Agentic
AI Development
Building context-aware assistants, self-directed agents, and custom OCR parsing pipelines.
How we apply agentic AI to businesses.
We focus on building functional AI modules that automate business workflows, eliminate manual human inputs, and save operational costs.
Customer Support Agentic Routing
Rather than basic keyword matching, our AI agent evaluates the semantic meaning of support requests. It queries internal document databases via RAG, automatically drafts responses, and escalates to human staff only for complex edge-cases.
Document Processing & Invoice OCR
Automate financial ledger sorting. We build OCR ingestion lines utilizing layout models to isolate tables from invoices, match them to transaction schemas, and upload structured data records directly to accounting portals automatically.
Lead Scoring & Personalization
Connect leads to the right agent automatically. Our pipelines read company profiles, categorize vertical segments using custom LLM taxonomy tables, score deal values, and generate personalized onboarding copy before the call starts.
AI Stack Frameworks
Bespoke AI technology stack.
We leverage leading foundation models alongside robust orchestration tools to ensure determinism and cost efficiency.
Foundation Models
Context-Aware InferenceWe integrate commercial API structures (OpenAI GPT-4o, Anthropic Claude 3.5, and Google Gemini Pro) using strict schema parsing (JSON Mode or Zod validation schemas) to ensure consistent payloads.
Agent Orchestration
Multi-Agent Workflow LoopsWe construct self-directed agent architectures using Python, FastAPI, LangChain, and LangGraph. This supports multi-agent validation loops, external API calls, and human-in-the-loop triggers.
Semantic Memory (RAG)
Context Indexing & EmbeddingWe store application memory buffers and context embeddings in low-latency vector databases (Pinecone or pgvector). We optimize custom chunking models to prevent information decay during RAG steps.
Telemetry & Infrastructure
Model Logs & Cost OptimizationWe log agent paths, latency, and tokens using custom tracing platforms (LangSmith, Langfuse). We set up strict rate limiters, token caching layers, and fallback triggers to keep costs optimized.
Deployment Milestones
Our 6-step AI agent scoping roadmap.
1. Feasibility Study & Data Audit
Week 1Identify core business tasks to automate, review baseline documentation structures, check token constraints, and confirm feasibility.
2. Baseline Prompt Engineering
Week 2Draft interactive system prompt sheets, establish JSON structures, evaluate baseline hallucinations, and configure model settings.
3. Context Sync & RAG Build
Week 3-4Initialize pgvector/Pinecone memory databases, configure document sync workers, set up semantic indexes, and write background pipelines.
4. Agent Logic & Validation Sprints
Week 5-6Code decision-making nodes using LangGraph, implement retry limits, link APIs, and set up human-in-the-loop dashboards.
5. Latency & Cost Optimization
Week 7Implement query caches to optimize costs, test asynchronous model calls, profiles latency rates, and check token logs.
6. Live Launch & Tracing Setup
Week 8Deploy pipelines to active server networks, set up tracing analytics (LangSmith) to monitor operations, and launch to public.
Case Studies
Production-grade AI systems we shipped.
A HIPAA-compliant tracking platform for chronic health conditions and patient-doctor collaboration.
40% Reduction in patient re-admissions
4.8 Star rating on Apple App Store
100k+ Active monthly patients
Rudra IT Solutions delivered our MVP within 8 weeks. Their understanding of HIPAA requirements and mobile engineering was impressive. We successfully raised our Seed round shortly after.
— Dr. Sarah Jenkins, Chief Medical Officer (PulseHealth Inc.)
An intelligent document processing platform utilizing OCR and LLMs to automate invoice categorization and analysis.
92% Reduction in processing time
99.4% Extraction accuracy rate
500k+ Documents processed monthly
Their expertise in AI pipelines and OCR is world-class. We went from a complex manual prototype to a highly scalable AI automation engine in just 6 weeks.
— Elena Rostova, VP of Product (ScribeAI Ltd.)
FAQ Accordion
Common questions answered.
How do you prevent AI model hallucinations?
How do you prevent AI model hallucinations?
Will my company data be used to train public models?
Will my company data be used to train public models?
What vector databases do you recommend?
What vector databases do you recommend?
Ready to automate your operations with AI?
Get an engineering-led scoping call where we audit your background data feeds, establish baseline model dependencies, and estimate token rates beforehand.