the best work for you but we take 50% upfront payment for one time project. But hourly payment we can discuss accordingly may be like 1 week advance payment or 15 days advance.
Payment Methods:
You can pay directly to the company account if payment is received from International Currency to INR. If you are willing to pay INR to INR account then you can pay the company account managed by Indian banking.
Payment Service provider:
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International payments (Stripe, wise.com, Westen union, Remitly, MoneyGram, Bank to Bank transfers )
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Payment from India (Bank to bank transfers), any Indian UPI like GooglePay/PhonePe etc.
RAG Pipeline Job Support
Get practical support for RAG, retrieval, semantic search, vector search, hybrid search, web search, enterprise search, agentic retrieval, GraphRAG, and LLM applications.
Work through real development tasks, debugging issues, architecture problems, integrations, optimization, deployment, and production support.
RAG & Retrieval Support
RAG Development | Search Engineering | Advanced Retrieval | Production |
RAG pipelines | Semantic search | Agentic RAG | API deployment |
Document RAG | Vector search | GraphRAG | Docker |
LLM integration | Hybrid search | Multi-step retrieval | Kubernetes |
Prompt/context design | Keyword search | Multimodal RAG | Cloud deployment |
RAG applications | Reranking | Knowledge graphs | Monitoring |
What We Can Help With
Development | Debugging | Optimization | Integration |
Build RAG pipelines | Retrieval failures | Search relevance | LLM APIs |
Document ingestion | Wrong results | Chunking | Vector databases |
Embeddings | Missing documents | Embeddings | REST APIs |
Retrieval | Hallucinations | Reranking | Enterprise systems |
RAG applications | Pipeline errors | Latency & cost | Web search |
RAG Pipeline
Use a compact visual/process section:
Documents → Ingestion → Chunking → Embeddings → Vector Store → Retrieval → Reranking → Context → LLM → Response
Support at Every Stage
Data Layer | Retrieval Layer | Generation Layer | Evaluation |
PDF / DOCX | Semantic search | Prompt construction | Relevance |
HTML / Web | Keyword search | Context management | Faithfulness |
Parsing | Hybrid search | LLM integration | Groundedness |
Chunking | Reranking | Citations | Answer quality |
Metadata | Filtering | Structured output | Retrieval recall |
Search & Retrieval Systems
This section should deliberately expand beyond basic RAG.
Search Type | Typical Use |
Semantic Search | Meaning-based content discovery |
Vector Search | Embedding-based retrieval |
Hybrid Search | Semantic + keyword retrieval |
Enterprise Search | Internal company knowledge |
Web Search | Internet and website information |
Agentic Search | Multi-step intelligent retrieval |
GraphRAG | Relationship-based knowledge retrieval |
Multimodal Search | Text, image, table, PDF retrieval |
Technology Support
Frameworks | Models | Databases | Infrastructure |
LangChain | OpenAI | Pinecone | Docker |
LangGraph | Anthropic | Qdrant | Kubernetes |
LlamaIndex | Gemini | Weaviate | AWS |
Haystack | Hugging Face | Chroma | Google Cloud |
Python | Open-source LLMs | pgvector | Azure |
RAG Use Cases
Enterprise | Research | Customer-Facing | Product |
Knowledge search | Research assistant | AI support | AI SaaS |
Internal assistant | Paper search | FAQ systems | Document chat |
Technical search | Web research | Customer service | AI copilots |
Policy search | Market research | Support automation | AI assistants |
Enterprise search | Competitive research | Knowledge assistant | Search products |
Common RAG Problems
Retrieval Problems | Data Problems | LLM Problems | Production Problems |
Wrong documents | Poor parsing | Hallucinations | High latency |
Low relevance | Bad chunking | Poor context use | High cost |
Missing results | Bad metadata | Weak prompts | API failures |
Poor ranking | Duplicate data | Incorrect answers | Scaling issues |
Search failures | Incomplete indexing | Bad citations | Deployment errors |
RAG Evaluation & Optimization
Accuracy | Retrieval | Performance | Cost |
Answer quality | Recall | Latency | Token usage |
Faithfulness | Precision | Throughput | Model selection |
Groundedness | Top-k | Database speed | Caching |
Citations | Ranking | API performance | Retrieval volume |
Existing Project Support
Existing RAG | Search Project | AI Product | Production System |
Debug existing pipeline | Improve search | Add RAG capability | Fix production issues |
Modify retrieval | Add hybrid search | Add AI assistant | Optimize performance |
Change embeddings | Add web search | Integrate LLM | Monitor pipeline |
Improve accuracy | Add reranking | Build knowledge base | Scale infrastructure |
Who Needs RAG & Retrieval Job Support?
