End-to-End AI Services
for Enterprises

Machine learning built into products, from first model to serving at scale.

• Predictive Analytics: forecasting, anomaly and failure detection, demand, churn and price models, and root-cause analysis on logs and telemetry.
• Recommender Systems: collaborative and feature-based filtering, search ranking, and cold-start matching.
• Real-Time Decisioning: in-request-path scoring, reinforcement learning, bandits, and dynamic pricing.
• Computer Vision: detection and classification in images and video.
• NLP & Text Mining: intent and entity extraction, sentiment, and log and document classification.

LLM and diffusion-based capabilities that find, create, and understand content.

• RAG & Knowledge Assistants: hybrid search, grounded answers with citations to documents and pages.
• Conversational Data Access (NL2SQL): plain-language querying over databases with access guardrails.
• Document Intelligence: OCR, layout-aware parsing, and vision-language model (VLM) extraction across 100+ languages.
• Copilots & Content Generation: in-product assistants and template-grounded drafting with Word/PDF export.
• Generative Media: diffusion-based video and image generation and pose and expression transfer.
• Fine-Tuning & Model Customization: PEFT/LoRA fine-tuning and small language model (SLM) tuning for domain tasks.

Multi-agent systems running reliably in production.

• Agentic Discovery & Design: use-case discovery, AI data readiness, multi-agent system and goal-oriented architecture.
• Multi-Agent Development: orchestration, dynamic task routing, context-aware delegation and handoffs.
• Agent Harness & Control Planes: custom agent frameworks, agent registries, invocation gateways, and versioned deploys with rollback.
• Agent Reasoning & Tooling: tool-using agents, planning layers, and MCP and A2A connectors.
• SaaS to Agentic: modernizing existing products from no AI to agentic, with copilots and autonomous workflows.
• Enterprise Workflow Agents: customer service, sales, marketing, compliance and back-office agents, plus RPA-and-agent fusion.

Getting models and agents into production and keeping them healthy.

• Feature & Data Management: feature stores, online/offline consistency, dataset versioning and quality checks.
• Experimentation & Model Development: experiment tracking, and prompt and architecture sweeps.
• Deployment & Orchestration: containerized serving (KServe, Seldon, BentoML), and canary, shadow and blue-green rollouts.
• Model Scaling & Inference Optimization: distributed training (Ray, DeepSpeed), multi-GPU orchestration, quantization.
• Evaluation Harness: automated test suites, synthetic task generation, agent evaluators, and human feedback loops.
• Observability & AgentOps: drift, latency and cost alerts, tracing (OpenTelemetry, LangSmith, Langfuse), and hallucination tracking.
• LLM Cost Optimization: token budgeting, model routing, async queueing and rate-limit protection.

Controls built into the architecture, not bolted on later.

• Agent Guardrails & Policy Layers: scope enforcement, output validation, and out-of-scope refusal.
• Prompt-Injection & PII Defence: input screening, PII detection and masking.
• Access Control for AI: RBAC on agents, models and data (role-based access control), and per-tenant data scoping.
• Human-in-the-Loop Governance: approval gates, citation-first outputs and audit trails.
• Model Governance & Lineage: model cards, reproducible training runs, and audit logs.
• Privacy & Regulatory Compliance: policy-as-code for SOC 2, GDPR and DPDP.