Autonomous Agentic AI & Custom LLM Architectures
We engineer production-grade AI systems that integrate directly into your operational software, databases, and APIs — delivering verifiable business automation with zero hallucination risk.
35-50%
Token Cost Reduction
98.4%
Retrieval Accuracy
Built-In
Human Verification
2-4 Wks
Deployment Speed
From Experimental Prompts to Enterprise Production
Most AI initiatives fail in production due to lack of guardrails, poor retrieval, or unmanageable API costs. We solve these engineering challenges at the architectural level.
Autonomous Agentic Workflows
Move beyond simple Q&A chatbots. We build autonomous agent teams that execute multi-step business processes: data querying, document generation, ERP updates, and automated reconciliations.
- Multi-agent LangGraph & CrewAI orchestration
- Dynamic tool-calling & external API integration
- Human-in-the-loop review checkpoints
- Self-healing & fallback execution logic
Domain LLM Fine-Tuning
Train open-weights and proprietary models on your organisation's private vocabulary, compliance standards, and historical operational data with strict data boundary protections.
- LoRA / QLoRA parameter-efficient fine-tuning
- Domain-specific evaluation harnesses & benchmarks
- Data sanitation and PII removal pipelines
- Private self-hosted deployment (vLLM / Ollama)
Production RAG & Vector Knowledge
Connect your enterprise data (PDFs, Confluence, databases, Notion) to LLMs with sub-second hybrid retrieval, dynamic reranking, and citation-backed hallucinations prevention.
- Hybrid search (BM25 lexical + semantic dense vectors)
- Cohere & BGE cross-encoder rerankers
- PostgreSQL pgvector, Pinecone, and Qdrant integration
- Continuous index refresh & chunking optimization
AI Safety, Governance & Cost Controls
Production-grade guardrails, prompt injection defenses, token budget caps, and real-time observability to ensure reliability and budget predictability.
- Llama-Guard & custom prompt injection shields
- Semantic caching (Redis / GPTCache) for 40%+ token savings
- Latency & cost telemetry (Langfuse / Helicone)
- Strict data privacy & zero-retention API guarantees
How We Implement Enterprise AI
Every solution is engineered with strict isolation between your proprietary business data and public LLM APIs. We support AWS Bedrock private VPC endpoints, self-hosted open-weights models, and on-premises inference configurations.
Proof of Viability Framework
Not sure if AI is viable for your specific workflow? Our Rapid Proof of Concept (POC) delivers a working demo in 2 to 4 weeks for a fixed ₹50,000 investment.