AI & ML Professionals | Software & Engineering | Research & Technical | Business & Delivery |
AI Engineers | Software Engineers | AI Researchers | AI Consultants |
ML Engineers | Backend Developers | Research Engineers | Technical Consultants |
GenAI Engineers | Python Developers | PhD Researchers | Freelancers |
Data Scientists | Full-Stack Developers | Technical Researchers | Startups |
MLOps Engineers | DevOps Engineers | Students | Engineering Teams |
AI Architects | Solution Architects | Developers learning RAG | Product Teams |
What Are You Working On?
RAG & Knowledge | AI Search | Agentic Systems | Enterprise AI |
RAG Pipeline | Semantic Search | Agentic RAG | Enterprise Search |
Document RAG | Vector Search | AI Agents | Internal Knowledge Search |
PDF Chatbot | Hybrid Search | Multi-Agent Retrieval | Enterprise RAG |
Knowledge Base | Web Search | Tool-based Retrieval | AI Assistant |
Document Search | Internet Search | GraphRAG | Customer Support AI |
Research Assistant | AI Search | Multimodal Retrieval | Business Intelligence |
How It Works
01 — Share the Task
Tell us what you're building or where you're blocked.
02 — Share Context
Provide code, errors, architecture, documents, or logs as required.
03 — Solve the Problem
Work through development, debugging, architecture, integration, or optimization.
04 — Validate
Test the solution against your original requirement.
Support Models
One-Time | Hourly | Daily | Ongoing |
Bug fix | Development | Larger tasks | Monthly support |
Code review | Debugging | Implementation | Dedicated developer |
Architecture review | Pair programming | Migration | Production support |
Small feature | Optimization | Deployment | Retainer |
FAQs
Can you help with an existing RAG project?
Yes. Support can work with existing RAG applications, codebases, prototypes, research projects, and production systems.
Can you improve RAG accuracy?
Yes. Retrieval, chunking, embeddings, reranking, context construction, prompting, and evaluation can all be investigated.
Can you help with vector databases?
Yes. Support can cover Pinecone, Qdrant, Weaviate, Chroma, FAISS, pgvector, Elasticsearch, and similar technologies.
Can you help with web search and Internet retrieval?
Yes. Support can cover web-search APIs, website retrieval, crawling, indexing, source extraction, reranking, and Search + RAG architectures.
Can you build hybrid search?
Yes. Semantic, keyword, metadata, and reranking approaches can be combined into a hybrid retrieval system.
Can you help with GraphRAG?
Yes. Support can cover knowledge graphs, entity relationships, graph retrieval, and graph + vector architectures.
Can you deploy a RAG system?
Yes. Support can include APIs, Docker, Kubernetes, cloud infrastructure, vector databases, monitoring, and production troubleshooting.
Can you provide ongoing support?
Yes. One-time, hourly, daily, weekly, monthly, dedicated, and ongoing technical support are possible.
Need Help With a RAG or Search Pipeline?
Get technical support for RAG, vector search, enterprise search, web search, hybrid retrieval, agentic retrieval, GraphRAG, and AI-powered search systems.
React out to us
Requests are answered in the order they are received instantly.
Address:
G-69, Sector 63 Noida Pincode. 201301 (INDIA)
