Autonomous AI Agents & Enterprise RAG Systems Engineered for Deterministic Scale.
We architect and deploy production-grade multi-modal agents, proprietary document RAG pipelines, low-latency streaming voice assistants, and air-gapped on-premise LLMs with mathematical auditability.
Interactive AI Agent Execution Sandbox
Real-time multi-agent graph flows, live token burn telemetry, streaming execution activity, knowledge namespaces, and tool invocation tracking.
Agent Pipeline
Visualise how tasks flow across your multi-agent graph in real time.
Token Monitor
Track LLM token usage and cost-per-run across every model call.
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Activity Feed
Real-time logs of agent actions, tool calls, and memory retrievals.
Decomposed task into 4 sub-goals
Knowledge Base
Semantic search across documents, codebases, and conversations.
Namespaces
Retrieval Log
vector embeddings auth module
OpenAI function calling schema
Q3 roadmap — agent features
deployment discussion #eng
Tool Inspector
Monitor tool usage, latency, and success rates across all agents.
Production Deployments & Proof-of-Work
Real enterprise case studies engineered by Vyomara Technologies, driving measurable operational efficiency and sub-second intelligence.
FinQuery AI — Autonomous Financial Document Intelligence & SEC Analysis
Enterprise Asset Management & FinTech Firm (Mumbai)
The client was manually processing 10,000+ quarterly 10-K/10-Q reports, balance sheets, and earnings call transcripts monthly. Traditional OCR tools failed on nested financial tables and created hallucinated calculations.
We engineered a multi-agent financial extraction system utilizing LlamaParse, hybrid dense-sparse vector indexing on pgvector, and a deterministic Python code-execution sandbox to calculate ratios without model hallucination.
- Multi-Stage Layout-Aware PDF Parser (LlamaParse)
- Hybrid Retrieval (Dense BGE-large + Sparse BM25)
- Deterministic Python Code Interpreter Tool for Formulas
- NeMo Guardrails with PII Masking & Audit Traceability
- 85% reduction in manual analyst auditing time per filing
- Sub-400ms end-to-end question answering latency
- 100% mathematical auditability with step-by-step code proofs
MedRAG Copilot — Multi-Modal Clinical Knowledge Assistant
Multi-Specialty Healthcare & Diagnostics Network (Delhi NCR)
Doctors and radiologists needed instant cross-referencing of 500,000+ medical journals, drug-drug contraindication protocols, and ICD-10 diagnostic codes without sending patient data to third-party public clouds.
Deployed an entirely on-premise air-gapped LLM inference cluster using vLLM and Cohere Rerank, integrated with hospital EMR database via secure mTLS authentication and strict source citation verification.
- Air-Gapped vLLM Local Inference Cluster on NVIDIA H100s
- Cohere Rerank v3 for Top-k Context Precision
- Automated PubMed & Internal Protocol Ingestion Pipeline
- Strict Zero-Hallucination Threshold with Exact Page Citations
- Zero external cloud transmission — 100% on-premise HIPAA alignment
- Verified citations on every single clinical recommendation
- Adopted by 320+ resident physicians across 14 hospital branches
ApexVoice — Low-Latency Autonomous Voice Support Swarm
High-Volume D2C & Logistics Brand (Pan-India)
During peak festive sales, inbound customer support was flooded with 45,000+ calls daily regarding order tracking, address changes, and refund statuses, leading to 25-minute wait times and surging call center costs.
We built a bi-directional streaming Voice AI agent powered by Deepgram Nova-2 speech recognition, low-latency LLM streaming, and Cartesia neural voice synthesis supporting Hindi, English, and regional accents.
- Full-Duplex WebRTC Audio Streaming with Interruption Detection
- Deepgram Nova-2 Real-Time Speech-to-Text (<150ms)
- FastAPI Tool Execution Layer Connecting Live Logistics Database
- Automated Human Escalation Protocol for Sensitive Inquiries
- 68% first-call automated resolution with zero human intervention
- Average customer wait time reduced from 25 minutes to 3 seconds
- ₹18.5 Lakhs monthly call center operational expenditure saved
CodeWeaver — Automated Legacy Code Refactoring & Migration Swarm
Enterprise Supply Chain & Logistics SaaS Provider
A 12-year-old monolithic Java backend with 250,000 lines of code was bottlenecking release velocity. Manual migration to Go microservices was projected to take 14 months and cost hundreds of thousands of dollars.
Deployed an autonomous Multi-Agent Graph (Planner Agent -> AST Parser -> Go Code Generator -> Static Security Verifier -> Unit Test Synthesizer) with integrated SonarQube validation pipelines.
- AST Semantic Code Parsing & Dependency Graph Mapping
- Planner-Coder-Critic Multi-Agent Iterative Refinement Loop
- Automated JUnit to Go Test Suite Translation & Execution Sandbox
- CI/CD Pull Request Integration with Line-by-Line Diffs
- Completed microservices migration in 3.5 months (saving 10.5 months)
- Generated 92% unit and integration test coverage across all services
- Zero regression bugs reported in post-deployment production testing
What We Engineer Across the AI Lifecycle
Autonomous Agent Swarms
State machines with LangGraph and CrewAI executing multi-step tool calls, live database updates, external webhook pings, and human-in-the-loop approvals.
Hybrid Dense/Sparse RAG
Combining dense vector embeddings with BM25 keyword matching and cross-encoder reranking (Cohere/BGE) to guarantee zero context miss on complex enterprise datasets.
Air-Gapped On-Premise LLMs
Deploying quantised open-weight models (Llama 3.3, DeepSeek, Mistral) on private VPCs or bare-metal GPUs via vLLM with zero outbound data egress.
How We Ship Production AI Systems
A disciplined, milestone-driven process ensuring zero hallucination before production release.
Data Audit & Feasibility POC
Ingest sample documents and structure validation metrics to benchmark retrieval accuracy in 5 business days.
RAG Architecture & Tool Graph
Build LangGraph tool nodes, vector indexes, database connectors, and deterministic execution sandboxes.
Guardrail & Security Hardening
Integrate NeMo Guardrails, PII redaction, prompt injection defense, and automated regression test suites.
Production Rollout & Telemetry
Deploy containerized microservices to AWS/GCP or on-premise Kubernetes with live Prometheus latency monitoring.
Ready to build custom autonomous agents for your enterprise?
Schedule a 30-minute technical architecture review with our senior AI engineers. We will analyze your workflows, recommend the ideal model stack, and provide a fixed-price delivery roadmap.