Enterprise AI & Autonomous Systems

AI Services & Engineering

Accelerate enterprise innovation with custom Generative AI architectures, autonomous multi-agent systems, secure RAG pipelines, and private LLM fine-tuning built for scale.

Rapid AI PoC speed
< 14 DaysRapid AI PoC speedFrom kickoff to working functional prototype
RAG context accuracy
99%+RAG context accuracyGrounding on enterprise proprietary data
Data privacy & IP safety
100%Data privacy & IP safetyDedicated VPCs with zero model training retention
Cost efficiency
65-75%Cost efficiencyvs domestic US in-house AI development labs

Enterprise AI solutions built for measurable ROI

End-to-end AI engineering from proof-of-concept to production workloads operating with enterprise resilience and security.

  • Custom Foundation & LoRA

    Generative AI & LLM Fine-Tuning

    Architect, adapt, and fine-tune open and proprietary foundation models on your domain data. From LoRA/PEFT parameter-efficient fine-tuning to customized prompt engineering.

    Key deliverables

    • Domain-specific model fine-tuning (LoRA / QLoRA)
    • Prompt engineering & evaluation benchmarks
    • Synthetic data generation & data curation
    • Multi-modal vision & text architectures
  • Multi-Agent Systems

    Autonomous AI Agents & Workflows

    Deploy self-orchestrating, goal-driven AI agents capable of planning, executing complex multi-step workflows, calling APIs, and collaborating with human supervisors.

    Key deliverables

    • Autonomous task planning & tool-calling agents
    • Human-in-the-loop validation checkpoints
    • Multi-agent orchestration (LangGraph, CrewAI)
    • ERP, CRM & internal API workflow automation
  • Proprietary Data Grounding

    Enterprise RAG & Vector Intelligence

    Eliminate hallucinations by grounding LLMs in your enterprise documentation, codebases, and transactional records with high-accuracy hybrid retrieval pipelines.

    Key deliverables

    • Hybrid dense + sparse vector embeddings
    • Semantic chunking & contextual re-ranking
    • Real-time connector sync (SQL, Notion, Drive, S3)
    • Role-based document access control (RBAC)
  • Enterprise Copilots

    Conversational AI & Digital Assistants

    Deliver context-aware customer support agents and internal knowledge copilots that understand technical nuance, company policies, and multi-turn user intent.

    Key deliverables

    • Customer support copilots with ticket escalation
    • Internal HR, engineering & operations assistants
    • Omnichannel integration (Web, Slack, Teams, Mobile)
    • Real-time sentiment & guardrail monitoring
  • Zero Data Retention

    Private VPC Deployment & MLOps

    Deploy models securely within your isolated AWS, Azure, or GCP virtual private cloud. Full sovereignty over weights, embeddings, inference logs, and sensitive IP.

    Key deliverables

    • Private VPC deployment with zero third-party training
    • Semantic caching & token cost reduction
    • High-throughput, low-latency GPU inference serving
    • Continuous drift detection & automated redeployment
  • Responsible AI

    AI Governance, Security & Guardrails

    Protect brand integrity and regulatory compliance with real-time prompt injection defense, PII masking, safety guardrails, and compliance audits.

    Key deliverables

    • NeMo / Llama Guard safety guardrail layers
    • Automated PII scrubbing & prompt injection defense
    • Explainability audits & hallucination benchmarking
    • EU AI Act & enterprise compliance alignment

Our 4-phase AI engineering lifecycle

A disciplined, risk-managed approach that moves enterprise use cases from initial scoping to secure production deployment.

  1. Discovery

    Feasibility & Data Readiness

    Identify high-ROI enterprise use-cases, benchmark existing data hygiene, assess token economics, and define quantifiable success metrics.

  2. Prototyping

    Rapid PoC (< 14 Days)

    Engineer a functional, production-aligned proof of concept using state-of-the-art models and custom RAG retrieval on sample datasets.

  3. Hardening

    Guardrails & Integration

    Implement enterprise security guardrails, connect live databases/APIs, optimize latency, and conduct rigorous red-teaming.

  4. Scale

    Production MLOps & Monitoring

    Deploy on private cloud infrastructure with automated monitoring, cache tuning, feedback loops, and continuous model optimization.

Architected for enterprise security & sovereignty

Rigorous governance and private infrastructure controls designed to satisfy enterprise CISO and legal standards.

  • Model Agnostic Architecture

    We engineer around your specific needs across OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini, Llama 3, Mistral, and DeepSeek without vendor lock-in.

  • Absolute Data Sovereignty

    Your data never leaves your perimeter. Enterprise deployments operate in client-controlled VPCs with strict zero-data-retention agreements.

  • Low-Latency & Token Economics

    Sophisticated semantic caching, prompt compression, and intelligent model routing cut inference costs by up to 60% while maintaining sub-second responses.

  • Dual-Shore US & India Pods

    Product strategy and architecture alignment in US timezones, backed by high-velocity engineering pods at our Chennai innovation center.

Ready to architect your enterprise AI strategy?

Discuss your AI use cases, data architecture, and custom agent requirements with our senior AI engineering architects.